U.S. patent application number 17/827332 was filed with the patent office on 2022-09-15 for predicting small cell capacity and coverage to facilitate offloading of macrocell capacity.
The applicant listed for this patent is AT&T Intellectual Property I, L.P.. Invention is credited to Yuning Yang, Wenjie Zhao.
Application Number | 20220295369 17/827332 |
Document ID | / |
Family ID | 1000006362571 |
Filed Date | 2022-09-15 |
United States Patent
Application |
20220295369 |
Kind Code |
A1 |
Yang; Yuning ; et
al. |
September 15, 2022 |
PREDICTING SMALL CELL CAPACITY AND COVERAGE TO FACILITATE
OFFLOADING OF MACROCELL CAPACITY
Abstract
Predicting small cell capacity and coverage to facilitate
offloading of macrocell capacity is presented herein. A system
selects a group of candidate locations for placement of respective
small cells to facilitate offloading, via the respective small
cells, of traffic from respective macrocells corresponding to the
candidate locations--the respective small cells including first
transmission powers that are less than second transmission powers
of the respective macrocells. Further, for each candidate location
of the group of candidate locations, the system determines an
estimated amount of traffic capacity of a small cell of the
respective small cells that has been presumed to have been placed
at the candidate location, and determines estimated signal
strengths of respective signals that have been predicted to have
been received from the small cell at respective portions of a grid
of a defined signal coverage area corresponding to the candidate
location.
Inventors: |
Yang; Yuning; (Basking
Ridge, NJ) ; Zhao; Wenjie; (Princeton, NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
AT&T Intellectual Property I, L.P. |
Atlanta |
GA |
US |
|
|
Family ID: |
1000006362571 |
Appl. No.: |
17/827332 |
Filed: |
May 27, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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16916998 |
Jun 30, 2020 |
11368890 |
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17827332 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04W 36/16 20130101;
H04W 36/04 20130101 |
International
Class: |
H04W 36/16 20060101
H04W036/16; H04W 36/04 20060101 H04W036/04 |
Claims
1. A system, comprising: a processor; and a memory that stores
executable instructions that, when executed by the processor,
facilitate performance of operations by the processor, comprising:
in response to a machine learning model being selected from a group
of machine learning models comprising a decision tree based
learning model, a linear regression based learning model, and a
Bayesian based learning model, selecting, using the machine
learning model, a group of candidate locations for placement of
respective small cells, wherein the respective small cells comprise
first transmission powers that are less than second transmission
powers of respective macrocells corresponding to respective
candidate locations of the group of candidate locations; and for
the respective candidate locations of the group of candidate
locations, determining, via the machine learning model, respective
estimated amounts of traffic capacity of a small cell of the
respective small cells determined to have been placed at the
respective candidate locations, wherein a number of hyperparameters
for the machine learning model has been determined based on a
defined accuracy with respect to the respective estimated amounts
of traffic capacity.
2. The system of claim 1, wherein the operations further comprise:
performing a binary search with respect to determining a number of
nodes of a decision tree corresponding to the decision tree machine
learning model.
3. The system of claim 1, wherein the operations further comprise:
updating the machine learning model using signal strength
measurements that have been obtained from user equipment that have
been communicatively coupled to the respective macrocells.
4. The system of claim 1, wherein selecting the group of candidate
locations comprises: selecting a candidate location of the group of
candidate locations based on a signal quality of a macrocell of the
respective macrocells corresponding to the candidate location.
5. The system of claim 1, wherein determining the respective
estimated amounts of traffic capacity of the small cell comprises:
determining a predicted amount of megabytes to be transmitted via a
downlink channel of the small cell over a defined period of
time.
6. The system of claim 1, wherein determining the respective
estimated amounts of traffic capacity of the small cell comprises:
determining a group of macrocell characteristics of a macrocell of
the respective macrocells corresponding to a candidate location of
the group of candidate locations; determining a group of small cell
characteristics of the small cell; determining a group of location
characteristics corresponding to the candidate location;
determining a traffic distribution of user equipment corresponding
to the candidate location; and based on the group of macrocell
characteristics, the group of small cell characteristics, the group
of location characteristics, and the traffic distribution of user
equipment, determining the respective estimated amounts of traffic
capacity of the small cell.
7. The system of claim 6, wherein the group of macrocell
characteristics comprises: at least one of a reference signal
receive power corresponding to the macrocell, a reference signal
received quality corresponding to the macrocell, or an amount of
megabytes that have been transmitted via a downlink channel of the
macrocell over a defined period of time.
8. The system of claim 6, wherein the group of small cell
characteristics comprises: at least one of an installation height
of the small cell, a transmission power of the small cell, an
antenna model of the small cell, or a period of deployment of the
small cell.
9. The system of claim 6, wherein the group of location
characteristics comprises: at least one of a line of sight
percentage of the defined signal coverage area that has been
determined to be within a line of site of the small cell, an indoor
location percentage of the defined signal coverage area that has
been determined to be located within a building or structure, or a
pedestrian traffic percentage of pedestrian traffic that has been
determined to correspond to the defined signal coverage area.
10. The system of claim 6, wherein the traffic distribution of user
equipment comprises a number of the user equipment that have been
determined to have been located in the defined signal coverage area
over a defined period of time.
11. The system of claim 1, wherein the operations further comprise:
determining a group of small characteristics of the small cell;
determining a group of location features corresponding to the small
cell and the respective portions of the defined signal coverage
area; and based on the group of small characteristics and the group
of location features, determining estimated signal strengths of
respective signals that have been predicted to have been received
from the small cell at respective portions of a grid of a defined
signal coverage area corresponding to the candidate location.
12. The system of claim 11, wherein the group of small cell
characteristics comprises: at least one of an installation height
of the small cell, a transmission power of the small cell, an
antenna tilt of the small cell, a width of transmission of a beam
from the small cell, or a frequency band of the transmission of the
beam.
13. The system of claim 11, wherein the group of location features
comprises: at least one of a distance between the small cell and a
portion of the respective portions of the defined signal coverage
area, a difference between a bearing angle of a predicted
transmission of a beam that has been predicted to have been
transmitted by the small cell from the candidate location and a
reception angle of a predicted reception of the beam at the portion
of the defined signal coverage area, or a path characteristic of a
transmission path between the small cell and the portion of the
defined signal coverage area.
14. The system of claim 1, wherein selecting the group of candidate
locations comprises: selecting the candidate location based on an
amount of traffic communicated by the macrocell over a defined
period of time.
15. The system of claim 1, wherein selecting the group of candidate
locations comprises: selecting the candidate location based on a
number of user equipment that have been determined to be
communicatively coupled to the macrocell over a defined period of
time.
16. A method, comprising: selecting, by a system comprising a
processor, a machine learning model from a group of machine
learning models comprising a decision tree based learning model, a
linear regression based learning model, and a Bayesian based
learning model; based on the machine learning model, determining,
by the system, a group of candidate locations for placement of
respective small cells comprising first respective transmission
powers that are less than second respective transmission powers of
respective macrocells; determining, by the system using the machine
learning model based on a number of hyperparameters of the machine
learning model, an estimated amount of traffic capacity of a small
cell of the respective small cells that has been determined to have
been placed at a candidate location of the group of candidate
locations; and based on a defined accuracy with respect to the
estimated amount of traffic capacity, determining, by the system,
the number of hyperparameters for the machine learning model.
17. The method of claim 16, further comprising: performing, by the
system, a binary search with respect to determining a number of
nodes of a decision tree corresponding to the decision tree based
machine learning model.
18. The method of claim 16, further comprising: updating, by the
system, the machine learning model using signal strength
measurements that have been obtained from user equipment that have
been communicatively coupled to the respective macrocells.
19. A non-transitory machine-readable medium, comprising executable
instructions that, when executed by network equipment comprising a
processor, facilitate performance of operations, comprising: in
response to selecting a machine learning model from a group of
machine learning models comprising a decision tree based learning
model, a linear regression based learning model, and a Bayesian
based learning model, determining, via the machine learning model,
candidate locations for placement of respective small cells; and
for respective candidate locations of the candidate locations,
determining, via the group of machine learning models based on a
number of hyperparameters of the machine learning models,
respective estimated amounts of traffic capacity of a small cell of
the respective small cells that has been determined to have been
placed at the respective candidate locations, wherein the number of
hyperparameters has been determined based on a defined accuracy
with respect to the respective estimated amounts of traffic
capacity of the small cell.
20. The non-transitory machine-readable medium of claim 19, wherein
determining the respective estimated amounts of traffic capacity of
the small cell comprises: determining a predicted amount of
megabytes to be transmitted via a downlink channel of the small
cell over a defined period of time.
Description
RELATED APPLICATION
[0001] The subject patent application is a continuation of, and
claims priority to, U.S. patent application Ser. No. 16/916,998,
filed Jun. 30, 2020, and entitled "PREDICTING SMALL CELL CAPACITY
AND COVERAGE TO FACILITATE OFFLOADING OF MACROCELL CAPACITY," the
entirety of which priority application is hereby incorporated by
reference herein.
TECHNICAL FIELD
[0002] The subject disclosure generally relates to embodiments for
predicting small cell capacity and coverage to facilitate
offloading of macrocell capacity.
BACKGROUND
[0003] Conventional wireless technologies have faced obstacles in
meeting the exponential growth in demand for wireless access. For
example, increasing an amount of macrocells within a city to
improve wireless coverage has been limited due to macrocell tower
cost and/or macrocell tower size restrictions. In addition, use of
existing macrocell radio frequency (RF) spectrum to accommodate the
increased demand for wireless resources is limited by "spectrum
exhaust"--a reduction in the available bandwidth of an existing
licensed RF spectrum utilized by the macrocell. Further, procuring
additional spectrum has been undoable due to the lack of
availability and/or cost of such spectrum.
[0004] Consequently, conventional cellular technologies have had
some drawbacks with respect to accommodating the demand for
wireless access, some of which are noted with reference to the
various embodiments described herein below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] Non-limiting embodiments of the subject disclosure are
described with reference to the following figures, wherein like
reference numerals refer to like parts throughout the various views
unless otherwise specified:
[0006] FIG. 1 illustrates a block diagram of a cellular network
comprising a heterogeneous network (HetNet) planning system for
predicting small cell capacity and coverage to facilitate
offloading of macrocell capacity, in accordance with various
example embodiments;
[0007] FIG. 2 illustrates a block diagram of a HetNet planning
system for predicting small cell capacity and coverage to
facilitate offloading of macrocell capacity, in accordance with
various example embodiments;
[0008] FIG. 3 illustrates a block diagram of a location selection
component of a HetNet planning system that facilitates offloading
of macrocell capacity, in accordance with various example
embodiments;
[0009] FIG. 4 illustrates a block diagram of a prediction component
of a HetNet planning system that facilitates offloading of
macrocell capacity, in accordance with various example
embodiments;
[0010] FIG. 5 illustrates a block diagram of a small cell traffic
capacity prediction component that facilitates offloading of
macrocell capacity, in accordance with various example
embodiments;
[0011] FIG. 6 illustrates a block diagram of a small cell wireless
coverage prediction component that facilitates offloading of
macrocell capacity, in accordance with various example
embodiments;
[0012] FIG. 7 illustrates a block diagram of a machine learning
component of a HetNet planning system that facilitates offloading
of macrocell capacity, in accordance with various example
embodiments;
[0013] FIG. 8 illustrates a flow chart of a method associated with
predicting small cell capacity and coverage to facilitate
offloading of macrocell capacity, in accordance with various
example embodiments;
[0014] FIG. 9 illustrates a flow chart of another method associated
with predicting small cell capacity and coverage to facilitate
offloading of macrocell capacity, in accordance with various
example embodiments;
[0015] FIG. 10 illustrates a block diagram of a wireless network
environment, in accordance various example embodiments; and
[0016] FIG. 11 is a block diagram representing an illustrative
non-limiting computing system or operating environment in which one
or more aspects of various embodiments described herein can be
implemented.
DETAILED DESCRIPTION
[0017] Aspects of the subject disclosure will now be described more
fully hereinafter with reference to the accompanying drawings in
which example embodiments are shown. In the following description,
for purposes of explanation, numerous specific details are set
forth in order to provide a thorough understanding of the various
embodiments. However, the subject disclosure may be embodied in
many different forms and should not be construed as limited to the
example embodiments set forth herein.
[0018] As described above, conventional wireless technologies have
had some drawbacks with respect to meeting the demand for wireless
access, e.g., due to macrocell size, marocell cost, the limited
availability of licensed RF spectrum, etc. Various embodiments
disclosed herein can improve wireless access by facilitating
offloading, via use of small cells, of macrocell capacity, e.g.,
within a centralized radio access network (C-RAN) environment, by
automatically predicting small cell capacity and coverage within a
defined geographic region comprising macrocell(s), e.g., city,
region, etc.
[0019] For example, in embodiment(s), a system comprises a
processor and a memory that stores executable instructions that,
when executed by the processor, facilitate performance of
operations by the processor, comprising: selecting a group of
candidate locations for placement of respective small cells to
facilitate offloading, via the respective small cells, of traffic
from respective macrocells corresponding to the candidate
locations, wherein the respective small cells comprise first
transmission powers that are less than second transmission powers
of the respective macrocells; and for each candidate location of
the group of candidate locations, determining an estimated amount
of traffic capacity of a small cell of the respective small cells
that has been presumed to have been placed at the candidate
location, and determining estimated signal strengths of respective
signals that have been predicted to have been received from the
small cell at respective portions of a grid of a defined signal
coverage area corresponding to the candidate location.
[0020] In an embodiment, the operations further comprise: utilizing
at least one machine learning model to perform the selecting of the
group of candidate locations, the determining of the estimated
amount of traffic capacity, and the determining of the estimated
signal strengths.
[0021] In one embodiment, the utilizing comprises: selecting the
machine learning model from a group of machine learning models, the
group comprising a decision tree based learning model, a linear
regression based learning model, and a Bayesian based learning
model.
[0022] In an embodiment, the operations further comprise:
determining a number of hyperparameters for the machine learning
model based on a defined accuracy with respect to at least one of
the estimated amount of traffic capacity or the estimated signal
strengths.
[0023] In another embodiment, the determining of the number of
hyperparameters comprises: performing a binary search with respect
to determining a number of nodes of a decision tree corresponding
to the machine learning model.
[0024] In yet another embodiment, the operations further comprise:
updating the machine learning model using signal strength
measurements that have been obtained from user equipment that have
been communicatively coupled to the respective macrocells.
[0025] In an embodiment, the selecting of the group of candidate
locations comprises: selecting the candidate location based on at
least one of: a signal quality of a macrocell of the respective
macrocells corresponding to the candidate location, an amount of
the traffic communicated by the macrocell over a defined period of
time, or a number of user equipment that have been determined to be
communicatively coupled to the macrocell over a defined time
period.
[0026] In one embodiment, the determining of the estimated amount
of traffic capacity of the small cell comprises: determining a
predicted amount of megabytes to be transmitted via a downlink
channel of the small cell over a defined period of time.
[0027] In another embodiment, the determining of the estimated
amount of traffic capacity of the small cell comprises: determining
a group of macrocell characteristics of a macrocell of the
respective macrocells corresponding to the candidate location;
determining a group of small cell characteristics of the small
cell; determining a group of location characteristics corresponding
to the candidate location; determining a traffic distribution of
user equipment corresponding to the candidate location; and based
on the group of macrocell characteristics, the group of small cell
characteristics, the group of environmental characteristics, and
the traffic distribution of user equipment, determining the
estimated amount of traffic capacity of the small cell.
[0028] In yet another embodiment, the group of macrocell
characteristics comprises: at least one of a reference signal
receive power corresponding to the macrocell, a reference signal
received quality corresponding to the macrocell, or an amount of
megabytes that have been transmitted via a downlink channel of the
macrocell over a defined period of time.
[0029] In an embodiment, the group of small cell characteristics
comprises: at least one of an installation height of the small
cell, a transmission power of the small cell, an antenna model of
the small cell, or a period of deployment of the small cell.
[0030] In one embodiment, the group of location characteristics
comprises: at least one of a line of sight percentage of the
defined signal coverage area that has been determined to be within
a line of site of the small cell, an indoor location percentage of
the defined signal coverage area that has been determined to be
located within a building or structure, or a pedestrian traffic
percentage of pedestrian traffic that has been determined to
correspond to the defined signal coverage area.
[0031] In another embodiment, the traffic distribution of user
equipment comprises a number of the user equipment that have been
determined to have been located in the defined signal coverage area
over a defined period of time.
[0032] In yet another embodiment, the determining the estimated
signal strengths comprises: determining a group of small
characteristics of the small cell; determining a group of location
features corresponding to the small cell and the respective
portions of the defined signal coverage area; and based on the
group of small characteristics and the group of location features,
determining the estimated signal strength.
[0033] In an embodiment, the group of small cell characteristics
comprises: at least one of an installation height of the small
cell, a transmission power of the small cell, an antenna tilt of
the small cell, a width of transmission of a beam from the small
cell, or a frequency band of the transmission of the beam.
[0034] In another embodiment, the group of location features
comprises: at least one of a distance between the small cell and a
portion of the respective portions of the defined signal coverage
area, a difference between a bearing angle of a predicted
transmission of a beam that has been predicted to have been
transmitted by the small cell from the candidate location and a
reception angle of a predicted reception of the beam at the portion
of the defined signal coverage area, or a path characteristic of a
transmission path between the small cell and the portion of the
defined signal coverage area.
[0035] In an embodiment, a method comprises: assigning, by a system
comprising a processor, wireless coverage regions to respective
candidate locations to facilitate offloading, via projected
placement of small cells at the respective locations, of wireless
capacity of macrocells corresponding to the wireless coverage
regions; dividing, by the system, the respective wireless coverage
regions into respective groups of wireless coverage areas; and
predicting, by the system, respective wireless bandwidth capacities
of the small cells and received signal strengths of respective
beams that have been projected to have been received, from the
small cells, at the wireless coverage areas.
[0036] In one embodiment, predicting the respective wireless
bandwidth capacities comprises predicting the respective wireless
bandwidth capacities of the small cells based on a group of
macrocell characteristics of the macrocells, a first group of small
cell characteristics of the small cells, a first group of location
characteristics corresponding to the respective candidate
locations, and traffic distributions of respective mobile devices
corresponding to the respective candidate locations, and wherein
predicting the received signal strengths comprises predicting the
received signal strengths of the respective beams at the wireless
coverage areas based on a second group of small cell
characteristics of the small cells and a second group of location
characteristics corresponding to the small cells and the respective
groups of wireless coverage areas.
[0037] In another embodiment, a non-transitory machine-readable
medium, comprising executable instructions that, when executed by
network equipment comprising a processor, facilitate performance of
operations, comprising: determining a group of respective candidate
locations for placement of respective small cells to facilitate
respective reductions of traffic of macrocells corresponding to the
respective candidate locations; and based on the respective
candidate locations, determining traffic volumes of the respective
small cells and radio propagation characteristics of respective
beams that have been projected to have been received, from the
respective small cells, at portions of respective grids of coverage
areas corresponding to the respective candidate locations.
[0038] In yet another embodiment, determining the traffic volumes
is based on respective macrocell characteristics of the macrocells,
first small cell characteristics of the respective small cells,
first location features corresponding to the respective candidate
locations, and traffic distributions of respective mobile devices
corresponding to the respective candidate locations, and wherein
determining the radio propagation characteristics is based on
second small cell characteristics of the respective small cells and
second location features corresponding to the portions of the
respective grids of coverage areas.
[0039] Reference throughout this specification to "one embodiment,"
"an embodiment," etc. means that a particular feature, structure,
or characteristic described in connection with the embodiment is
included in at least one embodiment. Thus, the appearances of the
phrase "in one embodiment," "in an embodiment," etc. in various
places throughout this specification are not necessarily all
referring to the same embodiment. Furthermore, the particular
features, structures, or characteristics may be combined in any
suitable manner in one or more embodiments.
[0040] As described above, conventional wireless technologies have
faced obstacles in meeting the exponential growth in demand for
wireless access. In this regard, further deployment of macrocells
within cities to meet such demand has been limited due to macrocell
tower cost, antenna size restrictions, weight restrictions, etc.,
e.g., corresponding to rooftop placement on city buildings.
Further, use of existing macrocell RF spectrum to accommodate for
such growth has been limited by spectrum exhaust, and availability
of additional spectrum is cost prohibitive or nonexistant.
[0041] Now referring to FIGS. 1-4, a block diagram of a cellular
network comprising a HetNet planning system (110) for predicting
small cell capacity and coverage to facilitate offloading of
macrocell capacity, and block diagrams of the HetNet planning
system, a location selection component (210) of the HetNet planning
system, and a prediction component (220) of the HetNet planning
system are illustrated, respectively, in accordance with various
example embodiments.
[0042] As illustrated by FIG. 3, the location selection component
comprises a candidate location selection component (310) and a
coverage area partition component (320). The candidate location
selection component selects a group of candidate locations for
placement of respective small cells (e.g., 108.sub.A, 108.sub.B,
108.sub.C) (e.g., c-RAN based cell, femtocell, picocell) within a
wireless coverage area (104) of a macrocell (102) (e.g., a base
station device, wireless access point (AP), eNodeB (eNB)) to
facilitate offloading, via the respective small cells, of traffic
from the macrocell--the macrocell having an antenna footprint that
is larger than an antenna footprint of a small cell, and the
macrocell having a transmission power that is order(s) of magnitude
greater than a transmission power of the small cell.
[0043] In embodiment(s), the candidate location selection component
selects a candidate location of the group of candidate locations
based on macrocell features (202) of the macrocell including a
signal quality of the macrocell, an amount of traffic communicated
by the macrocell over a defined period of time, e.g., month, a
number of user equipment (e.g., 101.sub.A, 101.sub.B, 101.sub.C)
that have been determined to be communicatively coupled to the
macrocell over the defined time period, etc.
[0044] In other embodiment(s), the coverage area partition
component divides a wireless coverage area (e.g., 104) of the
macrocell into wireless coverage regions, defined signal coverage
areas, etc. (e.g., 106.sub.A, 106.sub.B, 106.sub.C) corresponding
to the group of candidate locations. In turn, the coverage area
partition component further divides the wireless coverage regions,
etc. into respective grids (not shown), e.g., dividing a wireless
coverage region into a grid having portions of coverage areas.
[0045] As illustrated by FIG. 4, the prediction component comprises
a small cell traffic capacity prediction component (410) and a
small cell wireless coverage prediction component (420). The small
cell traffic capacity prediction component determines, for each
candidate location of the group of candidate locations, an
estimated amount of traffic capacity of a small cell of the
respective small cells that has been presumed to have been placed
at the candidate location. Further, the small cell wireless
coverage prediction component determines estimated signal strengths
of respective wireless signals that have been predicted to have
been received from the small cell at respective portions of a grid
(not shown) of a wireless coverage region (e.g., 106.sub.A,
106.sub.B, 106.sub.C) corresponding to the candidate location.
[0046] Referring now to FIG. 5, in embodiment(s), the small cell
traffic capacity prediction component comprises a macrocell
characteristic component (510), a small cell characteristic
component (520), a candidate location characteristic component
(530), and a user equipment traffic distribution characteristic
component (540). The macrocell characteristic component can
receive, determine, obtain, etc. a group of macrocell
characteristics, features, etc. (202) of a macrocell of the
respective macrocells corresponding to the candidate location. In
embodiment(s), the group of macrocell characteristics comprises a
reference signal receive power (RSRP) corresponding to the
macrocell, a reference signal received quality (RSRQ) corresponding
to the macrocell, and/or an amount of megabytes that have been
transmitted via a downlink channel of the macrocell over a defined
period of time.
[0047] The small cell characteristic component can determine a
predicted amount of megabytes to be transmitted via a downlink
channel of the small cell over a defined period of time. In
embodiment(s), the small cell characteristic component can receive,
determine, obtain, etc. a group of small cell characteristics,
features, etc. (204) of the small cell. In embodiment(s), the group
of small cell characteristics comprises an installation height of
the small cell, a transmission power of the small cell, an antenna
model of the small cell, and/or a period of deployment of the small
cell.
[0048] The candidate location characteristic component can receive,
determine, obtain, etc. a group of location, environmental, etc.
characteristics, features, etc. (206) corresponding to the
candidate location. In embodiment(s), the group of location
characteristics comprises a line of sight percentage of the defined
signal coverage area that has been determined to be within a line
of site of the small cell, an indoor location percentage of the
defined signal coverage area that has been determined to be located
within a building or structure, and/or a pedestrian traffic
percentage of pedestrian traffic that has been determined to
correspond to the defined signal coverage area.
[0049] The user equipment traffic distribution characteristic
component can receive, determine, obtain, etc. a traffic
distribution (208) of user equipment corresponding to the candidate
location. In embodiment(s), the traffic distribution of user
equipment comprises a number of the user equipment that have been
determined to have been located in the wireless coverage region
over a defined period of time, e.g., month.
[0050] In turn, based on the group of macrocell characteristics,
the group of small cell characteristics, the group of location
characteristics, and the traffic distribution of user equipment,
the small cell traffic capacity prediction component can determine
the estimated amount of traffic capacity of the small cell.
[0051] Now referring to FIG. 6, in embodiment(s), the small cell
wireless coverage prediction component comprises a small cell
characteristic component (610) and a candidate location
characteristic component (620). The small cell characteristic
component can determine a group of small characteristics of the
small cell. In embodiment(s), the group of small cell
characteristics includes an installation height of the small cell,
a transmission power of the small cell, an antenna tilt of the
small cell, a width of transmission of a beam from the small cell,
and/or a frequency band of the transmission of the beam.
[0052] The candidate location characteristic component can
determine a group of location features corresponding to the small
cell and the respective portions of the grid of the wireless
coverage region. In embodiment(s), the group of location features
includes a distance between the small cell and a portion of the
respective portions of the defined signal coverage area, a
difference between a bearing angle of a predicted transmission of a
beam that has been predicted to have been transmitted by the small
cell from the candidate location and a reception angle of a
predicted reception of the beam at the portion of the defined
signal coverage area, and/or a path characteristic of a
transmission path between the small cell and the portion of the
defined signal coverage area.
[0053] In turn, based on the group of small characteristics and the
group of location features, the small cell wireless coverage
prediction component can determine the estimated signal strengths
of the respective signals that have been predicted to have been
received from the small cell at the respective portions of the grid
of the wireless coverage region.
[0054] Returning now to FIG. 2, the HetNet planning system can
include a machine learning component (230) to facilitate, utilizing
machine learning model(s), selection of the group of candidate
locations, determination of the estimated amount of traffic
capacity of the small cell, and/or determination of the estimated
signal strengths of the respective signals that have been predicted
to have been received from the small cell at the respective
portions of the grid of the wireless coverage region.
[0055] In this regard, and now referring to FIG. 7, in
embodiment(s), the machine learning component can comprise a group
of machine learning models (710) including a decision tree based
learning model, a linear regression based learning model, and a
Bayesian based learning model.
[0056] Further, the machine learning component can include a binary
hyperparameter optimization component (730) that can determine,
based on a defined accuracy with respect the estimated amount of
traffic capacity of the small call and/or the estimated signal
strengths of the respective signals that have been predicted to
have been received from the small cell at the respective portions
of the grid of the wireless coverage region, a number of
hyperparameters, e.g., a number of layers, nodes, etc. of a
decision tree, for a machine learning model of the group of machine
learning models.
[0057] In embodiment(s), the binary hyperparameter optimization
component can determine the number of hyperparameters by performing
a binary search with respect to determining the number of layers,
nodes, etc. of the decision tree, e.g., iteratively narrowing down
the number of layers, nodes, etc. of the decision tree until the
estimated amount of traffic capacity and/or the estimated signal
strengths has been determined to be within the defined
accuracy.
[0058] In other embodiment(s), an input data component (720) of the
machine learning component can update the machine learning model
using signal strength measurements, e.g., RSRP, RSRQ, etc. that
have been obtained from user equipment (e.g., 101.sub.A, 101.sub.B,
101.sub.C) communicatively coupled to the respective
macrocells.
[0059] FIGS. 8-9 illustrate methodologies in accordance with the
disclosed subject matter. For simplicity of explanation, the
methodologies are depicted and described as a series of acts. It is
to be understood and appreciated that various embodiments disclosed
herein are not limited by the acts illustrated and/or by the order
of acts. For example, acts can occur in various orders and/or
concurrently, and with other acts not presented or described
herein. Furthermore, not all illustrated acts may be required to
implement the methodologies in accordance with the disclosed
subject matter. In addition, those skilled in the art will
understand and appreciate that the methodologies could
alternatively be represented as a series of interrelated states via
a state diagram or events. Additionally, it should be further
appreciated that the methodologies disclosed hereinafter and
throughout this specification are capable of being stored on an
article of manufacture to facilitate transporting and transferring
such methodologies to computers. The term article of manufacture,
as used herein, is intended to encompass a computer program
accessible from any computer-readable device, carrier, or
media.
[0060] Referring now to FIG. 8, a flowchart of a method (800)
associated with predicting small cell capacity and coverage to
facilitate offloading of macrocell capacity, is illustrated, in
accordance with various example embodiments. At 810, a system (110)
assigns wireless coverage regions to respective candidate locations
to facilitate offloading, via projected placement of small cells at
the respective locations, of wireless capacity of macrocells
corresponding to the wireless coverage regions
[0061] At 820, the system divides, e.g., via a grid, the respective
wireless coverage regions into respective groups of wireless
coverage areas. At 830, the system predicts respective wireless
bandwidth capacities of the small cells and received signal
strengths of respective beams that have been projected to have been
received, from the small cells, at the wireless coverage areas.
[0062] FIG. 9 illustrates a flowchart of a method (900) associated
with predicting, utilizing machine learning model(s), small cell
capacity and coverage to facilitate offloading of macrocell
capacity, in accordance with various example embodiments. At 910, a
system (110), selects a machine learning model from a group of
machine learning models comprising a decision tree based learning
model, a linear regression based learning model, and a Bayesian
based learning model.
[0063] At 920, the system updates the machine learning model using
signal strength measurements, e.g., RSRP, RSRQ, etc. that have been
obtained from user equipment that have been communicatively coupled
to the macrocells.
[0064] At 930, the system determines a number of hyperparameters
for the machine learning model based on a defined accuracy with
respect to the respective wireless bandwidth capacities of the
small cells and/or the received signal strengths of the respective
beams that have been projected to have been received, from the
small cells, at the wireless coverage areas.
[0065] At 940, based on the number of hyperparameters, the system
utilizes the machine learning model to predict the respective
wireless bandwidth capacities of the small cells and the received
signal strengths of respective beams that have been projected to
have been received, from the small cells, at the wireless coverage
areas.
[0066] FIG. 10 illustrates a wireless communication environment
10000 including macro network platform 1010 is illustrated, in
accordance with various embodiments. Macro network platform 1010
serves or facilitates communication with a device (e.g., 101.sub.A,
101.sub.B, 101.sub.C) via a cellular network (100). It should be
appreciated that in cellular wireless technologies, e.g., 3GPP
UMTS, high speed packet access (HSPA), 3GPP LTE, third generation
partnership project 2 (3GPP2), ultra-mobile broadband (UMB), LTE-A,
5G, etc. that can be associated with the cellular system, macro
network platform 1010 can be embodied in a core network. It is
noted that the cellular system can include base station(s), base
transceiver station(s), access point(s), macrocells, small cell(s),
(e.g., 102, 108.sub.A, 108.sub.B, 108.sub.C) and associated
electronic circuitry and deployment site(s), in addition to a
wireless radio link (120) operated in accordance with the base
station(s), etc. Accordingly, the cellular system can comprise
various coverage cells, or wireless coverage areas. In addition, it
should be appreciated that elements and/or components, e.g., of the
cellular system, can be located/included within one or more
components/elements, e.g., hardware, software, etc., of wireless
communication environment 1000, e.g., macro network platform 1010,
etc.
[0067] Generally, macro network platform 1010 includes components,
e.g., nodes, GWs, interfaces, servers, platforms, etc. that
facilitate both packet-switched (PS), e.g., IP, frame relay,
asynchronous transfer mode (ATM), and circuit-switched (CS)
traffic, e.g., voice and data, and control generation for networked
wireless communication. In various embodiments, macro network
platform 1010 includes CS gateway (GW) node(s) 1012 that can
interface CS traffic received from legacy networks like telephony
network(s) 1040, e.g., public switched telephone network (PSTN),
public land mobile network (PLMN), Signaling System No. 7 (SS7)
network 1060, etc. CS GW node(s) 1012 can authorize and
authenticate traffic, e.g., voice, arising from such networks.
Additionally, CS GW node(s) 1012 can access mobility or roaming
data generated through SS7 network 1060; for instance, mobility
data stored in a visitor location register (VLR), which can reside
in memory 1030. Moreover, CS GW node(s) 1012 interfaces CS-based
traffic and signaling with PS GW node(s) 1018. As an example, in a
3GPP UMTS network, PS GW node(s) 1018 can be embodied in GW general
packet radio service (GPRS) support node(s) (GGSN).
[0068] As illustrated by FIG. 10, PS GW node(s) 1018 can receive
and process CS-switched traffic and signaling via CS GW node(s)
1012. Further PS GW node(s) 1018 can authorize and authenticate
PS-based data sessions, e.g., via the cellular system, with served
devices, communication devices, etc. Such data sessions can include
traffic exchange with networks external to macro network platform
1010, like wide area network(s) (WANs) 1050; enterprise networks
(NWs) 1070, e.g., E911, service NW(s) 1080, e.g., an IP multimedia
subsystem (IMS), etc. It should be appreciated that local area
network(s) (LANs), which may be a part of enterprise NW(s) 1070,
can also be interfaced with macro network platform 1010 through PS
GW node(s) 1018. PS GW node(s) 1018 can generate packet data
contexts when a data session is established, e.g., associated with
an EPS bearer context activation. To that end, in an aspect, PS GW
node(s) 1018 can include a tunnel interface, e.g., tunnel
termination GW (TTG) in 3GPP UMTS network(s) (not shown), which can
facilitate packetized communication with disparate wireless
network(s), such as Wi-Fi networks. It should be further
appreciated that the packetized communication can include multiple
flows that can be generated through server(s) 1014. It is to be
noted that in 3GPP UMTS network(s), PS GW node(s) 1018 (e.g., GGSN)
and tunnel interface (e.g., TTG) comprise a packet data GW
(PDG).
[0069] Macro network platform 1010 also includes serving node(s)
1016 that can convey the various packetized flows of information,
or data streams, received through PS GW node(s) 1018. As an
example, in a 3GPP UMTS network, serving node(s) can be embodied in
serving GPRS support node(s) (SGSN).
[0070] As indicated above, server(s) 1014 in macro network platform
1010 can execute numerous applications, e.g., messaging, location
services, wireless device management, etc. that can generate
multiple disparate packetized data streams or flows; and can manage
such flows, e.g., schedule, queue, format. Such application(s), for
example can include add-on features to standard services provided
by macro network platform 1010. Data streams can be conveyed to PS
GW node(s) 1018 for authorization/authentication and initiation of
a data session, and to serving node(s) 1016 for communication
thereafter. Server(s) 1014 can also effect security, e.g.,
implement one or more firewalls, of macro network platform 1010 to
ensure network's operation and data integrity in addition to
authorization and authentication procedures that CS GW node(s) 1012
and PS GW node(s) 1018 can enact. Moreover, server(s) 1014 can
provision services from external network(s), e.g., WAN 1050, or
global positioning system (GPS) network(s), which can be a part of
enterprise NW(s) 1080. It is to be noted that server(s) 1014 can
include one or more processors configured to confer at least in
part the functionality of macro network platform 1010. To that end,
the one or more processors can execute code instructions stored in
memory 1030, for example.
[0071] In wireless communication environment 1000, memory 1030 can
store information related to operation of macro network platform
1010, e.g., related to operation of a base station device, etc. The
information can include data, business data, etc. associated with
subscribers of respective services; market plans and strategies,
e.g., promotional campaigns, business partnerships, mobile devices
served through macro network platform, etc.; service and privacy
information, policies, etc.; end-user service logs for law
enforcement; term(s) and/or condition(s) associated with wireless
service(s) provided via the cellular system; and so forth. Memory
1030 can also store information from at least one of telephony
network(s) 1040, WAN 1050, SS7 network 1060, enterprise NW(s) 1070,
or service NW(s) 1080.
[0072] In one or more embodiments, components of wireless
communication environment 1000 can provide communication services
to the device utilizing an over-the-air wireless link (e.g., 120)
via the cellular system. In this regard, the cellular system can
include one or more: macro, Femto, or pico access points (APs) (not
shown); base stations (BS); landline networks (e.g., optical
landline networks, electrical landline networks) (not shown)
communicatively coupled between the device and macro network
platform 1010, etc.
[0073] Wireless communication environment 1000 can include one or
more of the Internet (or another communication network (e.g.,
IP-based network)), or DSL-type or broadband network facilitated by
Ethernet or other technology. In various embodiments, wireless
communication environment 1000 can include hardware and/or software
for allocating resources to the device and the cellular system,
converting or enforcing protocols, establishing and/or providing
levels of quality of service (QoS), providing applications or
services, translating signals, and/or performing other desired
functions to facilitate system interoperability and communication
to/from the device and the cellular system.
[0074] In other embodiment(s), wireless communication environment
1000 can include data store component(s), a memory configured to
store information, computer-readable storage media storing
computer-executable instructions, e.g., memory 250, memory 1030,
etc. enabling various operations performed via the wireless
communication environment as described herein.
[0075] As it employed in the subject specification, the term
"processor", "processing component", etc. 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 and/or
processes 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 mobile devices. A
processor may also be implemented as a combination of computing
processing units.
[0076] In the subject specification, terms such as "data store,"
"memory storage," "memory component", "memory", and substantially
any other information storage component relevant to operation and
functionality of a component and/or process, 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 include both volatile and nonvolatile
memory.
[0077] By way of illustration, and not limitation, nonvolatile
memory, for example, can be included in memory 250, memory 1030,
system memory 1106 (see below), external storage 1116 (see below),
and/or memory storage 1152 (see below). 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 (e.g., 1112) can include
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 (DRRAM). 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.
[0078] In order to provide additional context for various
embodiments described herein, FIG. 11 and the following discussion
are intended to provide a brief, general description of a suitable
computing environment 11000 in which the various embodiments of the
embodiment described herein can be implemented. While the
embodiments have been described above in the general context of
computer-executable instructions that can run on one or more
computers, those skilled in the art will recognize that the
embodiments can be also implemented in combination with other
program modules and/or as a combination of hardware and
software.
[0079] Generally, program modules include 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 in various embodiments, methods
disclosed herein can be practiced with other computer system
configurations, including single-processor or multiprocessor
computer systems, minicomputers, mainframe computers, Internet of
Things (IoT) devices, distributed computing systems, 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.
[0080] 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.
[0081] Computing devices typically include a variety of media,
which can include computer-readable storage media, machine-readable
storage media, and/or communications media, which two terms are
used herein differently from one another as follows.
Computer-readable storage media or machine-readable storage media
can be any available storage media that can be accessed by the
computer and includes both volatile and nonvolatile media,
removable and non-removable media. By way of example, and not
limitation, computer-readable storage media or machine-readable
storage media can be implemented in connection with any method or
technology for storage of information such as computer-readable or
machine-readable instructions, program modules, structured data or
unstructured data.
[0082] Computer-readable storage media can include, 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), Blu-ray disc (BD) or other
optical disk storage, magnetic cassettes, magnetic tape, magnetic
disk storage or other magnetic storage devices, solid state drives
or other solid state 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.
[0083] 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.
[0084] 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
includes 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 include wired media, such
as a wired network or direct-wired connection, and wireless media
such as acoustic, RF, infrared and other wireless media.
[0085] With reference again to FIG. 11, the example environment
1100 for implementing various embodiments of the aspects described
herein includes a computer 1102, the computer 1102 including a
processing unit 1104, a system memory 1106 and a system bus 1108.
The system bus 1108 couples system components including, but not
limited to, the system memory 1106 to the processing unit 1104. The
processing unit 1104 can be any of various commercially available
processors. Dual microprocessors and other multi-processor
architectures can also be employed as the processing unit 1104.
[0086] The system bus 1108 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 1106 includes ROM 1110 and RAM 1112. 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 1102,
such as during startup. The RAM 1112 can also include a high-speed
RAM such as static RAM for caching data.
[0087] The computer 1102 further includes an internal hard disk
drive (HDD) 1114 (e.g., EIDE, SATA), one or more external storage
devices 1116 (e.g., a magnetic floppy disk drive (FDD) 1116, a
memory stick or flash drive reader, a memory card reader, etc.) and
an optical disk drive 1120 (e.g., which can read or write from a
CD-ROM disc, a DVD, a BD, etc.). While the internal HDD 1114 is
illustrated as located within the computer 1102, the internal HDD
1114 can also be configured for external use in a suitable chassis
(not shown). Additionally, while not shown in environment 1100, a
solid state drive (SSD) could be used in addition to, or in place
of, an HDD 1114. The HDD 1114, external storage device(s) 1116 and
optical disk drive 1120 can be connected to the system bus 1108 by
an HDD interface 1124, an external storage interface 1126 and an
optical drive interface 1128, respectively. The interface 1124 for
external drive implementations can include 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.
[0088] 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
1102, 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 respective types of
storage devices, it should be appreciated by those skilled in the
art that other types of storage media which are readable by a
computer, whether presently existing or developed in the future,
could 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.
[0089] A number of program modules can be stored in the drives and
RAM 1112, including an operating system 1130, one or more
application programs 1132, other program modules 1134 and program
data 1136. All or portions of the operating system, applications,
modules, and/or data can also be cached in the RAM 1112. The
systems and methods described herein can be implemented utilizing
various commercially available operating systems or combinations of
operating systems.
[0090] Computer 1102 can optionally comprise emulation
technologies. For example, a hypervisor (not shown) or other
intermediary can emulate a hardware environment for operating
system 1130, and the emulated hardware can optionally be different
from the hardware illustrated in FIG. 11. In such an embodiment,
operating system 1130 can comprise one virtual machine (VM) of
multiple VMs hosted at computer 1102. Furthermore, operating system
1130 can provide runtime environments, such as the Java runtime
environment or the .NET framework, for applications 1132. Runtime
environments are consistent execution environments that allow
applications 1132 to run on any operating system that includes the
runtime environment. Similarly, operating system 1130 can support
containers, and applications 1132 can be in the form of containers,
which are lightweight, standalone, executable packages of software
that include, e.g., code, runtime, system tools, system libraries
and settings for an application.
[0091] Further, computer 1102 can be enabled with a security
module, such as a trusted processing module (TPM). For instance,
with a TPM, boot components hash next in time boot components, and
wait for a match of results to secured values, before loading a
next boot component. This process can take place at any layer in
the code execution stack of computer 1102, e.g., applied at the
application execution level or at the operating system (OS) kernel
level, thereby enabling security at any level of code
execution.
[0092] A user can enter commands and information into the computer
1102 through one or more wired/wireless input devices, e.g., a
keyboard 1138, a touch screen 1140, and a pointing device, such as
a mouse 1142. Other input devices (not shown) can include a
microphone, an infrared (IR) remote control, a radio frequency (RF)
remote control, or other remote control, a joystick, a virtual
reality controller and/or virtual reality headset, a game pad, a
stylus pen, an image input device, e.g., camera(s), a gesture
sensor input device, a vision movement sensor input device, an
emotion or facial detection device, a biometric input device, e.g.,
fingerprint or iris scanner, or the like. These and other input
devices are often connected to the processing unit 1104 through an
input device interface 1144 that can be coupled to the system bus
1108, but can be connected by other interfaces, such as a parallel
port, an IEEE 1394 serial port, a game port, a USB port, an IR
interface, a BLUETOOTH.RTM. interface, etc.
[0093] A monitor 1146 or other type of display device can be also
connected to the system bus 1108 via an interface, such as a video
adapter 1148. In addition to the monitor 1146, a computer typically
includes other peripheral output devices (not shown), such as
speakers, printers, etc.
[0094] The computer 1102 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) 1150.
The remote computer(s) 1150 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 includes many or all of
the elements described relative to the computer 1102, although, for
purposes of brevity, only a memory/storage device 1152 is
illustrated. The logical connections depicted include
wired/wireless connectivity to a local area network (LAN) 1154
and/or larger networks, e.g., a wide area network (WAN) 1156. 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.
[0095] When used in a LAN networking environment, the computer 1102
can be connected to the local network 1154 through a wired and/or
wireless communication network interface or adapter 1158. The
adapter 1158 can facilitate wired or wireless communication to the
LAN 1154, which can also include a wireless access point (AP)
disposed thereon for communicating with the adapter 1158 in a
wireless mode.
[0096] When used in a WAN networking environment, the computer 1102
can include a modem 1160 or can be connected to a communications
server on the WAN 1156 via other means for establishing
communications over the WAN 1156, such as by way of the Internet.
The modem 1160, which can be internal or external and a wired or
wireless device, can be connected to the system bus 1108 via the
input device interface 1144. In a networked environment, program
modules depicted relative to the computer 1102 or portions thereof,
can be stored in the remote memory/storage device 1152. 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.
[0097] When used in either a LAN or WAN networking environment, the
computer 1102 can access cloud storage systems or other
network-based storage systems in addition to, or in place of,
external storage devices 1116 as described above. Generally, a
connection between the computer 1102 and a cloud storage system can
be established over a LAN 1154 or WAN 1156 e.g., by the adapter
1158 or modem 1160, respectively. Upon connecting the computer 1102
to an associated cloud storage system, the external storage
interface 1126 can, with the aid of the adapter 1158 and/or modem
1160, manage storage provided by the cloud storage system as it
would other types of external storage. For instance, the external
storage interface 1126 can be configured to provide access to cloud
storage sources as if those sources were physically connected to
the computer 1102.
[0098] The computer 1102 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, store shelf, etc.), and
telephone. This can include 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.
[0099] Wi-Fi allows connection to the Internet from a desired
location (e.g., a vehicle, couch at home, a bed in a hotel room, or
a conference room at work, etc.) without wires. Wi-Fi is a wireless
technology similar to that used in a cell phone that enables such
devices, e.g., mobile phones, computers, etc., 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,
etc.) to provide secure, reliable, fast wireless connectivity. A
Wi-Fi network can be used to connect devices (e.g., mobile phones,
computers, etc.) to each other, to the Internet, and to wired
networks (which use IEEE 802.3 or Ethernet). Wi-Fi networks operate
in the unlicensed 2.4 and 5 GHz radio bands, at an 11 Mbps
(802.11a) or 54 Mbps (802.11b) data rate, for example, or with
products that contain both bands (dual band), so the networks can
provide real-world performance similar to the basic 10BaseT wired
Ethernet networks used in many offices.
[0100] As utilized herein, terms "component," "system," "server,"
and the like are intended to refer to a computer-related entity,
hardware, software (e.g., in execution), and/or firmware. For
example, a component can be a processor, a process running on a
processor, an object, an executable, a program, a storage device,
and/or a computer. By way of illustration, an application running
on a server and the server can be a component. One or more
components can reside within a process, and a component can be
localized on one computer and/or distributed between two or more
computers.
[0101] Aspects of systems, apparatus, and processes explained
herein can constitute machine-executable instructions embodied
within a machine, e.g., embodied in a computer readable medium (or
media) associated with the machine. Such instructions, when
executed by the machine, can cause the machine to perform the
operations described. Additionally, systems, processes, process
blocks, etc. can be embodied within hardware, such as an
application specific integrated circuit (ASIC) or the like.
Moreover, the order in which some or all of the process blocks
appear in each process should not be deemed limiting. Rather, it
should be understood by a person of ordinary skill in the art
having the benefit of the instant disclosure that some of the
process blocks can be executed in a variety of orders not
illustrated.
[0102] Further, components can execute from various computer
readable media having various data structures stored thereon. The
components can 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,
e.g., the Internet, with other systems via the signal).
[0103] As another example, a component can be an apparatus with
specific functionality provided by mechanical parts operated by
electric or electronic circuitry; the electric or electronic
circuitry can be operated by a software application or a firmware
application executed by one or more processors; the one or more
processors can be internal or external to the apparatus and can
execute 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 include one or more
processors therein to execute software and/or firmware that
confer(s), at least in part, the functionality of the electronic
components.
[0104] Further, aspects, features, and/or advantages of the
disclosed subject matter can be exploited in substantially any
wireless telecommunication or radio technology, e.g., IEEE 802.XX
technology, e.g., Wi-Fi, Bluetooth, etc.; WiMAX; enhanced GPRS;
3GPP LTE; 3GPP2; UMB; 3GPP UMTS; HSPA; high speed downlink packet
access (HSDPA); high speed uplink packet access (HSUPA); LTE-A,
GSM, NFC, Wibree, Zigbee, satellite, Wi-Fi Direct, etc.
[0105] Further, selections of a radio technology, or radio access
technology, can include second generation (2G), third generation
(3G), fourth generation (4G), fifth generation (5G), x.sup.th
generation, etc. evolution of the radio access technology; however,
such selections are not intended as a limitation of the disclosed
subject matter and related aspects thereof. Further, aspects,
features, and/or advantages of the disclosed subject matter can be
exploited in disparate electromagnetic frequency bands. Moreover,
one or more embodiments described herein can be executed in one or
more network elements, and/or within one or more elements of a
network infrastructure, e.g., radio network controller, wireless
access point (AP), etc.
[0106] Moreover, terms like "user equipment," (UE) "mobile
station," "mobile subscriber station," "access terminal,"
"terminal", "handset," "appliance," "machine," "wireless
communication device," "cellular phone," "personal digital
assistant," "smartphone," "wireless device", and similar
terminology refer to a wireless device, or wireless communication
device, which is at least one of (1) utilized by a subscriber of a
wireless service, or communication service, to receive and/or
convey data associated with voice, video, sound, and/or
substantially any data-stream or signaling-stream; or (2) utilized
by a subscriber of a voice over IP (VoIP) service that delivers
voice communications over IP networks such as the Internet or other
packet-switched networks. Further, the foregoing terms are utilized
interchangeably in the subject specification and related
drawings.
[0107] A communication network, e.g., corresponding to a wireless
system (see e.g., 100), for systems, methods, and/or apparatus
disclosed herein can include any suitable mobile and/or
wireline-based circuit-switched communication network including a
GSM network, a time division multiple access (TDMA) network, a code
division multiple access (CDMA) network, such as an Interim
Standard 95 (IS-95) and subsequent iterations of CDMA technology,
an integrated digital enhanced network (iDEN) network and a PSTN.
Further, examples of the communication network can include any
suitable data packet-switched or combination data
packet/circuit-switched communication network, wired or wireless IP
network such as a VoLTE network, a VoIP network, an IP data
network, a UMTS network, a GPRS network, or other communication
networks that provide streaming data communication over IP and/or
integrated voice and data communication over combination data
packet/circuit-switched technologies.
[0108] Similarly, one of ordinary skill in the art will appreciate
that a wireless device, e.g., a wireless communication device, a
user equipment, etc. for systems, methods, and/or apparatus
disclosed herein can include a mobile device, a mobile phone, a 4G,
a 5G, etc. cellular communication device, a PSTN phone, a cellular
communication device, a cellular phone, a satellite communication
device, a satellite phone, a VoIP phone, WiFi phone, a dual-mode
cellular/WiFi phone, a combination cellular/VoIP/WiFi/WiMAX phone,
a portable computer, or any suitable combination thereof. Specific
examples of a wireless system can include, but are not limited to,
a cellular device, such as a GSM, TDMA, CDMA, IS-95 and/or iDEN
phone, a cellular/WiFi device, such as a dual-mode GSM, TDMA, IS-95
and/or iDEN/VoIP phones, UMTS phones, UMTS VoIP phones, or like
devices or combinations thereof.
[0109] The disclosed subject matter 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,
computer-readable carrier, or computer-readable media. For example,
computer-readable media can include, but are not limited to,
magnetic storage devices, e.g., hard disk; floppy disk; magnetic
strip(s); optical disk (e.g., compact disk (CD), digital video disc
(DVD), Blu-ray Disc (BD)); smart card(s); and flash memory
device(s) (e.g., card, stick, key drive); and/or a virtual device
that emulates a storage device and/or any of the above
computer-readable media.
[0110] In accordance with various aspects of the subject
specification, artificial intelligence based systems, components,
etc. can employ classifier(s) that are explicitly trained, e.g.,
via a generic training data, as well as implicitly trained, e.g.,
via observing characteristics of communication equipment, e.g., a
gateway, a wireless communication device, etc., by receiving
reports from such communication equipment, by receiving operator
preferences, by receiving historical information, by receiving
extrinsic information, etc.
[0111] For example, support vector machines can be configured via a
learning or training phase within a classifier constructor and
feature selection module, component, etc. Thus, the classifier(s)
can be used by an artificial intelligence system to automatically
learn and perform a number of functions, e.g., performed by a
system (e.g., 110), including but not limited to: selecting a group
of candidate locations for placement of respective small cells to
facilitate offloading, via the respective small cells, of traffic
from respective macrocells corresponding to the candidate
locations, wherein the respective small cells comprise first
transmission powers that are less than second transmission powers
of the respective macrocells; and for each candidate location of
the group of candidate locations, determining an estimated amount
of traffic capacity of a small cell of the respective small cells
that has been presumed to have been placed at the candidate
location, and determining estimated signal strengths of respective
signals that have been predicted to have been received from the
small cell at respective portions of a grid of a defined signal
coverage area corresponding to the candidate location
[0112] A classifier can be 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
infer an action that a user, e.g., subscriber, desires to be
automatically performed. In the case of communication systems, for
example, attributes can be information received from access points,
services, components of a wireless communication network, etc., and
the classes can be categories or areas of interest (e.g., levels of
priorities). A support vector machine is an example of a classifier
that can be employed. The support vector machine 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 include, 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 can also be
inclusive of statistical regression that is utilized to develop
models of priority.
[0113] As used herein, the term "infer" or "inference" refers
generally to the process of reasoning about, or inferring states
of, the system, environment, user, and/or intent from a set of
observations as captured via events and/or data. Captured data and
events can include user data, device data, environment data, data
from sensors, sensor data, application data, implicit data,
explicit data, etc. Inference can be employed to identify a
specific context or action, or can generate a probability
distribution over states of interest based on a consideration of
data and events, for example.
[0114] Inference can also refer to techniques employed for
composing higher-level events from a set of events and/or data.
Such inference results in the construction of new events or actions
from a set of observed events and/or stored event data, whether the
events are correlated in close temporal proximity, and whether the
events and data come from one or several event and data sources.
Various classification schemes and/or systems (e.g., a decision
tree based learning model, a linear regression based learning
model, support vector machines, neural networks, expert systems,
Bayesian belief networks, fuzzy logic, and data fusion engines) can
be employed in connection with performing automatic and/or inferred
action in connection with the disclosed subject matter.
[0115] Further, the word "exemplary" and/or "demonstrative" is used
herein to mean serving as an example, instance, or illustration.
For the avoidance of doubt, the subject matter disclosed herein is
not limited by such examples. In addition, any aspect or design
described herein as "exemplary" and/or "demonstrative" is not
necessarily to be construed as preferred or advantageous over other
aspects or designs, nor is it meant to preclude equivalent
exemplary structures and techniques known to those of ordinary
skill in the art having the benefit of the instant disclosure.
[0116] Furthermore, to the extent that the terms "includes," "has,"
"contains," and other similar words are used in either the detailed
description or the appended claims, such terms are intended to be
inclusive--in a manner similar to the term "comprising" as an open
transition word--without precluding any additional or other
elements. Moreover, 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.
[0117] The above description of illustrated embodiments of the
subject disclosure, including what is described in the Abstract, is
not intended to be exhaustive or to limit the disclosed embodiments
to the precise forms disclosed. While specific embodiments and
examples are described herein for illustrative purposes, various
modifications are possible that are considered within the scope of
such embodiments and examples, as those skilled in the relevant art
can recognize.
[0118] In this regard, while the disclosed subject matter has been
described in connection with various embodiments and corresponding
Figures, where applicable, it is to be understood that other
similar embodiments can be used or modifications and additions can
be made to the described embodiments for performing the same,
similar, alternative, or substitute function of the disclosed
subject matter without deviating therefrom. Therefore, the
disclosed subject matter should not be limited to any single
embodiment described herein, but rather should be construed in
breadth and scope in accordance with the appended claims below.
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