U.S. patent application number 13/435388 was filed with the patent office on 2013-10-03 for method, apparatus and computer readable medium for associating user equipment with a cell.
This patent application is currently assigned to ALCATEL-LUCENT USA INC.. The applicant listed for this patent is Supratim Deb, Pantelis Monogioudis. Invention is credited to Supratim Deb, Pantelis Monogioudis.
Application Number | 20130260712 13/435388 |
Document ID | / |
Family ID | 49235660 |
Filed Date | 2013-10-03 |
United States Patent
Application |
20130260712 |
Kind Code |
A1 |
Deb; Supratim ; et
al. |
October 3, 2013 |
METHOD, APPARATUS AND COMPUTER READABLE MEDIUM FOR ASSOCIATING USER
EQUIPMENT WITH A CELL
Abstract
The method includes determining one or more almost blank
subframes (ABS) associated with a macro cell and associating a user
equipment with one of the macro cell and a small cell using at
least one of the one or more ABS based on one or more market prices
associated with the small cell and the macro cell such that the
determining and associating occurs jointly.
Inventors: |
Deb; Supratim; (Somerset,
NJ) ; Monogioudis; Pantelis; (Randolph, NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Deb; Supratim
Monogioudis; Pantelis |
Somerset
Randolph |
NJ
NJ |
US
US |
|
|
Assignee: |
ALCATEL-LUCENT USA INC.
Murray Hill
NJ
|
Family ID: |
49235660 |
Appl. No.: |
13/435388 |
Filed: |
March 30, 2012 |
Current U.S.
Class: |
455/406 ;
455/405; 455/452.1 |
Current CPC
Class: |
H04W 48/20 20130101;
H04W 4/24 20130101; H04W 84/045 20130101; H04M 15/8033 20130101;
H04M 15/80 20130101 |
Class at
Publication: |
455/406 ;
455/452.1; 455/405 |
International
Class: |
H04W 4/26 20090101
H04W004/26; H04W 24/00 20090101 H04W024/00; H04W 72/04 20090101
H04W072/04 |
Claims
1. A method for wireless communication, comprising: associating a
user equipment with one of a macro cell and a small cell using one
or more almost blank subframes (ABS) offered by the macro cell, one
or more market prices related to associating with the macro cell
and the small cell, and a utility that accounts for user equipment
throughput based on potential associations.
2. The method of claim 1, prior to the associating step, further
comprising: offering, by one or more interfering macro cells of the
small cell, a plurality of ABS based on the utility.
3. The method of claim 2, wherein the utility is based on a
logarithm of data rates of a plurality of user equipment and user
equipment location, a downlink Signal to Interference plus Noise
Ratio (SINR) from macro cell transmission and a downlink SINR from
small cell transmission over ABS and non-ABS subframes.
4. The method of claim 3, wherein the logarithm is .SIGMA..sub.u
log(R.sub.u); u is the user equipment element of the plurality of
user equipment being summed; R.sub.u is a throughput associated
with user equipment (u); the throughput is based on the user
equipment location, the downlink SINR from macro cell transmission
and the downlink SINR from small cell transmission over ABS and
non-ABS subframes; and .SIGMA..sub.u log(R.sub.u) is maximized over
the plurality of user equipment.
5. The method of claim 2, wherein the utility is based on a
solution minimizing a sum of backlogs of a plurality of user
equipment.
6. The method of claim 1, wherein the associating is iterated a
number of times, during each iteration the one or more market
prices are updated based on a gradient descent plan, and the
associating uses the one or more market prices of the current
iteration.
7. The method of claim 6, wherein the gradient decent plan reaches
a global maxima for a given network configuration.
8. The method of claim 7, further comprising: determining an
average number of ABSs across all iterations, determining an
average throughput for each user equipment across all iterations,
wherein the average number of ABSs and the average throughput for
each user equipment converges based on a fractional solution.
9. The method of claim 8 wherein the fractional solution results in
at least one of the plurality of user equipment being associated
with both the macro cell and the small cell on a time-sharing
basis.
10. The method of claim 9, wherein the fractional solution is
rounded to an integral solution without violating a set of
feasibility constraints and produces an integer ABS offering of
user equipment associations to one of the small cell and the macro
cell.
11. The method of claim 10, further comprising: mapping the user
equipment associations to one or more cell selection bias value
such that a new set of user equipment associations obtained after
applying the cell selection bias is approximates the user equipment
associations.
12. The method of claim 1, wherein one of the one or more market
prices includes a price the user equipment pays to associate with
the small cell.
13. The method of claim 1, wherein one of the one or more market
prices includes a price the user equipment pays to associate with
the macro cell.
14. The method of claim 1, wherein one of the one or more market
prices includes a price the user equipment pays to use the one or
more ABS.
15. The method of claim 1, wherein one of the one or more market
prices includes a price associated with one or more interfering
macro cells.
16. The method of claim 1, wherein one of the one or more market
prices includes a price the small cell pays for the macro cell to
remain silent during a subframe period.
17. The method of claim 1, wherein one of the one or more market
prices includes a price the user equipment is willing to pay for
associating with one of the macro cell and the small cell.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field
[0002] Embodiments relate to associating user equipment (UE) with
macro cells and small cells in a wireless network.
[0003] 2. Related Art
[0004] Heterogeneous wireless networks (HetNets) are deployments of
cells with differing coverage radii within a single geographic
area. A typical configuration is one where macro (e.g., large)
cells provide contiguous coverage over the area while pico, femto
or metro (e.g., small) cells cover smaller areas that are
associated with either traffic hot spots or coverage holes. When
both the macro cells and metro cells share the same carrier
frequency, the deployment is called a co-channel or shared-carrier
deployment.
[0005] For example, a HetNet may include macro base stations (BSs)
and metro base stations BSs. Macro BSs provide wireless coverage
for user equipment (UEs) within the macro cells which may cover
large geographical areas, while metro BSs may provide wireless
coverage for UEs located in the metro cells which may cover smaller
geographical areas within the coverage are of a macro BS.
Parameters needed to configure BSs within HetNets include patterns
for and allocation of an almost blank subframe (ABS).
SUMMARY OF THE INVENTION
[0006] One embodiment includes a method. The method includes
determining one or more almost blank subframes (ABS) associated
with a macro cell and associating a user equipment with one of the
macro cell and a metro cell using at least one of the one or more
ABS based on one or more market prices associated with the small
cell and the macro cell such that the determining and associating
occurs jointly.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The present invention will become more fully understood from
the detailed description given herein below and the accompanying
drawings, wherein like elements are represented by like reference
numerals, which are given by way of illustration only and thus are
not limiting of the present invention and wherein:
[0008] FIG. 1A is a diagram illustrating a portion of a wireless
communications network according to at least one example
embodiment.
[0009] FIG. 1B is a diagram illustrating an example relationship
between transmission schedules for macro and small cells.
[0010] FIG. 2 illustrates a method of associating user equipment
according to an example embodiment.
[0011] FIG. 3 illustrates a system implementing the method of FIG.
2 according to an example embodiment.
[0012] FIG. 4 illustrates a method associated with the system of
FIG. 3 according to an example embodiment.
[0013] FIG. 5 illustrates a system implementing the method of FIG.
2 according to an example embodiment.
[0014] It should be noted that these Figures are intended to
illustrate the general characteristics of methods, structure and/or
materials utilized in certain example embodiments and to supplement
the written description provided below. These drawings are not,
however, to scale and may not precisely reflect the precise
structural or performance characteristics of any given embodiment,
and should not be interpreted as defining or limiting the range of
values or properties encompassed by example embodiments. For
example, the relative thicknesses and positioning of molecules,
layers, regions and/or structural elements may be reduced or
exaggerated for clarity. The use of similar or identical reference
numbers in the various drawings is intended to indicate the
presence of a similar or identical element or feature.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0015] While example embodiments are capable of various
modifications and alternative forms, embodiments thereof are shown
by way of example in the drawings and will herein be described in
detail. It should be understood, however, that there is no intent
to limit example embodiments to the particular forms disclosed, but
on the contrary, example embodiments are to cover all
modifications, equivalents, and alternatives falling within the
scope of the claims. Like numbers refer to like elements throughout
the description of the figures.
[0016] Before discussing example embodiments in more detail, it is
noted that some example embodiments are described as processes or
methods depicted as flowcharts. Although the flowcharts describe
the operations as sequential processes, many of the operations may
be performed in parallel, concurrently or simultaneously. In
addition, the order of operations may be re-arranged. The processes
may be terminated when their operations are completed, but may also
have additional steps not included in the figure. The processes may
correspond to methods, functions, procedures, subroutines,
subprograms, etc.
[0017] Methods discussed below, some of which are illustrated by
the flow charts, may be implemented by hardware, software,
firmware, middleware, microcode, hardware description languages, or
any combination thereof. When implemented in software, firmware,
middleware or microcode, the program code or code segments to
perform the necessary tasks may be stored in a machine or computer
readable medium such as a storage medium. A processor(s) may
perform the necessary tasks.
[0018] Specific structural and functional details disclosed herein
are merely representative for purposes of describing example
embodiments of the present invention. This invention may, however,
be embodied in many alternate forms and should not be construed as
limited to only the embodiments set forth herein.
[0019] It will be understood that, although the terms first,
second, etc. may be used herein to describe various elements, these
elements should not be limited by these terms. These terms are only
used to distinguish one element from another. For example, a first
element could be termed a second element, and, similarly, a second
element could be termed a first element, without departing from the
scope of example embodiments. As used herein, the term "and/or"
includes any and all combinations of one or more of the associated
listed items.
[0020] It will be understood that when an element is referred to as
being "connected" or "coupled" to another element, it can be
directly connected or coupled to the other element or intervening
elements may be present. In contrast, when an element is referred
to as being "directly connected" or "directly coupled" to another
element, there are no intervening elements present. Other words
used to describe the relationship between elements should be
interpreted in a like fashion (e.g., "between" versus "directly
between," "adjacent" versus "directly adjacent," etc.).
[0021] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
example embodiments. As used herein, the singular forms "a," "an"
and "the" are intended to include the plural forms as well, unless
the context clearly indicates otherwise. It will be further
understood that the terms "comprises," "comprising," "includes"
and/or "including," when used herein, specify the presence of
stated features, integers, steps, operations, elements and/or
components, but do not preclude the presence or addition of one or
more other features, integers, steps, operations, elements,
components and/or groups thereof.
[0022] It should also be noted that in some alternative
implementations, the functions/acts noted may occur out of the
order noted in the figures. For example, two figures shown in
succession may in fact be executed concurrently or may sometimes be
executed in the reverse order, depending upon the
functionality/acts involved.
[0023] Unless otherwise defined, all terms (including technical and
scientific terms) used herein have the same meaning as commonly
understood by one of ordinary skill in the art to which example
embodiments belong. It will be further understood that terms, e.g.,
those defined in commonly used dictionaries, should be interpreted
as having a meaning that is consistent with their meaning in the
context of the relevant art and will not be interpreted in an
idealized or overly formal sense unless expressly so defined
herein.
[0024] Portions of the example embodiments and corresponding
detailed description are presented in terms of software, or
algorithms and symbolic representations of operation on data bits
within a computer memory. These descriptions and representations
are the ones by which those of ordinary skill in the art
effectively convey the substance of their work to others of
ordinary skill in the art. An algorithm, as the term is used here,
and as it is used generally, is conceived to be a self-consistent
sequence of steps leading to a desired result. The steps are those
requiring physical manipulations of physical quantities. Usually,
though not necessarily, these quantities take the form of optical,
electrical, or magnetic signals capable of being stored,
transferred, combined, compared, and otherwise manipulated. It has
proven convenient at times, principally for reasons of common
usage, to refer to these signals as bits, values, elements,
symbols, characters, terms, numbers, or the like.
[0025] In the following description, illustrative embodiments will
be described with reference to acts and symbolic representations of
operations (e.g., in the form of flowcharts) that may be
implemented as program modules or functional processes include
routines, programs, objects, components, data structures, etc.,
that perform particular tasks or implement particular abstract data
types and may be implemented using existing hardware at existing
network elements. Such existing hardware may include one or more
Central Processing Units (CPUs), digital signal processors (DSPs),
application-specific-integrated-circuits, field programmable gate
arrays (FPGAs) computers or the like.
[0026] It should be borne in mind, however, that all of these and
similar terms are to be associated with the appropriate physical
quantities and are merely convenient labels applied to these
quantities. Unless specifically stated otherwise, or as is apparent
from the discussion, terms such as "processing" or "computing" or
"calculating" or "determining" of "displaying" or the like, refer
to the action and processes of a computer system, or similar
electronic computing device, that manipulates and transforms data
represented as physical, electronic quantities within the computer
system's registers and memories into other data similarly
represented as physical quantities within the computer system
memories or registers or other such information storage,
transmission or display devices.
[0027] Note also that the software implemented aspects of the
example embodiments are typically encoded on some form of program
storage medium or implemented over some type of transmission
medium. The program storage medium may be magnetic (e.g., a floppy
disk or a hard drive) or optical (e.g., a compact disk read only
memory, or "CD ROM"), and may be read only or random access.
Similarly, the transmission medium may be twisted wire pairs,
coaxial cable, optical fiber, or some other suitable transmission
medium known to the art. The example embodiments not limited by
these aspects of any given implementation.
[0028] Overview of Network Architecture
[0029] FIG. 1A illustrates a portion of a wireless communications
network 100. Referring to FIG. 1A, wireless communications network
100 may follow, for example, an LTE protocol. Communications
network 100 includes a macro base station (BS) 110A; a small cell
BS 110B; a macro cell 120, a small cell 125; and first through
third UEs 105A-C. The small cell 125 may be, for example a pico
cell, a femto cell or a metro cell. Further, the term small cell as
used herein may be considered synonymous to and/or referred to as
pico cell, a femto cell or a metro cell. The small cell 125
includes a cell range extended area (CRE) 127 and an in-cell area
130.
[0030] In the example illustrated in FIG. 1A, the first UE 105A is
attached to the macro cell 120, and the second and third UEs 105B
and 105C are attached to the small cell 125. Further, the second UE
105B is designated as a CRE UE and the third UE 105C is designated
as an in-cell UE. As will be discussed in greater detail below, the
attachment of UEs to either the macro cell 120 or the small cell
125 may be controlled by a cell selection bias (CSB) value 145.
[0031] Though, for the purpose of simplicity, communications
network 110 is illustrated as having only macro BS 110A, small cell
BSs 110B, and first through third UEs 105A-C, communications
network 100 may have any number of macro BSs, small cell BSs and
UEs. Further, the macro BS 110A and small cell BS 110B may be
connected to other core network elements included in the
communications network 100 including, for example, one or more
mobility management entities (MME) and/or one or more operations,
administration and management (OAM) nodes (not shown). Further, the
MME may include the OAM node.
[0032] The macro BS 110 may be, for example, an evolved nodeB (eNB)
providing wireless coverage for UEs within the macro cell 120. The
small cell BS 110B may be, for example, an eNB underlaid with
respect to the macro BS 110A. The small cell BS 110B may provide
wireless coverage for UEs associated with the small cell 125 that
supplements coverage provided by the macro BS 110A. Data, control
signals and other information described herein as be sent and/or
received by the macro cell 120 may be sent and/or received by the
macro BS 110A. Further, operations described herein as being
performed by the macro cell 120 may be performed by the macro BS
110A. Data, control signals and other information described herein
as be sent and/or received by the small cell 120 may be sent and/or
received by the small cell BS 110B. Further, operations described
herein as being performed by the small cell 125 may be performed by
the small cell BS 110B.
[0033] In general a transmit power of the macro BS 110A may be
greater than a transmit power of the small cell BS 110B. Transmit
powers 115A and 115B illustrate an example of the relative transmit
powers of the macro BS 110A and the small cell BS 110B,
respectively. Macro receive signal level 135 illustrates an example
of a strength of a receive signal of the macro cell 120 measured by
UEs within communications network 100. As is illustrated in FIG.
1A, in general, the strength of the macro receive signal level may
drop as a distance from a location of the macro BS 110A increases.
Small cell receive signal level 140 illustrates an example of a
strength of a receive signal of the small cell 125 measured by UEs
within communications network 100.
[0034] As is illustrated in FIG. 1A, in general, the strength of
the small cell receive signal level 140 may drop as a distance from
a location of the small cell BS 110B increases. Though, for the
purpose of simplicity, macro and small cell signal levels 135 and
140 are described above and illustrated in FIG. 1A as having
strengths which decrease along with an increased distance from a
BS, macro and small cell signal strengths can be effected by any of
a number of other factors in addition to distance including, for
example, shapes and heights of buildings or physical structures
within the coverage areas of macro cell 120 and small cell 125, and
a number of UEs or an amount of UE traffic within a given region of
the coverage area of the macro cell 120 or the small cell 125.
[0035] As one skilled in the art will appreciate (and indicated
above), a small cell may be a pico, micro or metro cell. For
example, a metro cell may imply an Open Subscriber Group (OSG)
small cell(s), in that subscribers (e.g., a user of a UE) with
valid subscriptions with an operator may (a) freely roam into the
coverage area of a metro small cell and register with the network,
if they are not already in a call. This procedure is typically
called cell (re)selection procedure, and may (b) establish a
traffic connection to the metro small cell if the subscribers are
already in a call, a procedure called handover. Therefore, there
may be an implicit association between a metro cell and the OSG and
an implicit association between femto and Closed Subscriber Group
(CSG). In CSG femto cells may include a selected group of
subscribers that can perform cell (re)selection and handover
procedures to/from the femto cell. Example embodiments are
applicable to OSG, CSG and any arrangement in between such as a
Hybrid Access (HA) where in between the two extremes access
policies may be established by the mobile operator.
[0036] As one skilled in the art will appreciate, a BS may be a
logical entity that incorporates transmitter and receiver
functionality. Therefore, a base station may be physically embodied
as a group of remote RF transmitting and receiving antennas,
associated with those antennas remote radio heads (RRHs) and remote
to the antenna and RRH centralized baseband cage or other physical
arrangement familiar to those skilled in the art. A physical
transmitting antenna may correspond to one or more coverage cells.
Therefore a logical BS may be associated with more than one cell.
The terms bases station (BS) may be used interchangeably in the
descriptions below.
[0037] Explanation of the Cell Selection Bias (CSB) Value and
Almost Blank Subframe (ABS) Parameters
[0038] UEs in the communications network 100 that are within both a
coverage area of the macro cell 120 and a coverage areas of the
small cell 125 may be in communication with the communications
network 100 via a wireless connection to either the macro BS 110A
or one of the small cell BS 110B. A UE in communications network
100 makes a decision with respect to which of the macro cell 120
and the small cell 125 to be associated with by comparing the macro
receive signal level 135 as measured by the UE to the small cell
receive signal level as measured by the UE with use of a CSB value.
For example, for a UE i currently attached to the macro cell 120,
if MR(i)<PR(i)+B, where MR(i) is the macro signal strength 135
measured at the UE i, and PR(i) is the small cell signal strength
140 measured at the UE i, and B is the CSB 145, then UE i is handed
over from the macro cell 120 to the small cell 125. The handover of
the UE i may be performed according to known processes.
[0039] Accordingly, once a UE associated with the macro cell 120
determines that the macro receive signal level 135 is below the
small cell receive signal level 140 plus a CSB value 145, the UE
may initiate a hand-over operation from the macro cell 120 to the
small cell 125 using known methods. As is illustrated in FIG. 1A,
the points at which the macro receive signal level 135 is below the
small cell receive signal level 140 plus the CSB value 145 define
the boundaries of the small cell 125. Accordingly, the CSB value
145 may control an amount of UEs which are handed over from the
macro cell 120 to the small cell 125 by controlling, for example, a
tendency of the UEs to initiate a hand off operation.
[0040] When the macro cell 120 and the small cell 125 transmit data
over downlink connections to associated UEs, the transmissions of
the macro cell 120 may cause interference for UEs receiving data
from the small cell 125 (e.g., those like the second UE 105B).
Further, the transmissions of the small cell 125 may cause
interference for UEs receiving data from the macro cell 120 (e.g.,
those like the first UE 105A). Accordingly, wireless communications
network 100 implements an interference reduction scheme which
includes dividing UEs attached to the small cell 125 into in-cell
UEs and CRE UEs according to known methods. For example, for a UE i
currently attached to the small cell 125, if PR(i)>MR(i), where
MR(i) is the macro signal strength 135 measured at the mobile i,
and PR(i) is the small cell signal strength 140 measured at the
mobile i then UE i may be designated as an in-cell UE. Otherwise,
if MR(i)-CSB<PR(i)<MR(i), the UE i may be designated as a CRE
UE.
[0041] The wireless communication network 100 may implement the
enhanced inter cell interference coordination (eICIC) scheme
defined by, for example, the 3GPP Release 10 standards. For
example, in order to reduce an amount of interference experienced
by UEs receiving downlink transmissions, transmissions for UEs
designated as CRE UEs (e.g, those like the second UE 105B in the
CRE 127) may be scheduled at a different time from transmissions
for UEs designated as in-cell UEs (e.g., those like the third UE
105C within the in-cell area 130) and UEs attached to the macro
cell 120 (e.g., those like the first UE 105A within the macro cell
120 and outside the small cell 125). This feature will now be
discussed in greater detail below with reference to FIG. 1B.
[0042] FIG. 1B is a diagram illustrating an example relationship
between transmission schedules for macro and small cells 120 and
125. Referring to FIG. 1B, first graph 170 illustrates subframes
transmitted over time for the macro cell 120 and second graph 175
illustrates subframes transmitted over time for the small cell 125.
As is illustrated by the first graph 170, the macro cell 120 may
transmit downlink data on all frames except those designated as
almost blank subframes (ABSs). As is illustrated by the second
graph 175, the small cell 125 may transmit data to UEs designated
as in-cell UEs on all frames except those designated as ABS
frames.
[0043] Accordingly, the small cell 125 may transmit data to UEs
designated as in-cell UEs following the same schedule as the macro
cell 120. Further, as is illustrated by the second graph 175, the
small cell 125 may transmit data to UEs designated as small cell
border UEs on the subframes designated by the macro cell 120 as ABS
subframes. In the example illustrated in FIG. 1B, an ABS pattern of
the transmission schedules illustrated in first and second graphs
170 and 175 is configured such that every third subframe is
designated as an ABS subframe. Accordingly, in the ABS pattern
illustrated in FIG. 1B, there is a 2-to-1 ratio between non-ABS
subframes and ABS subframes.
[0044] According to at least one example embodiment, in the
wireless network 100, the ABS pattern may be determined by a BS
within the wireless network 100 and communicated, by that BS, to
other BSs within the wireless network 100. For example, the macro
BS 110A may set an ABS pattern for downlink transmission in the
macro cell 120 based on information stored in the macro BS 110A and
information received from the small cell BS 110B. The macro BS 110A
may then communicate the set ABS pattern to the small cell BS 110B
so the small cell BS 110B can schedule downlink transmission in the
small cell 125 based on the set ABS pattern. Alternatively, the
small cell BS 110B may set an ABS pattern for downlink transmission
in the small cell 125 based on information stored in the small cell
BS 110B and information received from the macro cell BS 110A. The
small cell BS 110B may then communicate the set ABS pattern to the
macro BS 110A so the macro BS 110A can schedule downlink
transmission in the macro cell 120 based on the set ABS pattern.
Methods by which one or more BSs within the wireless network 100
can determine an ABS pattern will be discussed in greater detail
below with reference to FIG. 2.
[0045] Though, in the example illustrated in FIG. 1A, the ABS
pattern includes a 2-to-1 ratio between non-ABS subframes and ABS
subframes, this is only an example, and the ABS pattern can include
any ratio (i.e., duty cycle) between non-ABS subframes and ABS
subframes including, for example, 4-to-3, 3-to-2, 3-to-1, 4-to-1,
etc.
[0046] Associating user Equipment to Macro Cells, Small Cells and
Almost Blank Subframes (ABS)
[0047] According to example embodiments, a dynamic price or market
price may be used in determining associations between UEs 105,
macro cells 120, small cells 125 and ABS. The dynamic price or
market price includes different price variables that are maintained
and updated. The price variables may be (1) macro BS prices, (2)
small BS prices and (3) UE prices.
[0048] For example, each macro BS 110A maintains and updates two
types of prices that the macro BS 110A charges: (1) a price UEs 105
may pay for association with the macro BS 110A and, (2) a price
that may be paid by any small BS 110B that requires the macro BS
110A to be silent over ABS subframes.
[0049] For example, each small BS 110B may maintain and update
three types of prices that the small BS 110B charges: (1) a price
that UE's 105 may pay for small BS 110B association, (2) an
additional price that UE's 105 may pay for using ABS subframes, and
(3) a price that each interfering macro BS 110A may pay.
[0050] For example, each UE 105 may have a price the UE 105 is
willing to pay (WTP) for association with BSs 110A, 110B and
availing airtime resources.
[0051] General Methodology
[0052] According to example embodiments, the ABS offered by each
macro BS 110A, and downlink airtime resources that UEs 105 avail
from the macro BS 110A and small BS 110B (over ABS subframes and
non-ABS subframes) may be more efficiently (or even optimized)
based on current prices. The different prices may be updated based
on current ABS/UE-airtime (downlink) such that the system
approaches a desired or optimal utility. The price update and
ABS/UE-airtime updates may be iteratively performed. Finally, the
UE 105 associations may be mapped to Cell Selection Bias (CSB)
values for each small BS 110B.
[0053] Any UE 105 may have achievable PHY rates to a macro BS 110A
and nearest small BS 110B. For the small BS 110B each UE 105 may
have two data rates, one with and one without ABS. This data rate
may be determined using measurements available at the UE 105. For
each small BS 110B, the set of macro BSs that interfere with the
small BS 110B may be known. The ABSs offered by each macro BS 110A
and a set of UEs 105 associated with each small BS 110B may be
determined so as to maximize .SIGMA..sub.u log(R.sub.u) where
R.sub.u is the throughput (e.g., data rate) attained by UE-u and
the summation is carried over all UEs 105 in the entire network.
.SIGMA..sub.u log(R.sub.u) may be maximized subject to association
constraints, interference constraints and total airtime
constraints.
[0054] The association constraints may be that a UE 105 can
associate with either the macro BS 110A or a small BS 110B but not
both. More precisely, fractional associations (e.g., associations
with the macro BS 110A for 25% of the time and the small BS 110B
for 75% of the time) are not allowed, only integral associations
are allowed.
[0055] The interference constraint may be that the ABS subframes
for a small BS 110B p is reserved by all macro BSs that interfere
with the small BS 110B.
[0056] The total airtime constraints may be: the total airtime
allocated to UEs 105 on an average should be less than the total
subframes in a frame. This ensures that the throughputs (e.g., data
rates) are achievable by the MAC scheduler.
[0057] According to example embodiments the general methodology for
associating UE 105 to macro cells 120, small cells 125 and ABS may
be performed in two steps. First, determine ABS subframes and
airtimes while ignoring the integrality requirements on user UE
105's association to small BSs, and the number of ABS subframes
offered by each Macro BS 110A. Second, round off the relaxed
solution that allows fractional solutions, to a feasible integral
solution taking into consideration (e.g., staying within) the above
constraints.
[0058] Determining ABS Subframes and Airtimes while Ignoring the
Integrality Requirements
[0059] Variable definitions: [0060] pr(m): price charged by macro-m
to any UE for which macro-m is a candidate macro BS; [0061] pr(p):
price charged by small-p to any UE for who Pico-p is a candidate
small BS; [0062] A_pr(p): additional price charged by small-p to
any UE that utilizes ABS subframes; [0063] I_pr(p,m): interference
price charged by small-p to any macro-m that interferes with
small-p; [0064] WTP(u): price that UE-u is willing to pay for a
unit data rate; [0065] NAS(m): non-ABS subframes/ frame reserved by
macro-m; [0066] AS(p): ABS subframes used by small-p; [0067] x(u):
aggregate airtime (in subframes/frame) that UE-u gets from a
candidate macro BS; [0068] y(u): aggregate ABS airtime (in
subframes/frame) that UE-u gets from its candidate small BS, if
any; and [0069] z(u): aggregate non-ABS airtime (in
subframes/frame) that UE-u gets from its candidate small BS.
[0070] Equations used for updating ABS subframes and airtimes:
gain(u,m)=WTP(u)*PhyDataRateToMacro-pr(m) (1);
A_gain(u,p)=WTP(u)*PhyDataRateToSmallInABS-(A.sub.--pr(P)+pr(p))
(2);
I_gain(m)=pr(m)-.SIGMA. I.sub.--pr(p) (3);
where .SIGMA. is over all interfering small BSs of macro-m;
I_gain(p)=pr(p)-.SIGMA. I.sub.--pr(m) (4);
where .SIGMA. is over all interfering macro BSs of small-p.
[0071] Equations used for updating variables:
x(u)=(1-w)*x(u)+w*curr.sub.--x(u) (5);
y(u)=(1-w)*y(u)+w*curr.sub.--y(u) (6);
z(u)=(1-w)*z(u)+w*curr.sub.--z(u) (7);
NAS(m)=(1-w)*NAS(m)+w*curr.sub.--NAS(m) (8);
AS(p)=(1-w)*AS(p)+w*curr.sub.--AS(p) (9);
where: w is a smoothing variable that may be set to a small
constant or, alternatively, set to 1/(IterationNo) as a dynamic
quantity; and curr_variable (e.g., curr_x(u)) is the current value
for the representative variable.
[0072] Equations used for updating prices (note: [ ].sup.+
represents projection into the space of positive real numbers):
pr(m)=pr(m)+step_size*[(total curr.sub.--x(u) over all child
UE-u)-curr.sub.--NAS(m)].sup.+ (10);
A.sub.--pr(p)=A.sub.--pr(p)+step_size*[(total curr.sub.--y(u) over
all child UE-u)-curr.sub.--AS(p)].sup.+ (11);
pr(p)=pr(p)+step_size*[(total curr.sub.--z(u)+curr.sub.--y(u) over
all child UE-u)-NumSubframes].sup.+ (12);
I.sub.--pr(m,p)=I.sub.--pr(m,p)+step_size*[curr.sub.--NAS(m)+Curr.sub.---
AS(p)-NumSubframes].sup.+ (13);
WTP(u)=WTP(u)+step_size*[1/WTP(u)-Available_throughput based on
Curr.sub.--x,curr.sub.--y,curr.sub.--z].sup.+ (14);
[0073] According to example embodiments a method may include
determining one or more almost blank subframes (ABS) associated
with a macro cell 120 and associating a user equipment with one of
the macro cell 120 and a small cell using at least one of the one
or more ABS based on one or more market prices associated with the
small cell 125 and the macro cell 120 such that the determining and
associating occurs jointly. For example, the determining and
associating is performed approximately simultaneously (e.g., in
close proximity of time) and such that determining and associating
may be a coupled solution.
[0074] FIG. 2 illustrates a method of associating user equipment
according to an example embodiment. The associating is of user
equipment to a macro BS 110A, a small BS 110B and/or ABS.
[0075] Referring to FIG. 2, in step S205 a macro BS 110A
initializes variables to arbitrary positive values. For example,
the macro BS 110A initializes pr(m), pr(p), A_pr(p), I_pr(p,m),
WTP(u), NAS(m), AS(p), x(u), y(u) and z(u), described above, to
arbitrary positive values. The arbitrary positive values may be for
example a same value, different values and/or a combination
thereof. The arbitrary positive values are a design time choice
based on empirical study.
[0076] In step S210 the macro BS 110A determines gain. For example,
gain may be determined for each UE 105 for which the macro BS 110A
is a candidate macro BS 110A (e.g., each UE 105 that may associate
with the macro BS 110A). For example, the gain for each UE 105 may
be calculated using equation 1 above. The overall gain of the macro
may be determined using equation 3 above.
[0077] Alternatively and/or in addition, in step S210 a small BS
110B determines gain. For example, gain may be determined for each
UE 105 for which the small BS 110B is a candidate (e.g., each UE
105 that may associate with the small BS 110B). For example, the
gain for each UE 105 may be calculated using equation 2 above. The
overall gain of the macro may be determined using equation 4
above.
[0078] In step S215 the macro BS 110A determines rates in the
current iteration. For example, current airtimes (e.g., current
x(u)) may be determined such that the current airtimes equal the
number of subframes if the current iteration has a positive,
maximum gain gain(u,m) for all UEs 105 that the macro BS 110A is a
candidate macro BS 110A. For example, current ABS airtime may be
determined such that the current ABS airtime equals the number of
subframes for any UE 105 with a positive, maximum gain gain(u,p).
For example, current non-ABS airtime may be determined such that
the current non-ABS airtime equals the number of subframes for any
UE 105 with a positive, maximum gain gain(u,p). If no such UE 105
exists for an iteration, the corresponding airtime may be set to
zero.
[0079] Further, in step S215 the non-ABS subframes used by the
macro BS 110A may equal the number of subframes if I_gain(m),
determined in step S210, is greater than zero. The non-ABS
subframes used by the macro BS 110A may equal zero otherwise. Still
further, the ABS subframes used by the small BS 110B may equal the
number of subframes if I_gain(p), determined in step S210, is
greater than zero. The non-ABS subframes used by the small BS 110B
may equal zero otherwise.
[0080] In step S220 the macro BS 110A updates the variables. For
example, the macro BS 110A updates NAS(m), AS(p), x(u), y(u) and
z(u) according to equations 5-9.
[0081] In step S225 the macro BS 110A updates the prices. For
example, the one or more market prices may be updated based on a
gradient descent plan. The gradient decent plan may reach a global
maxima for a given network configuration. Gradient decent plans are
known to those skilled in the art and will not be discussed
further. The one or more updated market prices may be used during
one or more iterations of a UE 105 association. For example, the
macro BS 110A updates pr(m), pr(p), A_pr(p), I_pr(p,m) and WTP(u)
according to equations 10-14.
[0082] One or more interfering macro cells 120 of the small cell
125 may offer a plurality of ABS based on a utility associated with
a wireless communication system.
[0083] In step S230 the macro BS 110A determines if the ABSs
offered by each macro BS 110A and a set of UEs 105 associated with
each small BS 110B converge. For example the convergence may be
based on utility associated with a wireless communication
system.
[0084] For example, utility may be based on a logarithm of data
rates of the plurality of user equipment based on user equipment
location, a downlink Signal to Interference plus Noise Ratio (SINR)
from macro cell 120 transmission and a downlink SINR from small
cell transmission over ABS and non-ABS subframes. Utility may be
determined so as to maximize .SIGMA..sub.u log(R.sub.u) where
R.sub.u is the throughput (e.g., data rate) attained by UE-u and
the summation is carried over all UEs 105 in the entire network
results. In other words, the macro BS 110A determines if the change
from iteration to iteration of .SIGMA..sub.u log(R.sub.u) is
sufficiently small. Alternatively and/or in addition, utility may
be based on a solution minimizing a sum of backlogs of a plurality
of user equipment's associated with the wireless communication
system. Utility is not limited to the above example. Utility may be
based on a "value" of providing a certain measurable metric (e.g.
throughput) of the wireless communication system.
[0085] Convergence may also include determining an average number
of ABSs across a plurality or all iterations and determining an
average throughput for each user equipment across the plurality or
all iterations such that the average number of ABSs and the average
throughput for each user equipment converges based on a fractional
solution. The fractional solution may result in at least one of the
plurality of user equipment being associated with both the macro
cell 120 and the small cell 125 on a time-sharing basis. If the
association converges, processing continues to step S235.
Otherwise, processing returns to stem S210.
[0086] In step S235 the macro BS 110A associates each of the UEs
105 with a base station. For example, if a UE 105 achieves a higher
throughput (e.g., higher datarate) from a macro BS 110A than a
small BS 110B, then the UE 105 is associated with the macro BS
110A. Otherwise, the UE 105 is associated with the small BS 110B.
Further, associating each of the UEs 105 with a base station may
include mapping the efficient (or even optimal) user equipment
associations to one or more cell selection bias (CSB) value such
that a new set of user equipment associations obtained after
applying the cell selection bias approximates the efficient (or
even optimal) user equipment associations.
[0087] In step S240 ABS subframes are rounded to the nearest
integer value. For example, should the number of ABS subframes
following step S230 be a fractional number or zero, the number of
ABS subframes are set to the nearest positive integer value. For
example, the fractional solution may be rounded to an integral
solution without violating a set of feasibility constraints (e.g.,
interference constraints) and produces an integer ABS offering of
user equipment associations to one of the small cell 125 and the
macro cell 120.
[0088] In addition, according to example embodiments, if the
association decisions in step S235 result in a non-zero number of
UEs 105 assigned to a small BS 110B, then the ABS sub-frames of
that small BS 110B is at least one. Further, after the rounding, if
any of the interference constraints (described above) are violated,
then either AS(p) or NAS(m) may be decremented by 1 for the
associated assignment that violates the constraint.
[0089] In step S245 the macro BS 110A determines the throughput
(e.g., datarate) for each base station (macro and small). The macro
determines the throughput using any known technique. For example,
the macro BS 110A may use the known weighted-proportional fair
allocation technique for each of the resources among the associated
UEs 105.
[0090] Mapping Small BS Associations to Cell Selection Bias (CSB)
Values
[0091] The method described above with regard to FIG. 2 may more
efficiently (or even optimize) ABS sub-frames and UE 105
association. However, UEs 105 associate to a suitable BS based on
RSRP measurements from the BS. In order to achieve the desired
association, suppose csb(c) is the cell section bias of cell c that
could be macro cell or a small cell. Then UE-u associates with cell
p(u) (denoting parent of UE-u) if:
p(u)=arg max.sub.--c[csb(c)+RSRP(c,u)] (15);
where, RSRP(c,u) is the RSRP of cell-c at UE-u; and arg max_c
indicates the set of points of c is used for which equation 15
attains its maximum value.
[0092] Consider small cell p and let S(p,m) be a more efficient (or
even optimal) set of UEs 105 that associated with small BS 110B p
if the choice is between macro cell m and small cell p.
Subsequently,
RSRP(p,u)+csb(p)>RSRP(m,u)+csb(m) (16);
for the UE-u element of S(p,m)
[0093] As a result,
csb(p)-csb(m)>Delta(m,p) (17);
where Delta(m,p) is defined as,
Delta(m,p)=max.sub.--u[RSRP(m,u)-RSRP(p,u)] (18);
[0094] The above condition should hold true for all interfering
small BS 110B, macro BS 110A pairs in order to ensure that all
UE(u) in S(p,m) associate to small BS 110B p. Accordingly, values
of CSB's so that equation 17 is satisfied for all interfering small
BS 110B, macro BS 110A pairs are found.
[0095] As is known, an interference graph associated with macro BSs
and small BSs may be represented as a bipartite graph with nodes of
the left-part denoting small BSs and nodes on the right-part
denoting macro BSs. Further, as is known, a bipartite graph may be
considered as a tree graph. Accordingly, known tree searches may be
used. For example, a breadth-first search tree (forest) search may
be used. For example, the breadth-first search tree of the
interference graph with macro BS 110A at the root of each tree of
the forest may be used. For example, this tree may have macro BS
110A nodes at the even-level and small BS 110B nodes at the
odd-level.
[0096] According to example embodiments, the tree may be denoted as
T(BFS). Further, let V(1) represent the nodes belonging to level-1
of T(BFS). According to example embodiments, values of CSB's so
that equation 17 is satisfied for all interfering small BS 110B,
macro BS 110A pairs may be determined as follows:
[0097] Start with level l=0 of T(BFS) and for all macro BSs `m` at
level `l`, set csb(m)=0. Evaluate each node (e.g., repeated until
all macro cells (nodes) and small cells (nodes) are visited in
T(BFS)) at the next level, such that l becoming l+1 results in one
of two solutions:
for all small BS's `p` in level-l, set csb(p) equal to
Max_{all macro `m` in level-(l-1)}[csb(m)+Delta(p,m)] (19);
where, l is odd (e.g., nodes in T(BFS) at this level are small
cells); and for all macro BS's `m` in level-l, set csb(m) equal
to
-1.0*Max_{all small `p` in level-(l-1)}[-1.0*csb(p)+Delta(p,m)]
(20);
where, l is even (e.g., nodes in T(BFS) at this level are macro
cells).
[0098] The above example embodiment is to illustrate one example of
the mapping of small BS associations to cell selection bias (CSB)
values. Example embodiments are not limited thereto.
Example Implementations
[0099] Radio Network Planning of LTE HetNet using a Network
Simulator
[0100] FIG. 3 illustrates a system implementing the method of FIG.
2 according to an example embodiment. As shown in FIG. 3, the
system 300 includes a traffic map generator 305, a network
simulator 310, a propagation map server/database 315, a parallel
computation module 320 and an ABS/CSB mapping module 330. The
parallel computation module 320 may include one or more computation
modules for a BS or group of BSs 325.
[0101] The traffic map generator 305, the network simulator 310,
the propagation map server/database 315, the parallel computation
module 320, the ABS/CSB mapping module 330 and the one or more
computation modules for a BS or group of BSs 325 may be implemented
as a computer program for use on a computer system (e.g., a server
or a processing device associated with a base station, a base
station controller or a radio network controller), the computer
program being, for example, a series of computer instructions, code
segments or program segments stored on a tangible or non-transitory
data recording medium (computer readable medium), such as a fixed
disk. The series of computer instructions, code segments or program
segments may constitute all or part of the functionality of the
elements described above, and may also be stored in any memory
device, volatile or non-volatile, such as semiconductor, magnetic,
optical or other memory device.
[0102] The system 300 may be incorporated into one or more macro
base stations. Alternatively and/or in addition the system 300 may
be incorporated into one or more core network elements (e.g., a
base station controller or a radio network controller).
[0103] The traffic map generator 305 and the propagation map
server/database 315 are generally known to those skilled in the
art. The traffic map generator 305 and the propagation map
server/database 315 may be configured to account for network load
and propagation characteristics such as, for example, traffic
intensity maps. Each may be a database or server that may be
queried to acquire up to date (time) information regarding the
current network load and propagation characteristics. In general,
system 300 does not require exact location traffic hot-spot and
load. For example, the traffic intensity maps may be coarse grained
and made available on a per-cell basis for different times in a
day.
[0104] The network simulator 310 may be a commercial network
simulator (e.g., commercially available embedded network simulator)
that is used to generate inputs required by an enhanced inter cell
interference coordination (eICIC) algorithm as modified by the
methods according to one or more example embodiments. The network
simulator 310 may use the traffic map, propagation map, and BS
locations to generate multiple snapshots of UE 105 locations.
Network simulator 310 may use methods according to one or more
example embodiments (e.g., described above with regard to FIG. 3)
and may perform ABS/CSB determinations for each of these snapshots
and average the output.
[0105] The computation modules for a BS or group of BSs 325 may
include one or more processor configured to execute a solution for
one or more subsets of the overall UE 105 association method or
problem. For example, UE 105's association may be based on
geographic or other location characteristics. For example, the
computational module 325-1 may include a computation of ABS and CSB
for a group of neighboring BSs and communicate to another
computational module 325-2, quantities that are used to establish
an a more efficient (or even optimum) solution across a multitude
of computational modules. The solution may be established with the
aid of the ABS/CSB mapping module 330.
[0106] The ABS/CSB mapping module 330 may determine the optimal ABS
value and which UEs 105 associate with small cells. The computation
may be broken down into different processes (either on the same
computing server or separate) with each process responsible for a
group of BSs. The processors communicate with each other through
simple messages, thus ensuring that the final solution accounts for
the entire network. The details of which are described above with
regard to FIG. 3.
[0107] FIG. 4 illustrates a method associated with the system of
FIG. 3 according to an example embodiment. Referring to FIG. 4, in
step S405 the macro BS 110A determines key performance indicators
(KPI). For example, the macro BS 110A may establish a radio
resource control (RRC) link in order to request KPI from one or
more cells (macro or small) in a time based manner (e.g., once an
hour). For example, the KPI may include reference signal received
power (RSRP), reference signal received quality (RSRQ), carrier
received signal strength indicator (RSSI), cell aggregate
throughput, cell edge user throughput and the like.
[0108] In step S410 the macro BS 110A determines if KPI has changed
significantly. For example, the macro BS 110A may compare each of
the KPI to a past KPI to determine a delta. If the delta is more
than a threshold value the change may be determined to be
significant. The change may be determined to be significant if one
KPI is greater than the threshold. Alternatively, more than one KPI
delta may exceed the threshold for the change to be significant.
Another alternative may be that there are multiple thresholds for
each KPI. For example, a lower delta indicates a threshold for
multiple KPI requirements for significant change to be indicated
and a higher threshold indicates the KPI (with the higher delta)
indicates a significant change. If KPI has changed significantly
processing continues to step S415. Otherwise, processing returns to
step S405.
[0109] In step S415 the macro BS 110A generates user equipment (UE)
snapshots and inputs for ABS determination. For example, the macro
BS 110A determines values for initializing pr(m), pr(p), A_pr(p),
I_pr(p,m), WTP(u), NAS(m), AS(p), x(u), y(u) and z(u) as required
for step S205. For example, the determined values may be associated
with stored values. For example the stored values may be from a
previous determination of ABS and CSB. For example, cell
performance indicators may be determined. For example the cell
performance indicators may be one or more PKI (e.g., RSRP). For
example, UE-location snapshots are generated based on traffic maps
and propagation maps. Further, the UE 105 snapshots (e.g., UE 105
location) may be from multiple systems. Each sample UE-locations
may be converted into downlink PHY-layer rate between UE 105 and
Macro, UE and Pico with and without ABS.
[0110] In step S420 the steps associated with the method of FIG. 2
are performed. For example, for each UE 105 snapshot, the method of
FIG. 2 is executed. For example, each step and/or portions thereof
may be segregated into multiple computers, each responsible for a
given set of BSs (e.g., BS prices and associated UEs 105). The
segregation may require price exchange between interfering small
BSs and macro BSs in different clusters which may be accomplished
using messaging standards.
[0111] In step S425 the macro BS 110A determines if a sufficient
number of UE 105 snapshots have been used to generate ABS and CSB
data. The sufficient number may be a threshold number and may be a
design time determination. If a sufficient number of UE 105
snapshots have been used processing continues to step S430.
Otherwise, processing returns to step S415. In step S430 ABS and
CSB results are averaged over all UE snapshot samples.
Example Implementations
[0112] Self-Organizing Networks
[0113] FIG. 5 illustrates a system implementing the method of FIG.
2 according to an example embodiment. As shown in FIG. 5, the
system includes several small base stations and several macro base
stations. Small base stations and macro base stations are described
above in more detail and will not be discussed further for the sake
of brevity. The system of FIG. 5 may be organized as a
self-organizing network (SON). A SON is generally known to
implement a `plug-and-play` paradigm in the way that new base
stations shall automatically be configured and integrated into the
network. This infers both connectivity establishment, and download
of configuration parameters and software. Generally, base station
parameters may be regularly adjusted based on both base station and
mobile station observations.
[0114] In order to make use of blank or almost blank subframes
(ABSs) effective, signaling is provided from the macro cell 120 to
the small cell 125 across the corresponding backhaul interface,
known in LTE as the "X2" interface. For LTE Release 10, it has been
agreed that this X2 signaling will take the form of a coordination
bitmap to indicate the ABS pattern (for example with each bit
corresponding to one subframe in a series of subframes, with the
value of the bit indicating whether the subframe is an ABS or not).
Such signaling can help the small cell 125 to schedule data
transmissions in the small cell 125 appropriately to avoid
interference (e.g. by scheduling transmissions to UEs 105 near the
edge of the small cell 125 during ABSs), and to signal to the UEs
105 the subframes which should have low macrocellular interference
and should therefore be used for RRM/RLM/CQI measurements.
(RRM=Radio Resource Management, typically relating to handover;
RLM=Radio Link Monitoring, typically relating to detection of
serving radio link failure; CQI=Channel Quality Information,
derived from the signal strength from the serving cell and the
interference from other cells, and typically used for link
adaptation and scheduling on the serving radio link)
[0115] FIG. 5 further illustrates an X2 message according example
embodiments. As shown in FIG. 5, the message may include three
fields. The fields may include Cell Id, Message Id and CQI Vector
Instance per Price (I_pr). The message may be communicated as
illustrated in FIG. 5.
[0116] Example embodiments are configured to provide additional X2
messages between an interfered small cell 125 and an interfering
macro cell 120. Each message may include (i) Cell id of the sending
BS, (ii) a unique message ids similar to the known
Escape_To_Proprietary message that may be either the current value
of I_pr or the interference price used in our algorithm, or the
agreed upon CQI vector over which the algorithm has to run, and
(iii) the price value of 2 bytes or CQI vector values.
[0117] Initially, the small cells 125 and macro cells 120 agree
upon a UE CQI vector to be used for running ABS optimization. Each
Macro maintains the macro prices and WTPs of UEs 105 for which the
macro cell 120 is a candidate. Each small cell 125 maintains small
cell prices and interference prices I_pr(p,m) for each interfering
macro cell 120. The prices are updated as described above (see step
S225).
[0118] The new values of I_pr are sent from small cell 125 to
interfering macro cells 120 over the X2 interface according to
example embodiments as described above. Each macro cell 120 updates
ABS offerings and UE 105 airtimes using the prices based on the
method of FIG. 2 described above. Sending the new values of I_pr,
updating ABS offerings and UE 105 airtimes may be repeated as
necessary.
CONCLUSION
[0119] Example embodiments provide cell specific almost blank
subframes (ABS) and Cell Selection Bias (CSB) value configuration.
Example embodiments may determine cell specific configurations. By
contrast, known solutions determine a single network wide ABS and
CSB value which may be less advantageous because different cells
may have different traffic and propagation maps. Example
embodiments account for the dependence of propagation map, traffic
map/hotspot location on the ABS and CSB. Because traffic maps may
be different at different times of the day, example embodiments may
determine configurations at different times of the day. Example
embodiments scale linearly with the number of BSs. Therefore,
example embodiments are less complex than the known solutions. This
property enables the solution to be applicable to large cities
(e.g., NYC) where there could be tens of thousands of BSs fed into
a planning tool.
[0120] Example embodiments take into consideration an entire
network instead of each macro cell 120 in isolation. Therefore,
example embodiments account for configurations in one cell
affecting an adjacent cell's performance which could cause a ripple
effect throughout the network. In example embodiments, the CSB
setting may be dependent on ABS and vice-versa. Therefore, ABS and
CSB may be determined jointly or simultaneously and not
independently.
[0121] The use of small cells 125 may theoretically result in
increased spectral efficiency. Example embodiments described above
provide a HetNet deployment to realize the promised gains. For
example, example embodiments provide association rules for deciding
which UEs 105 get associated with small cells 125. Further, example
embodiments provide solutions as to how macro and small BSs
time-share spectrum so that UEs 105 associated with small cells 125
achieve a high throughput without impacting the macro cell 120
performance.
[0122] In addition, wireless networks (e.g., wireless network 100)
may be highly dynamic. Traffic load, hot-spot locations, SINR
distribution in different cells, application mix may be dynamic and
each of these directly impact the UE 105 association and
time-sharing of spectrum between macro cells 120 and small cells
125. Further, configurations in cells (e.g., macro cell 120 and
small cell 125) of the network may affect adjacent cells and this
could have a ripple effect on throughput of the network. Example
embodiments take into consideration the network in its entirety
when associating UEs 105 to macro cells 120, small cells 125 and
ABSs should consider.
[0123] The choice of ABS and CSB may impact (a) a MAC scheduler
associated with the network (e.g., wireless network 100) and (b) a
UE's 105 choice of BS during association as shown in the above
figure. According to example embodiments a more efficient (or even
optimal) choice of ABS offered by a macro BS 110A may be coupled
with which UEs 105 are associated with macro cells 120 and which
UEs 105 are associated with small cells 125. Accordingly, according
to example embodiments, ABS selection and UE 105 association may be
determined jointly. In addition, for a small BS 110B to transmit
data over an ABS subframe, example embodiments may provide that all
the interfering macro BSs 110A of the small BS 110B are silent over
that particular ABS subframe.
[0124] Although the above example embodiments describe the steps as
being performed by the network entities illustrated in FIG. 1A
(e.g., macro BSs), example embodiments are not limited thereto. For
example, the above method steps may be performed by alternative
network components.
[0125] Alternative embodiments of the invention may be implemented
as a computer program product for use with a computer system, the
computer program product being, for example, a series of computer
instructions, code segments or program segments stored on a
tangible or non-transitory data recording medium (computer readable
medium), such as a diskette, CD-ROM, ROM, or fixed disk, or
embodied in a computer data signal, the signal being transmitted
over a tangible medium or a wireless medium, for example, microwave
or infrared. The series of computer instructions, code segments or
program segments can constitute all or part of the functionality of
the methods of example embodiments described above, and may also be
stored in any memory device, volatile or non-volatile, such as
semiconductor, magnetic, optical or other memory device.
[0126] While example embodiments have been particularly shown and
described, it will be understood by one of ordinary skill in the
art that variations in form and detail may be made therein without
departing from the spirit and scope of the claims.
[0127] The invention being thus described, it will be obvious that
the same may be varied in many ways. Such variations are not to be
regarded as a departure from the invention, and all such
modifications are intended to be included within the scope of the
invention.
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