U.S. patent application number 17/538037 was filed with the patent office on 2022-03-17 for apparatuses and methods for network resource dimensioning in accordance with differentiated quality of service.
This patent application is currently assigned to AT&T Intellectual Property I, L.P.. The applicant listed for this patent is AT&T Intellectual Property I, L.P.. Invention is credited to Gopalakrishnan Meempat, Ravi Raina, Huahui Wang.
Application Number | 20220086664 17/538037 |
Document ID | / |
Family ID | 1000005999937 |
Filed Date | 2022-03-17 |
United States Patent
Application |
20220086664 |
Kind Code |
A1 |
Meempat; Gopalakrishnan ; et
al. |
March 17, 2022 |
APPARATUSES AND METHODS FOR NETWORK RESOURCE DIMENSIONING IN
ACCORDANCE WITH DIFFERENTIATED QUALITY OF SERVICE
Abstract
Aspects include determining whether a utilization of wireless
spectrum associated with a guaranteed class of traffic in a network
is greater than a first threshold, responsive to the determining
indicating that the utilization of the wireless spectrum associated
with the guaranteed class of traffic is greater than the first
threshold, causing an upgrade of a capacity in the network, and
responsive to the determining indicating that the utilization of
the wireless spectrum associated with the guaranteed class of
traffic is not greater than the first threshold: determining a
throughput for a non-guaranteed class of traffic for each cell of a
plurality of cells of the network, and responsive to determining
that the throughput for the non-guaranteed class of traffic for at
least one cell of the plurality of cells is less than a second
threshold, causing the upgrade of the capacity in the network.
Other embodiments are disclosed.
Inventors: |
Meempat; Gopalakrishnan;
(East Brunswick, NJ) ; Wang; Huahui; (Bridgewater,
NJ) ; Raina; Ravi; (Skillman, NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
AT&T Intellectual Property I, L.P. |
Atlanta |
GA |
US |
|
|
Assignee: |
AT&T Intellectual Property I,
L.P.
Atlanta
GA
|
Family ID: |
1000005999937 |
Appl. No.: |
17/538037 |
Filed: |
November 30, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
16868718 |
May 7, 2020 |
11218888 |
|
|
17538037 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 43/16 20130101;
H04L 43/0876 20130101; H04W 24/02 20130101; H04W 72/0453 20130101;
H04W 16/22 20130101 |
International
Class: |
H04W 24/02 20060101
H04W024/02; H04W 72/04 20060101 H04W072/04; H04W 16/22 20060101
H04W016/22 |
Claims
1. A device, comprising: a processing system including a processor;
and a memory that stores executable instructions that, when
executed by the processing system, facilitate performance of
operations, the operations comprising: computing a capacity for
each cell of a plurality of cells associated with a network;
computing a capacity for a plurality of classes of traffic for each
cell of the plurality of cells in accordance with the capacity for
each cell; determining a throughput for each of the plurality of
classes of traffic for each cell of the plurality of cells in
accordance with the computing of the capacity for the plurality of
classes of traffic; and responsive to determining that the
throughput for at least one of the plurality of classes of traffic
for at least one cell of the plurality of cells is less than a
first threshold, upgrading a capacity in the network, wherein the
upgrading of the capacity in the network comprises at least one of:
deploying a new cell at a predetermined level of wireless spectrum,
wherein the new cell is not included in the plurality of cells; or
increasing a wireless spectrum allocation of a first cell of the
plurality of cells.
2. The device of claim 1, wherein the plurality of classes of
traffic includes buffered video traffic, email traffic, text
traffic, file transfer traffic, peer-to-peer file sharing traffic,
progressive video traffic, interactive gaming traffic, or any
combination thereof.
3. The device of claim 1, wherein the computing of the capacity for
the plurality of classes of traffic for each cell of the plurality
of cells is further in accordance with a plurality of projections
associated with a demand for a second plurality of classes of
traffic and a plurality of bandwidths occupied by each session of
the second plurality of classes of traffic.
4. The device of claim 1, wherein the upgrading of the capacity in
the network comprises the increasing of the wireless spectrum
allocation of the first cell.
5. The device of claim 1, wherein the upgrading of the capacity in
the network further comprises: determining that the wireless
spectrum allocation of the first cell is less than a second
threshold.
6. The device of claim 5, wherein the upgrading of the capacity in
the network further comprises: responsive to the determining that
the wireless spectrum allocation of the first cell is less than the
second threshold, performing the increasing of the wireless
spectrum allocation of the first cell.
7. The device of claim 1, wherein the upgrading of the capacity in
the network further comprises: determining that the wireless
spectrum allocation of the first cell is greater than a second
threshold.
8. The device of claim 7, wherein the upgrading of the capacity in
the network further comprises: responsive to the determining that
the wireless spectrum allocation of the first cell is greater than
the second threshold, performing the deploying of the new cell at
the predetermined level of wireless spectrum.
9. The device of claim 7, wherein the predetermined level of
wireless spectrum is less than the second threshold.
10. The device of claim 1, wherein the determining of the
throughput for each of the plurality of classes of traffic for each
cell of the plurality of cells is based on modeling.
11. The device of claim 10, wherein the modeling is based on an
apportionment of a volume of the plurality of classes of traffic in
proportion to a ratio of the capacity for each cell relative to a
total capacity for all of the plurality of cells.
12. The device of claim 1, wherein the determining of the
throughput for each of the plurality of classes of traffic for each
cell of the plurality of cells is based on an engaging of a
simulation.
13. The device of claim 12, wherein the engaging of the simulation
comprises: detecting an event corresponding to an arrival of first
traffic associated with a second plurality of classes of traffic,
an arrival of second traffic associated with the plurality of
classes of traffic, a departure of third traffic associated with
the second plurality of classes of traffic, a departure of fourth
traffic associated with the plurality of classes of traffic, or any
combination thereof; and responsive to the detecting of the event,
recording samples of throughput.
14. The device of claim 13, wherein the engaging of the simulation
further comprises: processing the samples by applying weights to
the samples to generate the throughput for each of the plurality of
classes of traffic for each cell of the plurality of cells.
15. The device of claim 13, wherein the event includes the arrival
of the first traffic, and wherein the engaging of the simulation
further comprises: determining that blocking is not implemented
with respect to the first traffic, wherein the recording of the
samples of throughput is further responsive to the determining that
blocking is not implemented with respect to the first traffic.
16. The device of claim 1, wherein the upgrading of the capacity in
the network comprises the deploying of the new cell at the
predetermined level of wireless spectrum.
17. A non-transitory machine-readable medium, comprising executable
instructions that, when executed by a processing system including a
processor, facilitate performance of operations, the operations
comprising: computing a capacity for a class of traffic for each
cell of a plurality of cells of a network; determining a throughput
for the class of traffic for each cell of the plurality of cells in
accordance with the computing of the capacity for the class of
traffic; and responsive to determining that the throughput for the
class of traffic for at least one cell of the plurality of cells is
less than a threshold, performing an upgrade of a capacity in the
network, wherein the performing of the upgrade of the capacity in
the network comprises at least one of: deploying a new cell at a
predetermined level of wireless spectrum, wherein the new cell is
not included in the plurality of cells; or increasing a wireless
spectrum allocation of a first cell of the plurality of cells.
18. A method, comprising: determining, by a processing system
including a processor, a throughput for a non-guaranteed class of
traffic for each cell of a plurality of cells of a network; and
responsive to determining, by the processing system, that the
throughput for the non-guaranteed class of traffic for at least one
cell of the plurality of cells is less than a threshold, causing an
upgrade of a capacity in the network, wherein the causing of the
upgrade of the capacity in the network comprises one or both of:
deploying a new cell at a predetermined level of wireless spectrum,
wherein the new cell is not included in the plurality of cells, and
increasing a wireless spectrum allocation of a first cell of the
plurality of cells.
19. The method of claim 18, wherein the causing of the upgrade of
the capacity in the network comprises the deploying of the new cell
at the predetermined level of wireless spectrum.
20. The method of claim 19, wherein the predetermined level
corresponds to a minimum discrete level within the new cell.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 16/868,718 filed May 7, 2020. All sections of
the aforementioned application are incorporated herein by reference
in their entirety.
FIELD OF THE DISCLOSURE
[0002] The subject disclosure relates to apparatuses and methods
for network resource dimensioning in accordance with differentiated
quality of service (QoS).
BACKGROUND
[0003] As the world becomes increasingly connected via vast
communication networks and communication devices, additional
challenges are created/generated from the perspective of
provisioning and managing network resources. For example, from a
perspective of a network operator, a policy that favors cost
reduction (e.g., cost minimization) while deemphasizing (e.g.,
disregarding/ignoring) quality of service (QoS) parameters runs a
risk of degradation in terms of a user's quality of experience
(QoE). The reduction in QoE may tend to alienate/annoy the user,
potentially to the point that the user may terminate service with
the network operator. On the other hand, a policy that
conservatively allocates resources (e.g., spectrum, bandwidth,
etc.) to ensure high levels of QoS or QoE, without taking into
account fine-grain QoS considerations, runs a risk of
wasteful/unnecessary surplus investment.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] Reference will now be made to the accompanying drawings,
which are not necessarily drawn to scale, and wherein:
[0005] FIG. 1 is a block diagram illustrating an exemplary,
non-limiting embodiment of a communications network in accordance
with various aspects described herein.
[0006] FIG. 2A is a block diagram illustrating an example,
non-limiting embodiment of a system functioning within the
communication network of FIG. 1 in accordance with various aspects
described herein.
[0007] FIG. 2B is a block diagram illustrating an example,
non-limiting embodiment of a system functioning within the system
of FIG. 2A in accordance with various aspects described herein.
[0008] FIG. 2C depicts an illustrative embodiment of a method for
allocating cells and spectrum in accordance with various aspects
described herein.
[0009] FIG. 2D depicts an illustrative embodiment of a method for
performing/engaging a simulation in accordance with various aspects
described herein.
[0010] FIG. 3 is a block diagram illustrating an example,
non-limiting embodiment of a virtualized communication network in
accordance with various aspects described herein.
[0011] FIG. 4 is a block diagram of an example, non-limiting
embodiment of a computing environment in accordance with various
aspects described herein.
[0012] FIG. 5 is a block diagram of an example, non-limiting
embodiment of a mobile network platform in accordance with various
aspects described herein.
[0013] FIG. 6 is a block diagram of an example, non-limiting
embodiment of a communication device in accordance with various
aspects described herein.
DETAILED DESCRIPTION
[0014] The subject disclosure describes, among other things,
illustrative embodiments for allocating or dimensioning resources
associated with a communication network in accordance with priority
tiers/classes of service. Other embodiments are described in the
subject disclosure.
[0015] One or more aspects of the subject disclosure include
computing a capacity for each cell of a plurality of cells
associated with a network, responsive to determining that a
utilization of wireless spectrum associated with a first plurality
of classes of traffic in the network is greater than a first
threshold, upgrading a capacity in the network, and responsive to
determining that the utilization of the wireless spectrum
associated with the first plurality of classes of traffic in the
network is less than or equal to the first threshold: computing a
capacity for a second plurality of classes of traffic for each cell
of the plurality of cells in accordance with the capacity for each
cell, performing analytical modeling or engaging a simulation to
determine a throughput for each of the second plurality of classes
of traffic for each cell of the plurality of cells in accordance
with the computing of the capacity for the second plurality of
classes of traffic, and responsive to determining that the
throughput for at least one of the second plurality of classes of
traffic for at least one cell of the plurality of cells is less
than a second threshold, upgrading the capacity in the network,
wherein the upgrading of the capacity in the network comprises one
of: deploying a new cell at a predetermined level of wireless
spectrum, wherein the new cell is not included in the plurality of
cells, or increasing a wireless spectrum allocation of a first cell
of the plurality of cells.
[0016] One or more aspects of the subject disclosure include
responsive to determining that a utilization of wireless spectrum
associated with a first class of traffic in a network is greater
than a first threshold, performing an upgrade of a capacity in the
network, and responsive to determining that the utilization of the
wireless spectrum associated with the first class of traffic in the
network is less than or equal to the first threshold: computing a
capacity for a second class of traffic for each cell of a plurality
of cells of the network, performing analytical modeling or engaging
a simulation to determine a throughput for the second class of
traffic for each cell of the plurality of cells in accordance with
the computing of the capacity for the second class of traffic, and
responsive to determining that the throughput for the second class
of traffic for at least one cell of the plurality of cells is less
than a second threshold, performing the upgrade of the capacity in
the network, wherein the performing of the upgrade of the capacity
in the network comprises one of: deploying a new cell at a
predetermined level of wireless spectrum, wherein the new cell is
not included in the plurality of cells, or increasing a wireless
spectrum allocation of a first cell of the plurality of cells.
[0017] One or more aspects of the subject disclosure include
determining whether a utilization of wireless spectrum associated
with a guaranteed class of traffic in a network is greater than a
first threshold, responsive to the determining indicating that the
utilization of the wireless spectrum associated with the guaranteed
class of traffic is greater than the first threshold, causing an
upgrade of a capacity in the network, and responsive to the
determining indicating that the utilization of the wireless
spectrum associated with the guaranteed class of traffic is not
greater than the first threshold: determining a throughput for a
non-guaranteed class of traffic for each cell of a plurality of
cells of the network, and responsive to determining that the
throughput for the non-guaranteed class of traffic for at least one
cell of the plurality of cells is less than a second threshold,
causing the upgrade of the capacity in the network, wherein the
causing of the upgrade of the capacity in the network comprises:
deploying a new cell at a predetermined level of wireless spectrum,
wherein the new cell is not included in the plurality of cells,
increasing a wireless spectrum allocation of a first cell of the
plurality of cells, or a combination thereof.
[0018] Referring now to FIG. 1, a block diagram is shown
illustrating an example, non-limiting embodiment of a system 100 in
accordance with various aspects described herein. For example,
system 100 can facilitate in whole or in part computing a capacity
for each cell of a plurality of cells associated with a network,
responsive to determining that a utilization of wireless spectrum
associated with a first plurality of classes of traffic in the
network is greater than a first threshold, upgrading a capacity in
the network, and responsive to determining that the utilization of
the wireless spectrum associated with the first plurality of
classes of traffic in the network is less than or equal to the
first threshold: computing a capacity for a second plurality of
classes of traffic for each cell of the plurality of cells in
accordance with the capacity for each cell, performing analytical
modeling or engaging a simulation to determine a throughput for
each of the second plurality of classes of traffic for each cell of
the plurality of cells in accordance with the computing of the
capacity for the second plurality of classes of traffic, and
responsive to determining that the throughput for at least one of
the second plurality of classes of traffic for at least one cell of
the plurality of cells is less than a second threshold, upgrading
the capacity in the network, wherein the upgrading of the capacity
in the network comprises one of: deploying a new cell at a
predetermined level of wireless spectrum, wherein the new cell is
not included in the plurality of cells, or increasing a wireless
spectrum allocation of a first cell of the plurality of cells.
System 100 can facilitate in whole or in part responsive to
determining that a utilization of wireless spectrum associated with
a first class of traffic in a network is greater than a first
threshold, performing an upgrade of a capacity in the network, and
responsive to determining that the utilization of the wireless
spectrum associated with the first class of traffic in the network
is less than or equal to the first threshold: computing a capacity
for a second class of traffic for each cell of a plurality of cells
of the network, performing analytical modeling or engaging a
simulation to determine a throughput for the second class of
traffic for each cell of the plurality of cells in accordance with
the computing of the capacity for the second class of traffic, and
responsive to determining that the throughput for the second class
of traffic for at least one cell of the plurality of cells is less
than a second threshold, performing the upgrade of the capacity in
the network, wherein the performing of the upgrade of the capacity
in the network comprises one of: deploying a new cell at a
predetermined level of wireless spectrum, wherein the new cell is
not included in the plurality of cells, or increasing a wireless
spectrum allocation of a first cell of the plurality of cells.
System 100 can facilitate in whole or in part determining whether a
utilization of wireless spectrum associated with a guaranteed class
of traffic in a network is greater than a first threshold,
responsive to the determining indicating that the utilization of
the wireless spectrum associated with the guaranteed class of
traffic is greater than the first threshold, causing an upgrade of
a capacity in the network, and responsive to the determining
indicating that the utilization of the wireless spectrum associated
with the guaranteed class of traffic is not greater than the first
threshold: determining a throughput for a non-guaranteed class of
traffic for each cell of a plurality of cells of the network, and
responsive to determining that the throughput for the
non-guaranteed class of traffic for at least one cell of the
plurality of cells is less than a second threshold, causing the
upgrade of the capacity in the network, wherein the causing of the
upgrade of the capacity in the network comprises: deploying a new
cell at a predetermined level of wireless spectrum, wherein the new
cell is not included in the plurality of cells, increasing a
wireless spectrum allocation of a first cell of the plurality of
cells, or a combination thereof.
[0019] In particular, in FIG. 1 a communications network 125 is
presented for providing broadband access 110 to a plurality of data
terminals 114 via access terminal 112, wireless access 120 to a
plurality of mobile devices 124 and vehicle 126 via base station or
access point 122, voice access 130 to a plurality of telephony
devices 134, via switching device 132 and/or media access 140 to a
plurality of audio/video display devices 144 via media terminal
142. In addition, communication network 125 is coupled to one or
more content sources 175 of audio, video, graphics, text and/or
other media. While broadband access 110, wireless access 120, voice
access 130 and media access 140 are shown separately, one or more
of these forms of access can be combined to provide multiple access
services to a single client device (e.g., mobile devices 124 can
receive media content via media terminal 142, data terminal 114 can
be provided voice access via switching device 132, and so on).
[0020] The communications network 125 includes a plurality of
network elements (NE) 150, 152, 154, 156, etc. for facilitating the
broadband access 110, wireless access 120, voice access 130, media
access 140 and/or the distribution of content from content sources
175. The communications network 125 can include a circuit switched
or packet switched network, a voice over Internet protocol (VoIP)
network, Internet protocol (IP) network, a cable network, a passive
or active optical network, a 4G, 5G, or higher generation wireless
access network, WIMAX network, UltraWideband network, personal area
network or other wireless access network, a broadcast satellite
network and/or other communications network.
[0021] In various embodiments, the access terminal 112 can include
a digital subscriber line access multiplexer (DSLAM), cable modem
termination system (CMTS), optical line terminal (OLT) and/or other
access terminal. The data terminals 114 can include personal
computers, laptop computers, netbook computers, tablets or other
computing devices along with digital subscriber line (DSL) modems,
data over coax service interface specification (DOCSIS) modems or
other cable modems, a wireless modem such as a 4G, 5G, or higher
generation modem, an optical modem and/or other access devices.
[0022] In various embodiments, the base station or access point 122
can include a 4G, 5G, or higher generation base station, an access
point that operates via an 802.11 standard such as 802.11n,
802.11ac or other wireless access terminal. The mobile devices 124
can include mobile phones, e-readers, tablets, phablets, wireless
modems, and/or other mobile computing devices.
[0023] In various embodiments, the switching device 132 can include
a private branch exchange or central office switch, a media
services gateway, VoIP gateway or other gateway device and/or other
switching device. The telephony devices 134 can include traditional
telephones (with or without a terminal adapter), VoIP telephones
and/or other telephony devices.
[0024] In various embodiments, the media terminal 142 can include a
cable head-end or other TV head-end, a satellite receiver, gateway
or other media terminal 142. The display devices 144 can include
televisions with or without a set top box, personal computers
and/or other display devices.
[0025] In various embodiments, the content sources 175 include
broadcast television and radio sources, video on demand platforms
and streaming video and audio services platforms, one or more
content data networks, data servers, web servers and other content
servers, and/or other sources of media.
[0026] In various embodiments, the communications network 125 can
include wired, optical and/or wireless links and the network
elements 150, 152, 154, 156, etc. can include service switching
points, signal transfer points, service control points, network
gateways, media distribution hubs, servers, firewalls, routers,
edge devices, switches and other network nodes for routing and
controlling communications traffic over wired, optical and wireless
links as part of the Internet and other public networks as well as
one or more private networks, for managing subscriber access, for
billing and network management and for supporting other network
functions.
[0027] FIG. 2A is a block diagram illustrating an example,
non-limiting embodiment of a system 200a functioning within, or
operatively overlaid upon, the communication network 100 of FIG. 1
in accordance with various aspects described herein. In particular,
the system 200a may include a tower/base station 202a that may be
used to provide service to one or more communication devices, e.g.,
communication devices 206a, 210a, 214a, 218a, 222a, and 226a. The
tower 202a may be communicatively linked/coupled to backhaul
infrastructure (not shown in FIG. 2A) via wired and/or wireless
connections.
[0028] The coverage provided by the tower 202a may be divided into
multiple sectors/faces, such as for example three sectors/faces
denoted as sector/face A, second/face B, and sector/face C in FIG.
2A. Each of the sectors/faces may be further divided into multiple
cells, e.g., cell 234a in FIG. 2A. Each cell within a sector/face
may operate at a distinct carrier frequency. The use of multiple
carrier frequencies within a sector/face may enhance a data
carrying capacity, which in turn may enhance a quality of
experience (QoE) or quality of service (QoS).
[0029] In the instance of the exemplary system 200a shown in FIG.
2A, the communication devices 206a and 210a may obtain service via
the sector/face A, the communication devices 214a-222a may obtain
service via the sector/face B, and the communication device 226a
may obtain service via the sector/face C. However, one or more of
the communication devices 206a-226a may be a mobile device and may
migrate from a scope of coverage associated with a first
sector/face (e.g., sector/face A) to a scope of coverage associated
with a second sector/face (e.g., sector/face B). In this regard,
the tower 202a may facilitate a handover of service (e.g., a
handover of a communication session) from the first sector/face to
the second sector/face. Still further, in some embodiments a
handover of service may be provided from the tower 202a to another
tower (not shown in FIG. 2A) if a communication device leaves the
range of coverage provided by any of the sectors/faces associated
with the tower 202a.
[0030] Aspects of the system 200a may be implemented in conjunction
with an allocation of resources. To demonstrate, and referring to
FIG. 2B, a system 200b is shown that may be used to
dimension/allocate resources (e.g., radio resources, communication
bandwidth, control resources, etc.) associated with a communication
network or system, such as the system 200a of FIG. 2A. The system
200b may include a load-aware dimensioning engine 204b, a
forecasting engine 208b, and a QoS-aware dimensioning engine
212b.
[0031] The load-aware dimensioning engine 204b may generate
profiles for, e.g., each cell of the network or system. The
profiles, which may include or be based on various parameters
(e.g., signals, interference, noise, etc.), may be specified in an
uplink direction, a downlink direction, or both uplink and downlink
directions. In some embodiments, one or more of the parameters may
be combined in connection with a given profile. For example, in
some embodiments the load-aware dimensioning engine 204b may
generate a signal-to-interference-plus-noise (SINR) profile for a
given cell. The SINR profile may be based at least in part on
estimates/projections of one or more communication devices being
located within the cell, estimates/projections of one or more
communication sessions of the communication device(s) falling
within a given SINR class/category, and estimates/projections
and/or measurements of throughput within the given SINR
class/category. The typical range of possible spectral efficiencies
that a random communication device may experience, with reference
to a particular cell j, may be segmented into a plurality of bins
m.sub.j. The spectral efficiency in bin u with reference to cell j
may be represented by the bin-specific spectral efficiency
parameter .theta..sub.ju. Aspects of past records (e.g., past drive
test records) may drive values for p.sub.ju, which denotes the
probability that a random communication device attached to cell j
will find itself in bin u.
[0032] The forecasting engine 208b may generate forecasts of
traffic in the network or system. The forecasts may be based on
traffic projections at a given level of granularity. In some
embodiments, the generation of the forecasts may take into
considerations of a type of traffic (e.g., voice and video), and
elasticity in terms of data volume at different priority
levels/classes.
[0033] The QoS-aware dimensioning engine 212b may be operative on
the outputs of the load-aware dimensioning engine 204b and the
forecasting engine 208b to provide/generate dimensioned resource
allocations. The generation of such resource allocations by, e.g.,
the QoS-aware dimensioning engine 212b is described in further
detail below in connection with, e.g., the method 200c of FIG. 2C.
Aspects of the method 200c may be executed/implemented in
conjunction with, or with respect to, an uplink direction, a
downlink direction, or a combination thereof.
[0034] For purposes of illustration, it may be assumed that a
particular sector/face of a system (e.g., system 200a of FIG. 2A)
initially has a total of .PSI. carriers or cells, where each of the
carriers/cells is arranged in a predetermined order of deployment,
and each of the carriers/cells is indexed as j=1, . . . , .PSI..
Further, it may be assumed that the cells corresponding to j=1
through j=.PSI.-1 are at their highest subscription level, and cell
j=.PSI. may be at any one of its intermediate subscription levels
(e.g., a subscription level that is less than, or equal to, a
subscription capacity maximum for the cell). Still further, each
carrier/cell j may initially have a provisioned spectrum Moreover,
each carrier/cell j may have an associated spectral efficiency. The
average spectral efficiency for the j.sup.th carrier/cell, denoted
as .theta..sub.j, may be calculated as follows:
.theta..sub.j=[.SIGMA..sub.u(p.sub.j,u.theta..sub.j,u)].sup.-1,
[0035] where p.sub.j,u denotes the probability of a communication
session of a communication device falling within an SINR bin u of
cell j for an arbitrary number of bins m (e.g., u=0, . . . , m-1),
and .theta..sub.j,u denotes the spectral efficiency in bin u
({p.sub.j,u, .theta..sub.j,u} are assumed to be available
beforehand from drive test data).
[0036] In the description that follows, it is assumed that there
are two guaranteed traffic classes (corresponding to k=0 and k=1),
where the guaranteed traffic classes correspond to: (1)
conversational voice (k=0), e.g., voice over LTE protocol [VoLTE]
supported at QCI priority level 1 in LTE networks, and (2)
conversational video (k=1), e.g., live streaming supported at QCI
priority level 2 in LTE networks. Still further, it is assumed that
there are four elastic data classes (corresponding to k=2 through
k=5). The elastic data classes (k=2 through k=5) each may
correspond to/include any combination of buffered video, email,
text (documents, chat), file transfers, peer-to-peer file sharing,
progressive video, and interactive gaming; e.g., the four data
traffic classes supported at QCI priority levels 6-9 in LTE
networks. One skilled in the art would appreciate that these
assumptions may be relaxed in a given embodiment to provide for
more or less guaranteed traffic classes and/or more or less elastic
data classes.
[0037] With the foregoing assumptions in place, in block 202c a
cell capacity for each of the .PSI. cells, C.sub.j, j=1, . . . ,
.PSI., may be computed. The capacity of the j.sup.th cell, C.sub.j,
may be computed as the product of the spectrum configured for the
cell (B.sub.j) and the spectral efficiency of the cell
(.theta..sub.j), e.g.:
C.sub.j=B.sub.j*.theta..sub.j
[0038] In block 206c, a determination may be made whether the
traffic utilization associated with the guaranteed traffic classes
(e.g., conversational voice (k=0) and conversational video (k=1) in
accordance with the foregoing assumptions) exceeds a threshold
R.sub.max, where R.sub.max represents the maximum fraction of the
available capacity allowed for the guaranteed traffic. If the
guaranteed traffic utilization does not exceed the threshold
(R.sub.max), flow may proceed from block 206c to block 210;
otherwise, flow may proceed from block 206c to block 230c.
[0039] In block 210c, erlangs associated with the guaranteed
traffic classes may be apportioned. For example, and in accordance
with projections/estimates regarding demand, E.sub.0 may denote the
erlangs for conversational voice (k=0) and E.sub.1 may denote the
erlangs for conversational video (k=1). As part of block 210c, the
elastic data volume of the sector/face (denoted as EDV) may be
apportioned to each of the cells j in an amount EDV.sub.j that is
in proportion to the capacity of the cell (C.sub.j) relative to the
total capacity (.SIGMA..sub.q C.sub.q), e.g.:
EDV.sub.j=EDV*(C.sub.j)/(.SIGMA..sub.q C.sub.q),
[0040] where the summation operator (.SIGMA.) is applied for all
cells `q` from 1 through .PSI.. In some embodiments, the
apportioning for E.sub.0 and E.sub.1 may use the same, or a
similar, scaling as set forth above for the elastic data
volume.
[0041] In block 214c, the elastic data capacity (D.sub.j) for
elastic data traffic (e.g., k=2 through k=5 in the foregoing
description) for each of the j cells may be computed as the
difference between the capacity of the cell (C.sub.j) and the
capacity allocated to the guaranteed traffic classes (k=0 and k=1
in the foregoing description). In other words, the elastic data
capacity may be computed as:
D.sub.j=C.sub.j-([E.sub.j,0*b.sub.0]+[E.sub.j,1*b.sub.1]),
[0042] where E.sub.j,0 denotes the apportioned erlangs for the
j.sup.th cell associated with the first guaranteed traffic class
(e.g., k=0), E.sub.j,1 denotes the erlangs for the j.sup.th cell
associated with the second guaranteed traffic class (e.g., k=1),
b.sub.0 denotes the bandwidth occupied by each session of the first
guaranteed traffic class, and bi denotes the bandwidth occupied by
each session of the second guaranteed traffic class.
[0043] In block 218c, and as described in further detail below,
analytical modeling and/or simulation may be performed based on the
elastic data capacity computed in block 214c to determine/compute
throughput T.sub.j,k for each cell j and each of the elastic data
classes (k=2 through k=5 in this example).
[0044] The choice of whether to perform analytical modeling or
simulation as part of block 218c may be based on one or more
considerations. An apportioning of traffic volumes among the cells
(i.e., load balancing) may be common to both analytical modeling
and simulation, and both may be used to compute performance (e.g.,
throughput) in an individual cell (post load balancing). However,
different tradeoffs may be present between execution speed and
granularity of results. For example, analytical modeling may be
fast but might only provide an average of throughput estimates.
Conversely, simulation may tend to be slow, but may provide greater
flexibility and may provide more refined metrics such as a
cumulative distribution function (CDF) of throughputs.
[0045] In block 222c, a determination may be made whether the
throughputs computed in block 218c all satisfy a threshold, denoted
as S.sub.min(k). For example, each of the classes (k=2 through k=5)
may have its own threshold (e.g., S.sub.min,k) as part of block
222c.
[0046] If the determination of block 222c is answered in the
affirmative, flow may proceed from block 222c to block 226c.
Otherwise, flow may proceed from block 222c to block 230c.
[0047] In block 226c, the count of cells may be updated to
correspond to the last count of cells. As described above,
initially the count of cells may be set equal to .PSI.; however,
execution of the method 200c (e.g., block 234c described below) may
result in a count that is different from .PSI.. As part of block
226c, the spectrum that is allocated to a given cell (e.g., the
cell corresponding to j=.PSI.) may be updated to correspond to the
last spectrum allocation to that cell. For example, execution of
block 234c or block 238c described below may result in a
new/different spectrum allocation.
[0048] In block 230c, a determination may be made whether the last
cell (e.g., the cell corresponding to j=.PSI.) is at a threshold
(e.g., maximal) spectrum allocation. If so, flow may proceed from
block 230c to block 234c. Otherwise, flow may proceed from block
230c to block 238c.
[0049] In block 234c, a new cell (e.g., a cell corresponding to
j=.PSI.+1) may be deployed at a predetermined (e.g., a minimum,
discrete) spectrum level. From block 234c, flow may proceed to
block 202c to facilitate additional (e.g., continued or periodic)
executions of the method 200c.
[0050] In block 238c, the spectrum associated with the last cell
(e.g., j=.PSI.) may be increased (e.g., incremented) to the next
available level. From block 238c, flow may proceed to block 202c to
facilitate additional (e.g., continued or periodic) executions of
the method 200c.
[0051] As described above in relation to block 218c, in some
embodiments analytical modeling may be utilized to compute the
throughput (T.sub.j,k) for each elastic data class (k=2 through k=5
in the given example) for each cell (initially, j=1 through
j=.PSI.). For a given cell j, the analytical modeling may be based
on the elastic data volume apportioned to the cell (EDV.sub.j) as
computed/determined in block 210c and the elastic data capacity
(D.sub.j) computed/determined in block 214c. In particular, and
with reference to G. Fayolle et al., "Sharing a Processor Among
Many Job Classes", Journal of the ACM, Vol. 27, No. 3, July 1980,
pp. 519-532, the contents of which are incorporated herein by way
of reference, equations described therein (hereinafter referred to
as the Fayolle equations) may be utilized/solved to compute the
throughputs (T.sub.j,k) as part of block 218c. To demonstrate, and
for the elastic data classes (k=2 through k=5 in this example), and
assuming a set of scheduler priority weights wk for k=2 through
k=5, the following system of Fayolle equations may be
computed/solved to ultimately obtain the throughputs
(T.sub.j,k):
x.sub.j,k*[1-.SIGMA..sub.r((Q.sub.j,r*w.sub.r)/(w.sub.r+w.sub.k))]-[.SIG-
MA..sub.r((Q.sub.j,r*w.sub.r)*x.sub.j,r/(w.sub.r+w.sub.k))=1/D.sub.j,
[0052] where Q.sub.j,r=EDV.sub.j,r/D.sub.j and denotes the class
`r` resource block utilization within cell j, and the summation
operators (.SIGMA..sub.r) are applied over the total number of
elastic data priority classes (e.g., r=2 through r=5 in this
example). In particular, the Fayolle equations may be solved to
determine values for the variables x.sub.j,k. Once the values
x.sub.j,k are known/computed, the throughputs (T.sub.j,k) may be
computed as the inverse of those values, e.g.:
T.sub.j,k=1/x.sub.j,k
[0053] As described above in relation to block 218c, in some
embodiments simulation may be utilized to compute the throughput
(T.sub.j,k) for each elastic data class (k=2 through k=5 in the
given example) for each cell (initially, j=1 through j=.PSI.).
Within the simulation, a Markovian birth-death queuing framework
may be adopted/incorporated. Each of the guaranteed traffic classes
(k=0 and k=1 in the foregoing description) may have an
exponentially distributed random holding time with a fixed average
denoted as H.sub.k; e.g., for the first guaranteed traffic class
the average holding time may be assumed to be H.sub.0 and for the
second guaranteed traffic class the average holding time may be
assumed to be H.sub.1. As used herein, a holding time may be
assumed to be approximately equal to an amount of time that a
resource is utilized/consumed as part of a communication session
associated with a given traffic class. Each of the elastic data
classes (e.g., k=2 through k=5 in the foregoing description) may
have associated transactions, where each transaction includes an
exponentially distributed average payload pld.sub.k. Inter-arrival
times of guaranteed traffic class transactions (i.e., voice/video
calls) and elastic data traffic class transactions may be modeled
as exponentially distributed random variables. Time-based epochs
may be defined by one or more events, such as for example an
arrival (e.g., a start) of a session associated with a guaranteed
traffic class (e.g., k=0 and k=1 in the foregoing examples), an
arrival of a session associated with an elastic data class (e.g.,
k=2 through k=5 in the foregoing examples), a departure (e.g., a
termination) of a session associated with a guaranteed traffic
class, and a departure of a session associated with an elastic data
class.
[0054] With the foregoing assumptions in place as part of the
simulation, a state of the system or network may be captured in a
vector of the form n.sub.k,u, where n.sub.k,u denotes the number of
active sessions belonging to QoS/priority class k and SINR bin u
(for u=0 to u=m-1). Upon arrival/start of a session, the
corresponding SINR class (u) may be chosen at random per a
probability mass distribution {p.sub.u} (estimated beforehand from
drive test data).
[0055] Referring now to the method 200d of FIG. 2D, in block 204d
parameters/variables of the method may be initialized. For example,
as part of block 204d, the state vector (n.sub.k,u) may be
initialized to zero and a simulation time (sim.sub.t) may be set
equal to zero. Furthermore, the spectrum share available for
elastic data, BD, is set equal to B, the total spectrum configured
at the cell.
[0056] In block 208d, a sojourn time interval (.tau.) may be
computed. The sojourn time interval (.tau.) may be modeled as an
exponentially distributed random variable, in accordance with a
state exit rate that is equal to the sum of arrival and departure
rates (known whenever this block is entered) as described in
further detail below. At the start of simulation (or following an
epoch when the system becomes empty) there are no pending
transactions (i.e., n.sub.k,u=0.A-inverted.k, u); hence the net
departure rate is 0. The exponentially distributed sojourn time
.tau. in this situation is determined in block 204d as the sum of
only the (known) arrival rates from all traffic classes.
Transaction arrival rates for voice and video (k=0, 1) equal the
respective projected Erlangs divided by the respective holding
times, and those for each of the elastic data classes (k=2, . . . ,
5) equals the respective projected traffic volume divided by an
assumed payload size per transaction x (a specified system
constant, e.g., x=1 Mbits). In the more general situation where
there are pending transactions, computation of the non-zero
departure rates to be included is state-dependent, hence more
complex, and will be described in further detail in the sequel.
[0057] In block 212d, the simulation time (sim.sub.t) may be set
equal to the sum of the current or last value of the simulation
time (sim.sub.t) and the sojourn interval (.tau.)-advancement of
the simulated virtual time.
[0058] In block 216d, a specific coarse event (i.e., an arrival or
a departure) may be differentiated, via pseudo-random coin toss
based on the logic that the probability of that event
(arrival/departure) equals the exit rate into that event (from the
preceding state) divided by the total exit rate from the preceding
state. For example, at the top level of hierarchy, the average
sojourn time equals 1/(arrival rate+departure rate), prob(next
event being arrival)=arrival rate/(arrival rate+departure rate) and
prob(next event being departure)=departure rate/(arrival
rate+departure rate). If the event is an arrival associated with a
guaranteed traffic class, flow may proceed from block 216d to block
220d. If the event is an arrival associated with an elastic data
traffic class, flow may proceed from block 216d to block 224d. If
the event is a departure associated with a guaranteed traffic
class, flow may proceed from block 216d to block 228d. If the event
is a departure associated with an elastic traffic class, flow may
proceed from block 216d to block 232d. Note that finer-grain
differentiation among guaranteed or elastic arrivals and guaranteed
or elastic class departures is again carried out based on
pseudo-random coin tosses driven by ratios of the (known) entrance
rates, analogously as described above for coarse event
differentiation.
[0059] In block 220d, a determination may be made whether call
blocking is implemented (either broadly speaking, or with respect
to the particular arrival identified in block 216d. Blocking may
entail effectively discarding/ignoring/dropping the arrival (as
identified in block 216d) if it meets one of a set of blocking
criteria--e.g., if the current total voice and video occupancy in
the system exceeds an allowed threshold, or by virtue of a
junk/spam filter that blocks incoming arrivals from particular
phone numbers or communication devices. If the incoming arrival is
blocked, flow may proceed from block 220d to block 208d. Otherwise
the arrival is accepted, and flow may proceed from block 220d to
block 236d.
[0060] In block 236d, the state vector (n.sub.k,u, k=0, 1) may be
incremented in accordance with the guaranteed traffic class arrival
(of block 216d), thereby increasing the resource/capacity
utilization for guaranteed traffic and effectively decreasing the
resources/capacity available for elastic data. Fine grain
identification of a specific guaranteed class among k=0, 1, is
carried out based on the pseudo-random coin toss logic driven by
ratios of exit rates, described above for event classification at
the higher levels of hierarchy. Next, fine grain identification of
a specific SINR bin u is carried out via pseudo-random coin toss
logic driven by the known probabilities of a transaction falling in
various SINR bins (i.e., {p.sub.k,u}). The aforementioned logic is
analogously applicable to the description set forth below as well.
To enable computation of the next sojourn time .tau. in block 216d
and the following event resolutions as described above, the
departure rate from the current state is now updated by adding the
inverse of the call holding time. Furthermore, the spectrum budget
available to data BD is decreased by b.sub.k/.theta..sub..,u where
b.sub.k denotes the payload bandwidth occupied by the arriving
guaranteed transaction (of priority class k=0, 1) and
.theta..sub..,u denotes the spectral efficiency achieved in the
SINR bin u for the cell in consideration, where the arrival
landed.
[0061] In block 224d, the state occupancy (n.sub.k,u, k=2, . . . ,
5) may be incremented in accordance with the elastic data traffic
class arrival (of block 216d). User throughputs as well as exit
rates for all data states (i.e., {(k, u), k=2, . . . , 5} may need
to be updated concurrently in block 224d, since (unlike in the case
of guaranteed transactions), they are interrelated for elastic data
transactions. The complex procedure for this step is detailed
further below.
[0062] In block 228d, the state occupancy (n.sub.k,u, k=0, 1) may
be decremented in accordance with the guaranteed traffic class
departure (of block 216d), thereby decreasing the resource/capacity
utilization for guaranteed traffic and effectively increasing the
resources/capacity available for elastic data. In particular, the
spectrum budget available to data BD is decreased by
b.sub.k/.theta..sub..,u where b.sub.k denotes the payload bandwidth
occupied by the departing guaranteed transaction (of priority class
k=0, 1) and .theta..sub..,u denotes the spectral efficiency
achieved in the SINR bin u for the cell in question, where the
departing transaction belonged. Also, the departure rate from this
state is updated by subtracting the inverse of the call holding
time.
[0063] In block 232d, the state occupancy (n.sub.k,u, k=2, . . . ,
5) may be decremented in accordance with the elastic data traffic
class departure (of block 216d). User throughputs as well as exit
rates for all components of the state vector for data (i.e.,
{n.sub.k,u, k=2, . . . , 5} may need to be updated concurrently in
block 232d, since (unlike in the case of guaranteed transactions),
they are interrelated for elastic data transactions. The complex
procedure for this step is detailed further below.
[0064] As part of each of blocks 236d, 224d, 228d, and 232d,
guaranteed traffic class and elastic data traffic class session
arrival or departure rates may be updated/captured, as applicable,
based on the nature of the event identified in block 216d. For
example, for a given class of the guaranteed traffic classes,
session arrival rates (see block 236d) may be computed as the
erlangs for the given class divided by the holding time for the
class. For a given class of the guaranteed traffic classes, session
departure rates (see block 228d) may be computed as the number of
active sessions for the given class divided by the hold time for
the class. Following the arrival/departure of a guaranteed class
transaction, the spectrum share available for elastic data, BD, may
be decreased/increased by an amount equal to the bandwidth
occupancy of the transaction divided by the spectrum efficiency
enjoyed by the transaction. For a given class of the elastic data
traffic classes, session arrival rates (see block 224d) may be
computed as the respective volume of data per unit time (as
illustratively measured in Megabits per second [Mbps]) divided by
the data payload size of the session/transaction x (e.g., x=1
Mbits). Departure rates for sessions of the elastic data traffic
classes (see block 232d) may be interrelated to each other and to
the number of transactions associated with the guaranteed traffic
classes, and may be modeled/computed in accordance with scheduling
priority rules.
[0065] To determine the update rules applicable for each elastic
data class state (k, u), with , k=2, . . . , 5 being the traffic
class and u being the SINR bin, the procedure may be as follows.
Per the weighted proportionate-fairness scheduling policy assumed
to be in place within the cell schedulers, the current spectrum
share of each active transaction in this state may be given by
s.sub.k=BD.times.w.sub.k/(.SIGMA..sub.l .SIGMA..sub.m
w.sub.ln.sub.l,m), the summation in the denominator being across
all data states (i.e., l=2, . . . , 5), where BD denotes the
currently available aggregate data spectrum share, w.sub.l denotes
the scheduler weight assigned to elastic priority class l and
n.sub.l,m denotes the number of currently ongoing transactions in
elastic state (l, m). Next, the current user throughput enjoyed by
each active transaction in state (k, u) is given by
T.sub.k,u=s.sub.k.times..theta..sub..,u where .theta..sub..,u
denotes the spectrum efficiency achievable in SINR bin u for the
cell in question, as recorded in advance from drive test data.
Finally, the departure rate from state (k, u) is given by
n.sub.k,u.times.T.sub.k,u/x. Note that these steps may need to be
carried out for all states upon each arrival to/departure from any
of the elastic data or guaranteed states. The procedure may be
simpler when a guaranteed transaction arrival/departure
occurs--only BD may need to be scaled in the above equations.
[0066] As part of each of blocks 236d, 224d, 228d, and 232d,
aggregate state exit rates may be computed to facilitate the
sojourn time computation and event resolution (based on
pseudo-random tosses aided by ratios of rates). For example, the
(total) state exit rate may be computed as the sum of all
session/transaction arrival rates and the session/transaction
departure rates described above. The state exit rate may be used to
determine the sojourn time interval (.tau.) as described above in
connection with block 208d.
[0067] As part of each of blocks 236d, 224d, 228d, and 232d,
samples of throughput (T.sub.k,u) may be recorded for a
communication device for each k and u, where applicable, with a
statistical probability weighting applied thereto. In some
embodiments, the probability weighting may be based on (a product
of) the state vector (n.sub.k,u) and the sojourn time interval
(.tau.).
[0068] In block 240d, a determination may be made whether the
simulation time (sim.sub.t) exceeds a threshold (time_limit). If
not, flow may proceed from block 240d to block 208d; otherwise,
flow may proceed from block 240d to block 244d. The threshold
(time_limit) of block 240d may be based on experimentation and may
be selected to ensure accuracy/convergence is obtained, while at
the same time avoiding excessive delay (e.g., delay in an amount
greater than a threshold) in generating simulation outputs/results
described below.
[0069] In block 244d, the (probability weighted) recorded
throughput values/samples obtained as part of blocks 236d, 224d,
228d, and 232d may be processed, potentially as part of generating
one or more outputs (e.g., reports, displays, audio
renderings/presentations, etc.). The processing of block 244d may
include the application of one or more filters to reduce the impact
of spurious values/samples.
[0070] In block 248d, a blocking probability for guaranteed traffic
classes may be computed and/or a tail probability for the data
(e.g., the recorded throughput values, as subject to any
probability weighting) may be computed. The computations of block
248d may be used as part of additional/future executions of the
method 200c and/or the method 200d (e.g., may be used as a filter
or prediction of a likelihood of an event--e.g., a
blocking--occurring, which may be used to modify resource
allocations potentially as part of one or more weightings). The
computations of block 248d may provide an indication of a degree of
confidence in the outputs of block 244d, which may serve as a
further refinement in relation to resource allocations.
[0071] In some embodiments, the determination of the event type in
block 216d may adhere/conform to three hierarchical steps. In a
first of the steps, a coarse resolution may be performed to
identify the event as among one of: (a) an arrival, (b) a first
guaranteed traffic class departure, (c) a second guaranteed traffic
class departure, and (d) an elastic data traffic class departure.
In a second of the steps, if the event is: an arrival (a), then a
one-step array lookup may be used to resolve its QoS and SINR class
(i.e., the computational steps of block 216d may be replaced by an
associative array lookup at fine granularity, to speed up
execution); if it is a departure involving a guaranteed traffic
class ((b) or (c)), then a resolution of the SINR class may be
performed; if it is an elastic data traffic class departure (d),
then a resolution of the QoS class may be performed. In a third of
the steps, if the event is a departure involving a certain QoS
class (as determined in the second step) of an elastic data traffic
class (as determined in the first step), then a resolution of the
SINR class may be performed.
[0072] While for purposes of simplicity of explanation, the
respective processes are shown and described as a series of blocks
in FIGS. 2C and 2D, it is to be understood and appreciated that the
claimed subject matter is not limited by the order of the blocks,
as some blocks may occur in different orders and/or concurrently
with other blocks from what is depicted and described herein.
Moreover, not all illustrated blocks may be required to implement
the methods described herein.
[0073] According to aspects of this disclosure, a QoS-based
approach may be utilized as part of dimensioning/allocating
resources (e.g., wireless spectrum) for a network. Such allocations
may be based on a progressive search/query for the required number
of cells and cell bandwidth assignments that may meet/satisfy
stability and target performance criteria. In various embodiments,
a sector/face load may be split among multiple cells in accordance
with load-balancing policies. A scheduling policy may allocate
resources in accordance with weights assigned to elastic data
traffic classes.
[0074] Aspects of this disclosure provide analytical modeling and
simulation as techniques for estimating performance in conjunction
with resource allocations. Analytical modeling may enable
dimensioning/allocations based on average throughput criteria.
Simulation may be used to enable dimensioning/allocations based on
more refined tail probability throughput criteria.
[0075] As described herein, aspects of this disclosure provide for
resource (e.g., wireless spectrum) dimensioning/allocations subject
to meeting differentiated QoS targets among multiple types of
traffic classes. As set forth herein, VoIP, (conversational) video,
and elastic data are examples of QoS traffic classes that may be
used. Other forms/types of traffic classifications may be
included/utilized in some embodiments.
[0076] As set forth above, additional carriers/cells may be
allocated within a given sector/face in response to changes (e.g.,
increases) in demand. Accordingly, aspects of this disclosure may
reduce (e.g., minimize) inter-sector interference by only
using/allocating carriers/cells that are needed to meet QoS
demands/requirements.
[0077] Aspects of this disclosure may incorporate SINR profiles,
traffic demand measurements and estimates, performance criteria,
and scheduling and load balancing policies as part of generating
resource (e.g., spectrum, carrier frequency, bandwidth, etc.)
allocations in a network.
[0078] Referring now to FIG. 3, a block diagram 300 is shown
illustrating an example, non-limiting embodiment of a virtualized
communication network in accordance with various aspects described
herein. In particular a virtualized communication network is
presented that can be used to implement some or all of the
subsystems and functions of system 100, the subsystems and
functions of systems 200a and 200b, and methods 200c and 200d
presented in FIG. 1 and FIGS. 2A-2D. For example, virtualized
communication network 300 can facilitate in whole or in part
computing a capacity for each cell of a plurality of cells
associated with a network, responsive to determining that a
utilization of wireless spectrum associated with a first plurality
of classes of traffic in the network is greater than a first
threshold, upgrading a capacity in the network, and responsive to
determining that the utilization of the wireless spectrum
associated with the first plurality of classes of traffic in the
network is less than or equal to the first threshold: computing a
capacity for a second plurality of classes of traffic for each cell
of the plurality of cells in accordance with the capacity for each
cell, performing analytical modeling or engaging a simulation to
determine a throughput for each of the second plurality of classes
of traffic for each cell of the plurality of cells in accordance
with the computing of the capacity for the second plurality of
classes of traffic, and responsive to determining that the
throughput for at least one of the second plurality of classes of
traffic for at least one cell of the plurality of cells is less
than a second threshold, upgrading the capacity in the network,
wherein the upgrading of the capacity in the network comprises one
of: deploying a new cell at a predetermined level of wireless
spectrum, wherein the new cell is not included in the plurality of
cells, or increasing a wireless spectrum allocation of a first cell
of the plurality of cells. Virtualized communication network 300
can facilitate in whole or in part responsive to determining that a
utilization of wireless spectrum associated with a first class of
traffic in a network is greater than a first threshold, performing
an upgrade of a capacity in the network, and responsive to
determining that the utilization of the wireless spectrum
associated with the first class of traffic in the network is less
than or equal to the first threshold: computing a capacity for a
second class of traffic for each cell of a plurality of cells of
the network, performing analytical modeling or engaging a
simulation to determine a throughput for the second class of
traffic for each cell of the plurality of cells in accordance with
the computing of the capacity for the second class of traffic, and
responsive to determining that the throughput for the second class
of traffic for at least one cell of the plurality of cells is less
than a second threshold, performing the upgrade of the capacity in
the network, wherein the performing of the upgrade of the capacity
in the network comprises one of: deploying a new cell at a
predetermined level of wireless spectrum, wherein the new cell is
not included in the plurality of cells, or increasing a wireless
spectrum allocation of a first cell of the plurality of cells.
Virtualized communication network 300 can facilitate in whole or in
part determining whether a utilization of wireless spectrum
associated with a guaranteed class of traffic in a network is
greater than a first threshold, responsive to the determining
indicating that the utilization of the wireless spectrum associated
with the guaranteed class of traffic is greater than the first
threshold, causing an upgrade of a capacity in the network, and
responsive to the determining indicating that the utilization of
the wireless spectrum associated with the guaranteed class of
traffic is not greater than the first threshold: determining a
throughput for a non-guaranteed class of traffic for each cell of a
plurality of cells of the network, and responsive to determining
that the throughput for the non-guaranteed class of traffic for at
least one cell of the plurality of cells is less than a second
threshold, causing the upgrade of the capacity in the network,
wherein the causing of the upgrade of the capacity in the network
comprises: deploying a new cell at a predetermined level of
wireless spectrum, wherein the new cell is not included in the
plurality of cells, increasing a wireless spectrum allocation of a
first cell of the plurality of cells, or a combination thereof.
[0079] In particular, a cloud networking architecture is shown that
leverages cloud technologies and supports rapid innovation and
scalability via a transport layer 350, a virtualized network
function cloud 325 and/or one or more cloud computing environments
375. In various embodiments, this cloud networking architecture is
an open architecture that leverages application programming
interfaces (APIs); reduces complexity from services and operations;
supports more nimble business models; and rapidly and seamlessly
scales to meet evolving customer requirements including traffic
growth, diversity of traffic types, and diversity of performance
and reliability expectations.
[0080] In contrast to traditional network elements--which are
typically integrated to perform a single function, the virtualized
communication network employs virtual network elements (VNEs) 330,
332, 334, etc. that perform some or all of the functions of network
elements 150, 152, 154, 156, etc. For example, the network
architecture can provide a substrate of networking capability,
often called Network Function Virtualization Infrastructure (NFVI)
or simply infrastructure that is capable of being directed with
software and Software Defined Networking (SDN) protocols to perform
a broad variety of network functions and services. This
infrastructure can include several types of substrates. The most
typical type of substrate being servers that support Network
Function Virtualization (NFV), followed by packet forwarding
capabilities based on generic computing resources, with specialized
network technologies brought to bear when general purpose
processors or general purpose integrated circuit devices offered by
merchants (referred to herein as merchant silicon) are not
appropriate. In this case, communication services can be
implemented as cloud-centric workloads.
[0081] As an example, a traditional network element 150 (shown in
FIG. 1), such as an edge router can be implemented via a VNE 330
composed of NFV software modules, merchant silicon, and associated
controllers. The software can be written so that increasing
workload consumes incremental resources from a common resource
pool, and moreover so that it's elastic: so the resources are only
consumed when needed. In a similar fashion, other network elements
such as other routers, switches, edge caches, and middle-boxes are
instantiated from the common resource pool. Such sharing of
infrastructure across a broad set of uses makes planning and
growing infrastructure easier to manage.
[0082] In an embodiment, the transport layer 350 includes fiber,
cable, wired and/or wireless transport elements, network elements
and interfaces to provide broadband access 110, wireless access
120, voice access 130, media access 140 and/or access to content
sources 175 for distribution of content to any or all of the access
technologies. In particular, in some cases a network element needs
to be positioned at a specific place, and this allows for less
sharing of common infrastructure. Other times, the network elements
have specific physical layer adapters that cannot be abstracted or
virtualized, and might require special DSP code and analog
front-ends (AFEs) that do not lend themselves to implementation as
VNEs 330, 332 or 334. These network elements can be included in
transport layer 350.
[0083] The virtualized network function cloud 325 interfaces with
the transport layer 350 to provide the VNEs 330, 332, 334, etc. to
provide specific NFVs. In particular, the virtualized network
function cloud 325 leverages cloud operations, applications, and
architectures to support networking workloads. The virtualized
network elements 330, 332 and 334 can employ network function
software that provides either a one-for-one mapping of traditional
network element function or alternately some combination of network
functions designed for cloud computing. For example, VNEs 330, 332
and 334 can include route reflectors, domain name system (DNS)
servers, and dynamic host configuration protocol (DHCP) servers,
system architecture evolution (SAE) and/or mobility management
entity (MME) gateways, broadband network gateways, IP edge routers
for IP-VPN, Ethernet and other services, load balancers,
distributers and other network elements. Because these elements
don't typically need to forward large amounts of traffic, their
workload can be distributed across a number of servers--each of
which adds a portion of the capability, and overall which creates
an elastic function with higher availability than its former
monolithic version. These virtual network elements 330, 332, 334,
etc. can be instantiated and managed using an orchestration
approach similar to those used in cloud compute services.
[0084] The cloud computing environments 375 can interface with the
virtualized network function cloud 325 via APIs that expose
functional capabilities of the VNEs 330, 332, 334, etc. to provide
the flexible and expanded capabilities to the virtualized network
function cloud 325. In particular, network workloads may have
applications distributed across the virtualized network function
cloud 325 and cloud computing environment 375 and in the commercial
cloud, or might simply orchestrate workloads supported entirely in
NFV infrastructure from these third party locations.
[0085] Turning now to FIG. 4, there is illustrated a block diagram
of a computing environment in accordance with various aspects
described herein. In order to provide additional context for
various embodiments of the embodiments described herein, FIG. 4 and
the following discussion are intended to provide a brief, general
description of a suitable computing environment 400 in which the
various embodiments of the subject disclosure can be implemented.
In particular, computing environment 400 can be used in the
implementation of network elements 150, 152, 154, 156, access
terminal 112, base station or access point 122, switching device
132, media terminal 142, and/or VNEs 330, 332, 334, etc. Each of
these devices can be implemented via computer-executable
instructions that can run on one or more computers, and/or in
combination with other program modules and/or as a combination of
hardware and software. For example, computing environment 400 can
facilitate in whole or in part computing a capacity for each cell
of a plurality of cells associated with a network, responsive to
determining that a utilization of wireless spectrum associated with
a first plurality of classes of traffic in the network is greater
than a first threshold, upgrading a capacity in the network, and
responsive to determining that the utilization of the wireless
spectrum associated with the first plurality of classes of traffic
in the network is less than or equal to the first threshold:
computing a capacity for a second plurality of classes of traffic
for each cell of the plurality of cells in accordance with the
capacity for each cell, performing analytical modeling or engaging
a simulation to determine a throughput for each of the second
plurality of classes of traffic for each cell of the plurality of
cells in accordance with the computing of the capacity for the
second plurality of classes of traffic, and responsive to
determining that the throughput for at least one of the second
plurality of classes of traffic for at least one cell of the
plurality of cells is less than a second threshold, upgrading the
capacity in the network, wherein the upgrading of the capacity in
the network comprises one of: deploying a new cell at a
predetermined level of wireless spectrum, wherein the new cell is
not included in the plurality of cells, or increasing a wireless
spectrum allocation of a first cell of the plurality of cells.
Computing environment 400 can facilitate in whole or in part
responsive to determining that a utilization of wireless spectrum
associated with a first class of traffic in a network is greater
than a first threshold, performing an upgrade of a capacity in the
network, and responsive to determining that the utilization of the
wireless spectrum associated with the first class of traffic in the
network is less than or equal to the first threshold: computing a
capacity for a second class of traffic for each cell of a plurality
of cells of the network, performing analytical modeling or engaging
a simulation to determine a throughput for the second class of
traffic for each cell of the plurality of cells in accordance with
the computing of the capacity for the second class of traffic, and
responsive to determining that the throughput for the second class
of traffic for at least one cell of the plurality of cells is less
than a second threshold, performing the upgrade of the capacity in
the network, wherein the performing of the upgrade of the capacity
in the network comprises one of: deploying a new cell at a
predetermined level of wireless spectrum, wherein the new cell is
not included in the plurality of cells, or increasing a wireless
spectrum allocation of a first cell of the plurality of cells.
Computing environment 400 can facilitate in whole or in part
determining whether a utilization of wireless spectrum associated
with a guaranteed class of traffic in a network is greater than a
first threshold, responsive to the determining indicating that the
utilization of the wireless spectrum associated with the guaranteed
class of traffic is greater than the first threshold, causing an
upgrade of a capacity in the network, and responsive to the
determining indicating that the utilization of the wireless
spectrum associated with the guaranteed class of traffic is not
greater than the first threshold: determining a throughput for a
non-guaranteed class of traffic for each cell of a plurality of
cells of the network, and responsive to determining that the
throughput for the non-guaranteed class of traffic for at least one
cell of the plurality of cells is less than a second threshold,
causing the upgrade of the capacity in the network, wherein the
causing of the upgrade of the capacity in the network comprises:
deploying a new cell at a predetermined level of wireless spectrum,
wherein the new cell is not included in the plurality of cells,
increasing a wireless spectrum allocation of a first cell of the
plurality of cells, or a combination thereof.
[0086] Generally, program modules comprise routines, programs,
components, data structures, etc., that perform particular tasks or
implement particular abstract data types. Moreover, those skilled
in the art will appreciate that the methods can be practiced with
other computer system configurations, comprising single-processor
or multiprocessor computer systems, minicomputers, mainframe
computers, as well as personal computers, hand-held computing
devices, microprocessor-based or programmable consumer electronics,
and the like, each of which can be operatively coupled to one or
more associated devices.
[0087] As used herein, a processing circuit includes one or more
processors as well as other application specific circuits such as
an application specific integrated circuit, digital logic circuit,
state machine, programmable gate array or other circuit that
processes input signals or data and that produces output signals or
data in response thereto. It should be noted that while any
functions and features described herein in association with the
operation of a processor could likewise be performed by a
processing circuit.
[0088] The illustrated embodiments of the embodiments herein can be
also practiced in distributed computing environments where certain
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed computing
environment, program modules can be located in both local and
remote memory storage devices.
[0089] Computing devices typically comprise a variety of media,
which can comprise computer-readable storage media and/or
communications media, which two terms are used herein differently
from one another as follows. Computer-readable storage media can be
any available storage media that can be accessed by the computer
and comprises both volatile and nonvolatile media, removable and
non-removable media. By way of example, and not limitation,
computer-readable storage media can be implemented in connection
with any method or technology for storage of information such as
computer-readable instructions, program modules, structured data or
unstructured data.
[0090] Computer-readable storage media can comprise, but are not
limited to, random access memory (RAM), read only memory (ROM),
electrically erasable programmable read only memory (EEPROM),flash
memory or other memory technology, compact disk read only memory
(CD-ROM), digital versatile disk (DVD) or other optical disk
storage, magnetic cassettes, magnetic tape, magnetic disk storage
or other magnetic storage devices or other tangible and/or
non-transitory media which can be used to store desired
information. In this regard, the terms "tangible" or
"non-transitory" herein as applied to storage, memory or
computer-readable media, are to be understood to exclude only
propagating transitory signals per se as modifiers and do not
relinquish rights to all standard storage, memory or
computer-readable media that are not only propagating transitory
signals per se.
[0091] Computer-readable storage media can be accessed by one or
more local or remote computing devices, e.g., via access requests,
queries or other data retrieval protocols, for a variety of
operations with respect to the information stored by the
medium.
[0092] Communications media typically embody computer-readable
instructions, data structures, program modules or other structured
or unstructured data in a data signal such as a modulated data
signal, e.g., a carrier wave or other transport mechanism, and
comprises any information delivery or transport media. The term
"modulated data signal" or signals refers to a signal that has one
or more of its characteristics set or changed in such a manner as
to encode information in one or more signals. By way of example,
and not limitation, communication media comprise wired media, such
as a wired network or direct-wired connection, and wireless media
such as acoustic, RF, infrared and other wireless media.
[0093] With reference again to FIG. 4, the example environment can
comprise a computer 402, the computer 402 comprising a processing
unit 404, a system memory 406 and a system bus 408. The system bus
408 couples system components including, but not limited to, the
system memory 406 to the processing unit 404. The processing unit
404 can be any of various commercially available processors. Dual
microprocessors and other multiprocessor architectures can also be
employed as the processing unit 404.
[0094] The system bus 408 can be any of several types of bus
structure that can further interconnect to a memory bus (with or
without a memory controller), a peripheral bus, and a local bus
using any of a variety of commercially available bus architectures.
The system memory 406 comprises ROM 410 and RAM 412. A basic
input/output system (BIOS) can be stored in a non-volatile memory
such as ROM, erasable programmable read only memory (EPROM),
EEPROM, which BIOS contains the basic routines that help to
transfer information between elements within the computer 402, such
as during startup. The RAM 412 can also comprise a high-speed RAM
such as static RAM for caching data.
[0095] The computer 402 further comprises an internal hard disk
drive (HDD) 414 (e.g., EIDE, SATA), which internal HDD 414 can also
be configured for external use in a suitable chassis (not shown), a
magnetic floppy disk drive (FDD) 416, (e.g., to read from or write
to a removable diskette 418) and an optical disk drive 420, (e.g.,
reading a CD-ROM disk 422 or, to read from or write to other high
capacity optical media such as the DVD). The HDD 414, magnetic FDD
416 and optical disk drive 420 can be connected to the system bus
408 by a hard disk drive interface 424, a magnetic disk drive
interface 426 and an optical drive interface 428, respectively. The
hard disk drive interface 424 for external drive implementations
comprises at least one or both of Universal Serial Bus (USB) and
Institute of Electrical and Electronics Engineers (IEEE) 1394
interface technologies. Other external drive connection
technologies are within contemplation of the embodiments described
herein.
[0096] The drives and their associated computer-readable storage
media provide nonvolatile storage of data, data structures,
computer-executable instructions, and so forth. For the computer
402, the drives and storage media accommodate the storage of any
data in a suitable digital format. Although the description of
computer-readable storage media above refers to a hard disk drive
(HDD), a removable magnetic diskette, and a removable optical media
such as a CD or DVD, it should be appreciated by those skilled in
the art that other types of storage media which are readable by a
computer, such as zip drives, magnetic cassettes, flash memory
cards, cartridges, and the like, can also be used in the example
operating environment, and further, that any such storage media can
contain computer-executable instructions for performing the methods
described herein.
[0097] A number of program modules can be stored in the drives and
RAM 412, comprising an operating system 430, one or more
application programs 432, other program modules 434 and program
data 436. All or portions of the operating system, applications,
modules, and/or data can also be cached in the RAM 412. The systems
and methods described herein can be implemented utilizing various
commercially available operating systems or combinations of
operating systems.
[0098] A user can enter commands and information into the computer
402 through one or more wired/wireless input devices, e.g., a
keyboard 438 and a pointing device, such as a mouse 440. Other
input devices (not shown) can comprise a microphone, an infrared
(IR) remote control, a joystick, a game pad, a stylus pen, touch
screen or the like. These and other input devices are often
connected to the processing unit 404 through an input device
interface 442 that can be coupled to the system bus 408, but can be
connected by other interfaces, such as a parallel port, an IEEE
1394 serial port, a game port, a universal serial bus (USB) port,
an IR interface, etc.
[0099] A monitor 444 or other type of display device can be also
connected to the system bus 408 via an interface, such as a video
adapter 446. It will also be appreciated that in alternative
embodiments, a monitor 444 can also be any display device (e.g.,
another computer having a display, a smart phone, a tablet
computer, etc.) for receiving display information associated with
computer 402 via any communication means, including via the
Internet and cloud-based networks. In addition to the monitor 444,
a computer typically comprises other peripheral output devices (not
shown), such as speakers, printers, etc.
[0100] The computer 402 can operate in a networked environment
using logical connections via wired and/or wireless communications
to one or more remote computers, such as a remote computer(s) 448.
The remote computer(s) 448 can be a workstation, a server computer,
a router, a personal computer, portable computer,
microprocessor-based entertainment appliance, a peer device or
other common network node, and typically comprises many or all of
the elements described relative to the computer 402, although, for
purposes of brevity, only a remote memory/storage device 450 is
illustrated. The logical connections depicted comprise
wired/wireless connectivity to a local area network (LAN) 452
and/or larger networks, e.g., a wide area network (WAN) 454. Such
LAN and WAN networking environments are commonplace in offices and
companies, and facilitate enterprise-wide computer networks, such
as intranets, all of which can connect to a global communications
network, e.g., the Internet.
[0101] When used in a LAN networking environment, the computer 402
can be connected to the LAN 452 through a wired and/or wireless
communication network interface or adapter 456. The adapter 456 can
facilitate wired or wireless communication to the LAN 452, which
can also comprise a wireless AP disposed thereon for communicating
with the adapter 456.
[0102] When used in a WAN networking environment, the computer 402
can comprise a modem 458 or can be connected to a communications
server on the WAN 454 or has other means for establishing
communications over the WAN 454, such as by way of the Internet.
The modem 458, which can be internal or external and a wired or
wireless device, can be connected to the system bus 408 via the
input device interface 442. In a networked environment, program
modules depicted relative to the computer 402 or portions thereof,
can be stored in the remote memory/storage device 450. It will be
appreciated that the network connections shown are example and
other means of establishing a communications link between the
computers can be used.
[0103] The computer 402 can be operable to communicate with any
wireless devices or entities operatively disposed in wireless
communication, e.g., a printer, scanner, desktop and/or portable
computer, portable data assistant, communications satellite, any
piece of equipment or location associated with a wirelessly
detectable tag (e.g., a kiosk, news stand, restroom), and
telephone. This can comprise Wireless Fidelity (Wi-Fi) and
BLUETOOTH.RTM. wireless technologies. Thus, the communication can
be a predefined structure as with a conventional network or simply
an ad hoc communication between at least two devices.
[0104] Wi-Fi can allow connection to the Internet from a couch at
home, a bed in a hotel room or a conference room at work, without
wires. Wi-Fi is a wireless technology similar to that used in a
cell phone that enables such devices, e.g., computers, to send and
receive data indoors and out; anywhere within the range of a base
station. Wi-Fi networks use radio technologies called IEEE 802.11
(a, b, g, n, ac, ag, etc.) to provide secure, reliable, fast
wireless connectivity. A Wi-Fi network can be used to connect
computers to each other, to the Internet, and to wired networks
(which can use IEEE 802.3 or Ethernet). Wi-Fi networks operate in
the unlicensed 2.4 and 5 GHz radio bands for example or with
products that contain both bands (dual band), so the networks can
provide real-world performance similar to the basic 10 BaseT wired
Ethernet networks used in many offices.
[0105] Turning now to FIG. 5, an embodiment 500 of a mobile network
platform 510 is shown that is an example of network elements 150,
152, 154, 156, and/or VNEs 330, 332, 334, etc. For example,
platform 510 can facilitate in whole or in part computing a
capacity for each cell of a plurality of cells associated with a
network, responsive to determining that a utilization of wireless
spectrum associated with a first plurality of classes of traffic in
the network is greater than a first threshold, upgrading a capacity
in the network, and responsive to determining that the utilization
of the wireless spectrum associated with the first plurality of
classes of traffic in the network is less than or equal to the
first threshold: computing a capacity for a second plurality of
classes of traffic for each cell of the plurality of cells in
accordance with the capacity for each cell, performing analytical
modeling or engaging a simulation to determine a throughput for
each of the second plurality of classes of traffic for each cell of
the plurality of cells in accordance with the computing of the
capacity for the second plurality of classes of traffic, and
responsive to determining that the throughput for at least one of
the second plurality of classes of traffic for at least one cell of
the plurality of cells is less than a second threshold, upgrading
the capacity in the network, wherein the upgrading of the capacity
in the network comprises one of: deploying a new cell at a
predetermined level of wireless spectrum, wherein the new cell is
not included in the plurality of cells, or increasing a wireless
spectrum allocation of a first cell of the plurality of cells.
Platform 510 can facilitate in whole or in part responsive to
determining that a utilization of wireless spectrum associated with
a first class of traffic in a network is greater than a first
threshold, performing an upgrade of a capacity in the network, and
responsive to determining that the utilization of the wireless
spectrum associated with the first class of traffic in the network
is less than or equal to the first threshold: computing a capacity
for a second class of traffic for each cell of a plurality of cells
of the network, performing analytical modeling or engaging a
simulation to determine a throughput for the second class of
traffic for each cell of the plurality of cells in accordance with
the computing of the capacity for the second class of traffic, and
responsive to determining that the throughput for the second class
of traffic for at least one cell of the plurality of cells is less
than a second threshold, performing the upgrade of the capacity in
the network, wherein the performing of the upgrade of the capacity
in the network comprises one of: deploying a new cell at a
predetermined level of wireless spectrum, wherein the new cell is
not included in the plurality of cells, or increasing a wireless
spectrum allocation of a first cell of the plurality of cells.
Platform 510 can facilitate in whole or in part determining whether
a utilization of wireless spectrum associated with a guaranteed
class of traffic in a network is greater than a first threshold,
responsive to the determining indicating that the utilization of
the wireless spectrum associated with the guaranteed class of
traffic is greater than the first threshold, causing an upgrade of
a capacity in the network, and responsive to the determining
indicating that the utilization of the wireless spectrum associated
with the guaranteed class of traffic is not greater than the first
threshold: determining a throughput for a non-guaranteed class of
traffic for each cell of a plurality of cells of the network, and
responsive to determining that the throughput for the
non-guaranteed class of traffic for at least one cell of the
plurality of cells is less than a second threshold, causing the
upgrade of the capacity in the network, wherein the causing of the
upgrade of the capacity in the network comprises: deploying a new
cell at a predetermined level of wireless spectrum, wherein the new
cell is not included in the plurality of cells, increasing a
wireless spectrum allocation of a first cell of the plurality of
cells, or a combination thereof.
[0106] In one or more embodiments, the mobile network platform 510
can generate and receive signals transmitted and received by base
stations or access points such as base station or access point 122.
Generally, mobile network platform 510 can comprise components,
e.g., nodes, gateways, interfaces, servers, or disparate platforms,
that facilitate both packet-switched (PS) (e.g., internet protocol
(IP), frame relay, asynchronous transfer mode (ATM)) and
circuit-switched (CS) traffic (e.g., voice and data), as well as
control generation for networked wireless telecommunication. As a
non-limiting example, mobile network platform 510 can be included
in telecommunications carrier networks, and can be considered
carrier-side components as discussed elsewhere herein. Mobile
network platform 510 comprises CS gateway node(s) 512 which can
interface CS traffic received from legacy networks like telephony
network(s) 540 (e.g., public switched telephone network (PSTN), or
public land mobile network (PLMN)) or a signaling system #7 (SS7)
network 560. CS gateway node(s) 512 can authorize and authenticate
traffic (e.g., voice) arising from such networks. Additionally, CS
gateway node(s) 512 can access mobility, or roaming, data generated
through SS7 network 560; for instance, mobility data stored in a
visited location register (VLR), which can reside in memory 530.
Moreover, CS gateway node(s) 512 interfaces CS-based traffic and
signaling and PS gateway node(s) 518. As an example, in a 3GPP UMTS
network, CS gateway node(s) 512 can be realized at least in part in
gateway GPRS support node(s) (GGSN). It should be appreciated that
functionality and specific operation of CS gateway node(s) 512, PS
gateway node(s) 518, and serving node(s) 516, is provided and
dictated by radio technology(ies) utilized by mobile network
platform 510 for telecommunication over a radio access network 520
with other devices, such as a radiotelephone 575.
[0107] In addition to receiving and processing CS-switched traffic
and signaling, PS gateway node(s) 518 can authorize and
authenticate PS-based data sessions with served mobile devices.
Data sessions can comprise traffic, or content(s), exchanged with
networks external to the mobile network platform 510, like wide
area network(s) (WANs) 550, enterprise network(s) 570, and service
network(s) 580, which can be embodied in local area network(s)
(LANs), can also be interfaced with mobile network platform 510
through PS gateway node(s) 518. It is to be noted that WANs 550 and
enterprise network(s) 570 can embody, at least in part, a service
network(s) like IP multimedia subsystem (IMS). Based on radio
technology layer(s) available in technology resource(s) or radio
access network 520, PS gateway node(s) 518 can generate packet data
protocol contexts when a data session is established; other data
structures that facilitate routing of packetized data also can be
generated. To that end, in an aspect, PS gateway node(s) 518 can
comprise a tunnel interface (e.g., tunnel termination gateway (TTG)
in 3GPP UMTS network(s) (not shown)) which can facilitate
packetized communication with disparate wireless network(s), such
as Wi-Fi networks.
[0108] In embodiment 500, mobile network platform 510 also
comprises serving node(s) 516 that, based upon available radio
technology layer(s) within technology resource(s) in the radio
access network 520, convey the various packetized flows of data
streams received through PS gateway node(s) 518. It is to be noted
that for technology resource(s) that rely primarily on CS
communication, server node(s) can deliver traffic without reliance
on PS gateway node(s) 518; for example, server node(s) can embody
at least in part a mobile switching center. As an example, in a
3GPP UMTS network, serving node(s) 516 can be embodied in serving
GPRS support node(s) (SGSN).
[0109] For radio technologies that exploit packetized
communication, server(s) 514 in mobile network platform 510 can
execute numerous applications that can generate multiple disparate
packetized data streams or flows, and manage (e.g., schedule,
queue, format . . . ) such flows. Such application(s) can comprise
add-on features to standard services (for example, provisioning,
billing, customer support . . . ) provided by mobile network
platform 510. Data streams (e.g., content(s) that are part of a
voice call or data session) can be conveyed to PS gateway node(s)
518 for authorization/authentication and initiation of a data
session, and to serving node(s) 516 for communication thereafter.
In addition to application server, server(s) 514 can comprise
utility server(s), a utility server can comprise a provisioning
server, an operations and maintenance server, a security server
that can implement at least in part a certificate authority and
firewalls as well as other security mechanisms, and the like. In an
aspect, security server(s) secure communication served through
mobile network platform 510 to ensure network's operation and data
integrity in addition to authorization and authentication
procedures that CS gateway node(s) 512 and PS gateway node(s) 518
can enact. Moreover, provisioning server(s) can provision services
from external network(s) like networks operated by a disparate
service provider; for instance, WAN 550 or Global Positioning
System (GPS) network(s) (not shown). Provisioning server(s) can
also provision coverage through networks associated to mobile
network platform 510 (e.g., deployed and operated by the same
service provider), such as the distributed antennas networks shown
in FIG. 1(s) that enhance wireless service coverage by providing
more network coverage.
[0110] It is to be noted that server(s) 514 can comprise one or
more processors configured to confer at least in part the
functionality of mobile network platform 510. To that end, the one
or more processor can execute code instructions stored in memory
530, for example. It is should be appreciated that server(s) 514
can comprise a content manager, which operates in substantially the
same manner as described hereinbefore.
[0111] In example embodiment 500, memory 530 can store information
related to operation of mobile network platform 510. Other
operational information can comprise provisioning information of
mobile devices served through mobile network platform 510,
subscriber databases; application intelligence, pricing schemes,
e.g., promotional rates, flat-rate programs, couponing campaigns;
technical specification(s) consistent with telecommunication
protocols for operation of disparate radio, or wireless, technology
layers; and so forth. Memory 530 can also store information from at
least one of telephony network(s) 540, WAN 550, SS7 network 560, or
enterprise network(s) 570. In an aspect, memory 530 can be, for
example, accessed as part of a data store component or as a
remotely connected memory store.
[0112] In order to provide a context for the various aspects of the
disclosed subject matter, FIG. 5, and the following discussion, are
intended to provide a brief, general description of a suitable
environment in which the various aspects of the disclosed subject
matter can be implemented. While the subject matter has been
described above in the general context of computer-executable
instructions of a computer program that runs on a computer and/or
computers, those skilled in the art will recognize that the
disclosed subject matter also can be implemented in combination
with other program modules. Generally, program modules comprise
routines, programs, components, data structures, etc. that perform
particular tasks and/or implement particular abstract data
types.
[0113] Turning now to FIG. 6, an illustrative embodiment of a
communication device 600 is shown. The communication device 600 can
serve as an illustrative embodiment of devices such as data
terminals 114, mobile devices 124, vehicle 126, display devices 144
or other client devices for communication via either communications
network 125. For example, computing device 600 can facilitate in
whole or in part computing a capacity for each cell of a plurality
of cells associated with a network, responsive to determining that
a utilization of wireless spectrum associated with a first
plurality of classes of traffic in the network is greater than a
first threshold, upgrading a capacity in the network, and
responsive to determining that the utilization of the wireless
spectrum associated with the first plurality of classes of traffic
in the network is less than or equal to the first threshold:
computing a capacity for a second plurality of classes of traffic
for each cell of the plurality of cells in accordance with the
capacity for each cell, performing analytical modeling or engaging
a simulation to determine a throughput for each of the second
plurality of classes of traffic for each cell of the plurality of
cells in accordance with the computing of the capacity for the
second plurality of classes of traffic, and responsive to
determining that the throughput for at least one of the second
plurality of classes of traffic for at least one cell of the
plurality of cells is less than a second threshold, upgrading the
capacity in the network, wherein the upgrading of the capacity in
the network comprises one of: deploying a new cell at a
predetermined level of wireless spectrum, wherein the new cell is
not included in the plurality of cells, or increasing a wireless
spectrum allocation of a first cell of the plurality of cells.
Computing device 600 can facilitate in whole or in part responsive
to determining that a utilization of wireless spectrum associated
with a first class of traffic in a network is greater than a first
threshold, performing an upgrade of a capacity in the network, and
responsive to determining that the utilization of the wireless
spectrum associated with the first class of traffic in the network
is less than or equal to the first threshold: computing a capacity
for a second class of traffic for each cell of a plurality of cells
of the network, performing analytical modeling or engaging a
simulation to determine a throughput for the second class of
traffic for each cell of the plurality of cells in accordance with
the computing of the capacity for the second class of traffic, and
responsive to determining that the throughput for the second class
of traffic for at least one cell of the plurality of cells is less
than a second threshold, performing the upgrade of the capacity in
the network, wherein the performing of the upgrade of the capacity
in the network comprises one of: deploying a new cell at a
predetermined level of wireless spectrum, wherein the new cell is
not included in the plurality of cells, or increasing a wireless
spectrum allocation of a first cell of the plurality of cells.
Computing device 600 can facilitate in whole or in part determining
whether a utilization of wireless spectrum associated with a
guaranteed class of traffic in a network is greater than a first
threshold, responsive to the determining indicating that the
utilization of the wireless spectrum associated with the guaranteed
class of traffic is greater than the first threshold, causing an
upgrade of a capacity in the network, and responsive to the
determining indicating that the utilization of the wireless
spectrum associated with the guaranteed class of traffic is not
greater than the first threshold: determining a throughput for a
non-guaranteed class of traffic for each cell of a plurality of
cells of the network, and responsive to determining that the
throughput for the non-guaranteed class of traffic for at least one
cell of the plurality of cells is less than a second threshold,
causing the upgrade of the capacity in the network, wherein the
causing of the upgrade of the capacity in the network comprises:
deploying a new cell at a predetermined level of wireless spectrum,
wherein the new cell is not included in the plurality of cells,
increasing a wireless spectrum allocation of a first cell of the
plurality of cells, or a combination thereof.
[0114] The communication device 600 can comprise a wireline and/or
wireless transceiver 602 (herein transceiver 602), a user interface
(UI) 604, a power supply 614, a location receiver 616, a motion
sensor 618, an orientation sensor 620, and a controller 606 for
managing operations thereof. The transceiver 602 can support
short-range or long-range wireless access technologies such as
Bluetooth.RTM., ZigBee.RTM., WiFi, DECT, or cellular communication
technologies, just to mention a few (Bluetooth.RTM. and ZigBee.RTM.
are trademarks registered by the Bluetooth.RTM. Special Interest
Group and the ZigBee.RTM. Alliance, respectively). Cellular
technologies can include, for example, CDMA-1.times., UMTS/HSDPA,
GSM/GPRS, TDMA/EDGE, EV/DO, WiMAX, SDR, LTE, as well as other next
generation wireless communication technologies as they arise. The
transceiver 602 can also be adapted to support circuit-switched
wireline access technologies (such as PSTN), packet-switched
wireline access technologies (such as TCP/IP, VoIP, etc.), and
combinations thereof.
[0115] The UI 604 can include a depressible or touch-sensitive
keypad 608 with a navigation mechanism such as a roller ball, a
joystick, a mouse, or a navigation disk for manipulating operations
of the communication device 600. The keypad 608 can be an integral
part of a housing assembly of the communication device 600 or an
independent device operably coupled thereto by a tethered wireline
interface (such as a USB cable) or a wireless interface supporting
for example Bluetooth.RTM.. The keypad 608 can represent a numeric
keypad commonly used by phones, and/or a QWERTY keypad with
alphanumeric keys. The UI 604 can further include a display 610
such as monochrome or color LCD (Liquid Crystal Display), OLED
(Organic Light Emitting Diode) or other suitable display technology
for conveying images to an end user of the communication device
600. In an embodiment where the display 610 is touch-sensitive, a
portion or all of the keypad 608 can be presented by way of the
display 610 with navigation features.
[0116] The display 610 can use touch screen technology to also
serve as a user interface for detecting user input. As a touch
screen display, the communication device 600 can be adapted to
present a user interface having graphical user interface (GUI)
elements that can be selected by a user with a touch of a finger.
The display 610 can be equipped with capacitive, resistive or other
forms of sensing technology to detect how much surface area of a
user's finger has been placed on a portion of the touch screen
display. This sensing information can be used to control the
manipulation of the GUI elements or other functions of the user
interface. The display 610 can be an integral part of the housing
assembly of the communication device 600 or an independent device
communicatively coupled thereto by a tethered wireline interface
(such as a cable) or a wireless interface.
[0117] The UI 604 can also include an audio system 612 that
utilizes audio technology for conveying low volume audio (such as
audio heard in proximity of a human ear) and high volume audio
(such as speakerphone for hands free operation). The audio system
612 can further include a microphone for receiving audible signals
of an end user. The audio system 612 can also be used for voice
recognition applications. The UI 604 can further include an image
sensor 613 such as a charged coupled device (CCD) camera for
capturing still or moving images.
[0118] The power supply 614 can utilize common power management
technologies such as replaceable and rechargeable batteries, supply
regulation technologies, and/or charging system technologies for
supplying energy to the components of the communication device 600
to facilitate long-range or short-range portable communications.
Alternatively, or in combination, the charging system can utilize
external power sources such as DC power supplied over a physical
interface such as a USB port or other suitable tethering
technologies.
[0119] The location receiver 616 can utilize location technology
such as a global positioning system (GPS) receiver capable of
assisted GPS for identifying a location of the communication device
600 based on signals generated by a constellation of GPS
satellites, which can be used for facilitating location services
such as navigation. The motion sensor 618 can utilize motion
sensing technology such as an accelerometer, a gyroscope, or other
suitable motion sensing technology to detect motion of the
communication device 600 in three-dimensional space. The
orientation sensor 620 can utilize orientation sensing technology
such as a magnetometer to detect the orientation of the
communication device 600 (north, south, west, and east, as well as
combined orientations in degrees, minutes, or other suitable
orientation metrics).
[0120] The communication device 600 can use the transceiver 602 to
also determine a proximity to a cellular, WiFi, Bluetooth.RTM., or
other wireless access points by sensing techniques such as
utilizing a received signal strength indicator (RSSI) and/or signal
time of arrival (TOA) or time of flight (TOF) measurements. The
controller 606 can utilize computing technologies such as a
microprocessor, a digital signal processor (DSP), programmable gate
arrays, application specific integrated circuits, and/or a video
processor with associated storage memory such as Flash, ROM, RAM,
SRAM, DRAM or other storage technologies for executing computer
instructions, controlling, and processing data supplied by the
aforementioned components of the communication device 600.
[0121] Other components not shown in FIG. 6 can be used in one or
more embodiments of the subject disclosure. For instance, the
communication device 600 can include a slot for adding or removing
an identity module such as a Subscriber Identity Module (SIM) card
or Universal Integrated Circuit Card (UICC). SIM or UICC cards can
be used for identifying subscriber services, executing programs,
storing subscriber data, and so on.
[0122] The terms "first," "second," "third," and so forth, as used
in the claims, unless otherwise clear by context, is for clarity
only and doesn't otherwise indicate or imply any order in time. For
instance, "a first determination," "a second determination," and "a
third determination," does not indicate or imply that the first
determination is to be made before the second determination, or
vice versa, etc.
[0123] In the subject specification, terms such as "store,"
"storage," "data store," "data storage," "database," and
substantially any other information storage component relevant to
operation and functionality of a component, refer to "memory
components," or entities embodied in a "memory" or components
comprising the memory. It will be appreciated that the memory
components described herein can be either volatile memory or
nonvolatile memory, or can comprise both volatile and nonvolatile
memory, by way of illustration, and not limitation, volatile
memory, non-volatile memory, disk storage, and memory storage.
Further, nonvolatile memory can be included in read only memory
(ROM), programmable ROM (PROM), electrically programmable ROM
(EPROM), electrically erasable ROM (EEPROM), or flash memory.
Volatile memory can comprise random access memory (RAM), which acts
as external cache memory. By way of illustration and not
limitation, RAM is available in many forms such as synchronous RAM
(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data
rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM
(SLDRAM), and direct Rambus RAM (DRRAIVI). Additionally, the
disclosed memory components of systems or methods herein are
intended to comprise, without being limited to comprising, these
and any other suitable types of memory.
[0124] Moreover, it will be noted that the disclosed subject matter
can be practiced with other computer system configurations,
comprising single-processor or multiprocessor computer systems,
mini-computing devices, mainframe computers, as well as personal
computers, hand-held computing devices (e.g., PDA, phone,
smartphone, watch, tablet computers, netbook computers, etc.),
microprocessor-based or programmable consumer or industrial
electronics, and the like. The illustrated aspects can also be
practiced in distributed computing environments where tasks are
performed by remote processing devices that are linked through a
communications network; however, some if not all aspects of the
subject disclosure can be practiced on stand-alone computers. In a
distributed computing environment, program modules can be located
in both local and remote memory storage devices.
[0125] In one or more embodiments, information regarding use of
services can be generated including services being accessed, media
consumption history, user preferences, and so forth. This
information can be obtained by various methods including user
input, detecting types of communications (e.g., video content vs.
audio content), analysis of content streams, sampling, and so
forth. The generating, obtaining and/or monitoring of this
information can be responsive to an authorization provided by the
user. In one or more embodiments, an analysis of data can be
subject to authorization from user(s) associated with the data,
such as an opt-in, an opt-out, acknowledgement requirements,
notifications, selective authorization based on types of data, and
so forth.
[0126] Some of the embodiments described herein can also employ
artificial intelligence (AI) to facilitate automating one or more
features described herein. The embodiments (e.g., in connection
with automatically identifying acquired cell sites that provide a
maximum value/benefit after addition to an existing communication
network) can employ various AI-based schemes for carrying out
various embodiments thereof. Moreover, the classifier can be
employed to determine a ranking or priority of each cell site of
the acquired network. A classifier is a function that maps an input
attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the
input belongs to a class, that is, f(x)=confidence (class). Such
classification can employ a probabilistic and/or statistical-based
analysis (e.g., factoring into the analysis utilities and costs) to
determine or infer an action that a user desires to be
automatically performed. A support vector machine (SVM) is an
example of a classifier that can be employed. The SVM operates by
finding a hypersurface in the space of possible inputs, which the
hypersurface attempts to split the triggering criteria from the
non-triggering events. Intuitively, this makes the classification
correct for testing data that is near, but not identical to
training data. Other directed and undirected model classification
approaches comprise, e.g., naive Bayes, Bayesian networks, decision
trees, neural networks, fuzzy logic models, and probabilistic
classification models providing different patterns of independence
can be employed. Classification as used herein also is inclusive of
statistical regression that is utilized to develop models of
priority.
[0127] As will be readily appreciated, one or more of the
embodiments can employ classifiers that are explicitly trained
(e.g., via a generic training data) as well as implicitly trained
(e.g., via observing UE behavior, operator preferences, historical
information, receiving extrinsic information). For example, SVMs
can be configured via a learning or training phase within a
classifier constructor and feature selection module. Thus, the
classifier(s) can be used to automatically learn and perform a
number of functions, including but not limited to determining
according to predetermined criteria which of the acquired cell
sites will benefit a maximum number of subscribers and/or which of
the acquired cell sites will add minimum value to the existing
communication network coverage, etc.
[0128] As used in some contexts in this application, in some
embodiments, the terms "component," "system" and the like are
intended to refer to, or comprise, a computer-related entity or an
entity related to an operational apparatus with one or more
specific functionalities, wherein the entity can be either
hardware, a combination of hardware and software, software, or
software in execution. As an example, a component may be, but is
not limited to being, a process running on a processor, a
processor, an object, an executable, a thread of execution,
computer-executable instructions, a program, and/or a computer. By
way of illustration and not limitation, both an application running
on a server and the server can be a component. One or more
components may reside within a process and/or thread of execution
and a component may be localized on one computer and/or distributed
between two or more computers. In addition, these components can
execute from various computer readable media having various data
structures stored thereon. The components may communicate via local
and/or remote processes such as in accordance with a signal having
one or more data packets (e.g., data from one component interacting
with another component in a local system, distributed system,
and/or across a network such as the Internet with other systems via
the signal). As another example, a component can be an apparatus
with specific functionality provided by mechanical parts operated
by electric or electronic circuitry, which is operated by a
software or firmware application executed by a processor, wherein
the processor can be internal or external to the apparatus and
executes at least a part of the software or firmware application.
As yet another example, a component can be an apparatus that
provides specific functionality through electronic components
without mechanical parts, the electronic components can comprise a
processor therein to execute software or firmware that confers at
least in part the functionality of the electronic components. While
various components have been illustrated as separate components, it
will be appreciated that multiple components can be implemented as
a single component, or a single component can be implemented as
multiple components, without departing from example
embodiments.
[0129] Further, the various embodiments can be implemented as a
method, apparatus or article of manufacture using standard
programming and/or engineering techniques to produce software,
firmware, hardware or any combination thereof to control a computer
to implement the disclosed subject matter. The term "article of
manufacture" as used herein is intended to encompass a computer
program accessible from any computer-readable device or
computer-readable storage/communications media. For example,
computer readable storage media can include, but are not limited
to, magnetic storage devices (e.g., hard disk, floppy disk,
magnetic strips), optical disks (e.g., compact disk (CD), digital
versatile disk (DVD)), smart cards, and flash memory devices (e.g.,
card, stick, key drive). Of course, those skilled in the art will
recognize many modifications can be made to this configuration
without departing from the scope or spirit of the various
embodiments.
[0130] In addition, the words "example" and "exemplary" are used
herein to mean serving as an instance or illustration. Any
embodiment or design described herein as "example" or "exemplary"
is not necessarily to be construed as preferred or advantageous
over other embodiments or designs. Rather, use of the word example
or exemplary is intended to present concepts in a concrete fashion.
As used in this application, the term "or" is intended to mean an
inclusive "or" rather than an exclusive "or". That is, unless
specified otherwise or clear from context, "X employs A or B" is
intended to mean any of the natural inclusive permutations. That
is, if X employs A; X employs B; or X employs both A and B, then "X
employs A or B" is satisfied under any of the foregoing instances.
In addition, the articles "a" and "an" as used in this application
and the appended claims should generally be construed to mean "one
or more" unless specified otherwise or clear from context to be
directed to a singular form.
[0131] Moreover, terms such as "user equipment," "mobile station,"
"mobile," "subscriber station," "access terminal," "terminal,"
"handset," "mobile device" (and/or terms representing similar
terminology) can refer to a wireless device utilized by a
subscriber or user of a wireless communication service to receive
or convey data, control, voice, video, sound, gaming or
substantially any data-stream or signaling-stream. The foregoing
terms are utilized interchangeably herein and with reference to the
related drawings.
[0132] Furthermore, the terms "user," "subscriber," "customer,"
"consumer" and the like are employed interchangeably throughout,
unless context warrants particular distinctions among the terms. It
should be appreciated that such terms can refer to human entities
or automated components supported through artificial intelligence
(e.g., a capacity to make inference based, at least, on complex
mathematical formalisms), which can provide simulated vision, sound
recognition and so forth.
[0133] As employed herein, the term "processor" can refer to
substantially any computing processing unit or device comprising,
but not limited to comprising, single-core processors;
single-processors with software multithread execution capability;
multi-core processors; multi-core processors with software
multithread execution capability; multi-core processors with
hardware multithread technology; parallel platforms; and parallel
platforms with distributed shared memory. Additionally, a processor
can refer to an integrated circuit, an application specific
integrated circuit (ASIC), a digital signal processor (DSP), a
field programmable gate array (FPGA), a programmable logic
controller (PLC), a complex programmable logic device (CPLD), a
discrete gate or transistor logic, discrete hardware components or
any combination thereof designed to perform the functions described
herein. Processors can exploit nano-scale architectures such as,
but not limited to, molecular and quantum-dot based transistors,
switches and gates, in order to optimize space usage or enhance
performance of user equipment. A processor can also be implemented
as a combination of computing processing units.
[0134] As used herein, terms such as "data storage," "data
storage," "database," and substantially any other information
storage component relevant to operation and functionality of a
component, refer to "memory components," or entities embodied in a
"memory" or components comprising the memory. It will be
appreciated that the memory components or computer-readable storage
media, described herein can be either volatile memory or
nonvolatile memory or can include both volatile and nonvolatile
memory.
[0135] What has been described above includes mere examples of
various embodiments. It is, of course, not possible to describe
every conceivable combination of components or methodologies for
purposes of describing these examples, but one of ordinary skill in
the art can recognize that many further combinations and
permutations of the present embodiments are possible. Accordingly,
the embodiments disclosed and/or claimed herein are intended to
embrace all such alterations, modifications and variations that
fall within the spirit and scope of the appended claims.
Furthermore, to the extent that the term "includes" is used in
either the detailed description or the claims, such term is
intended to be inclusive in a manner similar to the term
"comprising" as "comprising" is interpreted when employed as a
transitional word in a claim.
[0136] In addition, a flow diagram may include a "start" and/or
"continue" indication. The "start" and "continue" indications
reflect that the steps presented can optionally be incorporated in
or otherwise used in conjunction with other routines. In this
context, "start" indicates the beginning of the first step
presented and may be preceded by other activities not specifically
shown. Further, the "continue" indication reflects that the steps
presented may be performed multiple times and/or may be succeeded
by other activities not specifically shown. Further, while a flow
diagram indicates a particular ordering of steps, other orderings
are likewise possible provided that the principles of causality are
maintained.
[0137] As may also be used herein, the term(s) "operably coupled
to", "coupled to", and/or "coupling" includes direct coupling
between items and/or indirect coupling between items via one or
more intervening items. Such items and intervening items include,
but are not limited to, junctions, communication paths, components,
circuit elements, circuits, functional blocks, and/or devices. As
an example of indirect coupling, a signal conveyed from a first
item to a second item may be modified by one or more intervening
items by modifying the form, nature or format of information in a
signal, while one or more elements of the information in the signal
are nevertheless conveyed in a manner than can be recognized by the
second item. In a further example of indirect coupling, an action
in a first item can cause a reaction on the second item, as a
result of actions and/or reactions in one or more intervening
items.
[0138] Although specific embodiments have been illustrated and
described herein, it should be appreciated that any arrangement
which achieves the same or similar purpose may be substituted for
the embodiments described or shown by the subject disclosure. The
subject disclosure is intended to cover any and all adaptations or
variations of various embodiments. Combinations of the above
embodiments, and other embodiments not specifically described
herein, can be used in the subject disclosure. For instance, one or
more features from one or more embodiments can be combined with one
or more features of one or more other embodiments. In one or more
embodiments, features that are positively recited can also be
negatively recited and excluded from the embodiment with or without
replacement by another structural and/or functional feature. The
steps or functions described with respect to the embodiments of the
subject disclosure can be performed in any order. The steps or
functions described with respect to the embodiments of the subject
disclosure can be performed alone or in combination with other
steps or functions of the subject disclosure, as well as from other
embodiments or from other steps that have not been described in the
subject disclosure. Further, more than or less than all of the
features described with respect to an embodiment can also be
utilized.
* * * * *