U.S. patent application number 17/559231 was filed with the patent office on 2022-06-23 for self-optimizing distributed antenna system using soft frequency reuse.
The applicant listed for this patent is DALI WIRELESS, INC.. Invention is credited to Seyed Amin HEJAZI, Shawn Patrick STAPLETON.
Application Number | 20220201623 17/559231 |
Document ID | / |
Family ID | 1000006171986 |
Filed Date | 2022-06-23 |
United States Patent
Application |
20220201623 |
Kind Code |
A1 |
HEJAZI; Seyed Amin ; et
al. |
June 23, 2022 |
SELF-OPTIMIZING DISTRIBUTED ANTENNA SYSTEM USING SOFT FREQUENCY
REUSE
Abstract
A method of determining a carrier power in a communications
system including a processor includes a) setting a power
differential between a reference carrier and one or more carriers,
b) measuring a number of satisfied users at the power differential,
and c) measuring a capacity for the satisfied users at the power
differential. The method also includes d) increasing the power
differential by a predetermined amount and e) determining, using
the processor, that the number of satisfied users at the increased
power differential is greater than or equal to the number of
satisfied users at the power differential. The method further
includes f) repeating a)-c) and g) setting the carrier power at an
iterated power level.
Inventors: |
HEJAZI; Seyed Amin;
(Burnaby, CA) ; STAPLETON; Shawn Patrick;
(Vancouver, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
DALI WIRELESS, INC. |
Menlo Park |
CA |
US |
|
|
Family ID: |
1000006171986 |
Appl. No.: |
17/559231 |
Filed: |
December 22, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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16536167 |
Aug 8, 2019 |
11212751 |
|
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17559231 |
|
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|
15676631 |
Aug 14, 2017 |
10433261 |
|
|
16536167 |
|
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|
15154073 |
May 13, 2016 |
9769766 |
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15676631 |
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13935157 |
Jul 3, 2013 |
9363768 |
|
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15154073 |
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61669572 |
Jul 9, 2012 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 5/0032 20130101;
H04W 52/243 20130101; H04L 5/0023 20130101; H04L 5/0073 20130101;
H04W 52/346 20130101; H04W 72/0473 20130101 |
International
Class: |
H04W 52/24 20060101
H04W052/24; H04L 5/00 20060101 H04L005/00; H04W 52/34 20060101
H04W052/34; H04W 72/04 20060101 H04W072/04 |
Claims
1. A method comprising: setting a transmission power level for a
Digital remote Unit (DRU); determining a key performance indicator
related to a quality of service at the transmission power level;
iteratively adjusting the transmission power level for the DRU to
increase the key performance indicator related to the quality of
service; and setting the transmission power level for the DRU at an
iterated power level.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is a continuation of the co-pending U.S.
patent application Ser. No. 16/536,167 filed Aug. 8, 2019, which is
a continuation of U.S. patent application Ser. No. 15/676,631 filed
Aug. 14, 2017, now U.S. Pat. No. 10,433,261, which is a
continuation U.S. patent application Ser. No. 15/154,073, filed May
13, 2016, now U.S. Pat. No. 9,769,766, which is a continuation of
U.S. patent application Ser. No. 13/935,157, filed on Jul. 3, 2013,
now U.S. Pat. No. 9,363,768, which claims priority to U.S.
Provisional Patent Application No. 61/669,572, filed on Jul. 9,
2012. The aforementioned applications, and issued patents, are
incorporated herein by reference, in their entirety for any
purpose.
SUMMARY OF THE INVENTION
[0002] According to an embodiment of the present invention, a
method of determining a carrier power in a communications system
including a processor is provided. The method includes a) setting a
power differential between a reference carrier and one or more
carriers, b) measuring a number of satisfied users at the power
differential, and c) measuring a capacity for the satisfied users
at the power differential, which may be referred to as an initial
power differential. The method also includes d) adjusting the power
differential by a predetermined amount and e) determining, using
the processor, that the number of satisfied users at the adjusted
power differential is greater than or equal to the number of
satisfied users at the initial power differential. The method
further includes f) repeating a)-e) and g) setting the carrier
power at an iterated power level.
[0003] As described herein, unbalanced traffic distributions inside
cellular networks are common occurrences. Embodiments of the
present invention provide a throughput-balancing system that
optimizes cellular performance according to the geographic traffic
distribution in order to provide a high quality of service (QoS).
The throughput of an Orthogonal Frequency Division Multiple Access
(OFDMA) based architecture (DAS-SFR) that utilizes a combination
Soft Frequency Reuse (SFR) technique and a Distributed Antenna
System (DAS) is analyzed in light of embodiments of the present
invention. A concept employed by this architecture is to distribute
the antennas in a hexagonal cell in such a way that the central
antenna is responsible for serving a special area, using all of the
frequency bands, while the remaining antennas utilize only a subset
of the frequency bands based on a frequency reuse factor. A DAS-SFR
has the ability to distribute the cellular capacity (throughput)
over a given geographic area. To enable throughput balancing among
Distributed Antennas (DAs), embodiments of the present invention
dynamically change the DA's carrier power to manage the inter-cell
interference, as a function of the time-varying traffic. A Downlink
Power Self-Optimization (PSO) algorithm, for three different
resource allocation scenarios, is described for the DAS-SFR system.
The transmit powers are optimized in order to maximize the spectral
efficiency of a DAS-SFR and maximize the number of satisfied users
under different user distributions in some embodiments. The PSO
algorithm is able to guarantee a high Quality of Service (QoS) that
concentrates on the number of satisfied users as well as the
capacity of satisfied users as the two Key Performance Indicators
(KPIs). Analytical derivations and simulations are discussed and
used to evaluate the system performance for different traffic
scenarios, and the results are presented.
[0004] Embodiments of the present invention provide a method and
system for adjusting and potentially optimizing the powers of
multiple carriers in a DAS-SFR system. By adjusting the power
associated with the carriers provided by the central antenna of
each cell, the SFR system enables higher system performance and an
improved user experience as a result of higher system
bandwidth.
[0005] Numerous benefits are achieved by way of the present
invention over conventional techniques. For instance, embodiments
of the present invention control the amount of resources allocated
to users located in different areas, thereby increasing the
frequency efficiency and also improving the data rate for cell edge
users. As another example, embodiments of the present invention are
useful in adjusting the powers of carriers to increase or maximize
Key Performance Indicators, which are related to Quality of
Service. These and other embodiments of the invention along with
many of its advantages and features are described in more detail in
conjunction with the text below and attached figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 illustrates band width allocation to antennas for
three different combinations of DAS with SFR, HFR and FFR according
to embodiments of the present invention;
[0007] FIG. 2 illustrates the structure of a Distributed Antenna
System according to an embodiment of the present invention;
[0008] FIG. 3 illustrates a block diagram of the Received Signals
with Interference Signals and Noises according to an embodiment of
the present invention;
[0009] FIG. 4 is a simplified flowchart illustrating the PSO
algorithm according to an embodiment of the present invention;
[0010] FIGS. 5A-5B illustrate plots of ergodic capacity versus the
normalized distance from the DRU0 according to embodiments of the
present invention;
[0011] FIGS. 6A-6D illustrate KPIs versus the .DELTA.P for
different distribution users scheme where C.sub.th=0.01W.sub.RB
according to embodiments of the present invention; and
[0012] FIGS. 7A-7D illustrate KPIs versus the .DELTA.P for
different distribution users scheme where C.sub.th=0.07W.sub.RB
according to embodiments of the present invention.
[0013] FIG. 8 shows R1 and R2.
DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS
[0014] In existing networks, parameters are manually adjusted to
obtain a high level of network operational performance. 3GPP LTE is
the preferred candidate for the next generation wireless networks.
In the last 15 years, there has been substantial growth in cellular
mobile communication systems. It is imperative to provide a high
quality of service (QoS) at a minimum cost. With the substantial
increase in cellular users, unbalanced throughput distributions are
common in wireless networks which decrease the number of satisfied
users. As traffic environments change, the network performance will
not be optimum. Therefore, it is necessary to perform inter-cell
optimization of the network dynamically according to the traffic
environment, especially when cell traffic is not uniformly
distributed. This is one of the important optimization issues in
self-organizing networks (SON) for 3GPP LTE.
[0015] In SON, parameter tuning is done automatically based on
measurements. The use of throughput-balancing is meant to deliver
extra gain in terms of network performance. For
throughput-balancing this is achieved by adjusting the network
control parameters in such a way that ultra-high throughput users
can offload to ultra-low throughput users inside the cell. In a
live network, high throughput fluctuations occur. A SON enabled
network, where the proposed SON algorithm monitors the network and
reacts to these changes in throughput, can achieve better
performance by distributing the throughput among users.
[0016] When the traffic loads among cells are not balanced, the
satisfaction probability of heavily loaded cells may be lower,
since their neighboring cells cause high inter-cell interference on
cell edge users. In this case, throughput balancing can be
conducted to alleviate and even avoid this problem.
[0017] Inter-cell interference, experienced by cell-edge users, is
very high when this interference is a result of using the same
subcarriers in the adjacent cell in the same time slot. High
inter-cell interference means severe degradation of the cell-edge
throughput since Mobile 3GPP LTE adopts a frequency reuse factor of
one which is called Full Frequency Reuse (FFR), in which each cell
serves users using the entire system bandwidth.
[0018] To mitigate the inter-cell interference in cellular systems,
several techniques have been incorporated in these standards.
Advanced receiver techniques such as Maximum Likelihood (ML)
Multiuser Detection (MUD), the MMSE Receiver MUD and Other-cell
interference cancellation are the three potential ways to reduce
interference in cellular systems; however, these require a more
complicated receiver. Advanced transmitter techniques such as
Cooperative Encoding (CA), Closed-Loop MIMO Diversity Schemes
(CLMD) and Beam forming are three other techniques to overcome the
interference problem in cellular systems but CA requires very
accurate channel state knowledge and real time inter-cell
coordination, CLMD and Beam forming sacrifice spatial dimensions
and require channel state knowledge.
[0019] One possible strategy to alleviate interference, both in the
uplink and the downlink of cellular networks, is to reduce the
overall transmit power by using a Distributed Antenna Systems
(DAS), which also has the additional advantage of improving
capacity and coverage.
[0020] The other possible strategy is a Soft Frequency Reuse
technique; this technique effectively reduces the inter-cell
interference by geographically spacing the competing transmissions
farther apart, which benefits users near the cell boundaries.
[0021] A. Distributed Antenna System (DAS):
[0022] Distributed antenna systems (DAS) have been widely
implemented in state-of-the art cellular communication systems to
cover dead spots in wireless communications systems.
[0023] A DAS breaks the traditional radio base station architecture
into two pieces: a central processing facility and a set of
distributed antenna (DA), connected to the central facility by a
high-bandwidth network. The DAS network transports radio signals,
in either analog or digital form, to/from the central facility
where all the base station's processing is performed. By replacing
a single high-power antenna with several low-power antennas,
distributed to give the same coverage as the single antenna, a DAS
is able to provide more-reliable wireless services within a
geographic area or structure while reducing its power
consumption.
[0024] DAS has the following potential advantages such as:
throughput improvement, coverage improvement, increased cellphone
battery life and a reduction in transmitter power. Recent research
has shown the benefits of using DAS in a cellular system for
extending coverage, reducing call blocking rate and reducing
inter-cell interference. An extension to a traditional DAS system
is an Intelligent DAS, wherein each remote has the added
flexibility of independently transmitting preselected carriers.
[0025] Most of the research on DAS has focused on investigating
SINR advantages of DAS and analyzing its performance. Some research
on DAS has focused on the analysis of the uplink performance due to
its analytical simplicity, while there are few studies on the
downlink performance of DAS, although the demand for high-speed
data rate will be dominant in the downlink path. There is also very
little research that considers the advantages of DAS in a
multi-cell context.
[0026] B. Soft Frequency Reuse (SFR) Technique:
[0027] SFR has been proposed as an inter-cell interference
mitigation technique in OFDMA based wireless networks. In SFR, the
frequency band is divided into a fixed number of sub-bands; all
sub-bands are used by all eNBs to serve "near" users; the other
sub-bands are dedicated to "far" users. All sub-bands are allocated
to the cells according to some predefined reuse factor. The SFR
assigns sub-bands limited amount of transmit power to reduce
inter-cell interference. The transmit power needs to be reduced
enough to provide the required throughput to cell edge users of
neighboring cells. Also, the sub-bands of reduced transmit power
are used for the inner cell users.
[0028] Hard Frequency Reuse (HFR) suffers from a reduced spectral
efficiency in such a way that, in HFR, the frequency band is
divided into a fixed number of sub-bands that are allocated to the
cells according to some predefined reuse factor and lets
neighboring cells transmit on different sub-bands. On the other
hand, SFR has the benefit of a full spectral efficiency and is a
strong mechanism for inter-cell interference mitigation.
[0029] The capacity of the SFR was evaluated in assuming the offset
in the transmit powers of different sub-bands. Self-organization of
the transmit power in the uncoordinated systems was illustrated in
where some transient time is required to converge on the
equilibrium state of power allocation. Recent research on SFR has
focused on optimal system design utilizing advanced techniques such
as graph theory and convex optimization to maximize network
throughput. Additional work on FFR and SFR consider alternative
schedulers and the authors determined the frequency partitions in a
two-stage heuristic approach.
[0030] Accordingly, this paper proposes a new architecture to
suppress inter-cell interference. The proposed architecture
combines DAS and SFR for an OFDMA system (e.g. LTE). We analyze the
potential gains of DAS-SFR in a multi-cell environment.
[0031] The proposed architecture divides the entire spectral
bandwidth F into 3 parts (F.sub.1, F.sub.2, F.sub.3). The system
assigns the eNB the full-reused frequency (all 3 parts) to the
central antenna and the other 6 edge antennas work only on 1 part
based on a reuse factor of .DELTA. (ie. .DELTA.=3) in such a way
that neighbor cell edge antennas do not use the same frequency,
FIG. 1 (a). Two other combinations of DAS with HFR and FFR are also
demonstrated in FIG. 1 (b) and FIG. 1 (c), respectively.
[0032] In order to attain user satisfaction, a minimum throughput
should be provided for all users. In this publication, the system
QoS is a function of the number of satisfied users. For a DAS-SFR
architecture, the cell-edge throughput can be improved due to the
reduced inter-cell interference as well as from the boosted
cell-center transmission power. However, as compared to FFR the
overall network throughput decreases at the same time, since the
improvement is obtained at the cost of the cell-center user
throughput. Thus, an efficient resource allocation and power
allocation scheme is required to achieve the optimum overall
network throughput in the DAS-SFR implementation.
[0033] Therefore, to improve the throughput for the cell edge users
and further increase the number of satisfied users (the users that
can achieve a targeted service bitrate), a downlink Power
Self-Optimization (PSO) algorithm for three different resource
allocation scenarios is proposed for the DAS-SFR. The transmit
powers are allocated so that the spectral efficiency is maximized
for the DAS-SFR, and the number of satisfied users is also
maximized. The spectral efficiency represented by the ergodic
capacity is obtained for the different scenarios. The results show
that a DAS-SFR architecture effectively addresses inter-cell
interference in a multi-cell environment, especially at the cell
boundaries when compared to a HFR cellular architecture. The
results also show that a DAS-SFR architecture achieves a
non-trivial capacity enhancement over a HFR cellular architecture
for a frequency reuse factor of 3.
[0034] A contribution of this work is the development of an
analytical framework to evaluate the ergodic capacity of a DAS-SFR
architecture. This is an important metric to consider, especially
for users at the cell-edge since modern cellular networks are
increasingly required to provide users with high data-rate and a
guaranteed quality-of-service (QoS). This work presents a strategy
for optimally allocating frequency RBs to edge users in a DAS-SFR
architecture, based on a chosen performance threshold, which we
define as T.sub.p.
[0035] A system model is presented in section II. In section III,
the achievable capacity is derived for a distributed antenna
system. Formulation of the Power allocation algorithm is discussed
in section IV. Analytical and simulation results are shown in
section V and a conclusion is provided in section VI.
[0036] II. System Model
[0037] A. System Architecture:
[0038] The general architecture of an intelligent DAS in a
multi-cell environment is shown in FIG. 2, where 7 Digital Remote
Units (DRUs) are connected to an eNB via an optical fiber and a
Digital Access Unit (DAU). The DAUs are interconnected and
connected to multiple sectors. This capability enables the
virtualization of the eNB resources at the independent DRUs. The
eNBs are linked to a public switched telephone network or a mobile
switching center. DRUs are sectorized in such a way that each DRU
allocated to a given eNB sector can be simulcast. For the
simulcasting operation, the access network between each eNB and its
DRUs should have a multi-drop bus topology. In contrast, the same
area (7 DRUs) is covered by a single high-power eNB in a
traditional cellular system.
[0039] The total transmit power of the n-th DRU of i-th cell in
f-th frequency part is denoted P.sub.n.sup.(i,f), where the central
DRU of each cell is index by n=0.
[0040] We also consider the 2-tier cellular structure, where two
continuous tiers of eighteen cells surround a given cell. Although
this assumption of only 2-tiers of interfering cells is optimistic,
a pessimistic assumption that all the DRUs and the eNB are
transmitting full power all the time easily compensates.
[0041] B. Resource Allocation Scenarios:
[0042] In a multiuser DAS-SFR system, different users are located
at varying distances from the DRUs and have varying channel
conditions on the subcarriers. Therefore, resource allocation
allows for efficient exploitation of multiuser diversity in the
system.
[0043] Much of the research on SFR system design has focused on how
to determine the size of the frequency partitions, for example, in
a typical LTE system with a bandwidth of 5 MHz, 25 RBs may be
available to serve users for each frequency part (F.sub.i,
i=1,2,3).
[0044] For a typical central cell, we can assume that the center
DRU is assigned to the full-reused frequency and the other six edge
DRUs are assigned F.sub.1. Now, we consider three resource
allocation scenarios: [0045] Scenario 1: F.sub.1, F.sub.2, F.sub.3
RBs are assigned to all users in the cell. Note that in this
scenario, the very low SINR exterior users are inefficiently using
the F.sub.2 and F.sub.3 RBs. [0046] Scenario 2: F.sub.1 RBs are
assigned to all users but F.sub.2 and F.sub.3 RBs are solely
assigned to interior users. Note that in this scenario, the
available RBs are fully assigned to the interior users, which leads
to a big gap between the numbers of allocated RBs to the interior
users as compared to the exterior users. [0047] Scenario 3: F RBs
are solely assigned to the exterior users, whereas the F.sub.2 and
F.sub.3 RBs are assigned to the interior users. In this scenario,
all RBs are more fairly assigned between all users, as compared to
the previously mentioned scenario. Moreover, in this scenario the
RBs are allocated to the users following a SINR-based approach, in
which the edge users using the F.sub.1 RBs, and the interior users
using the F.sub.2 and F.sub.3 RBs have a high SINR.
[0048] We primarily assume a single user scenario, and further
extend it to a uniformly distributed multiuser LTE system. In a
multiuser scenario, we investigate both the analytical and the
simulation results in order to verify the system's capacity
improvement.
[0049] Received Signal and Channel Model
[0050] The downlink path of a DAS can be considered as an
equivalent MIMO system with additive interference and noise (FIG.
3). The received signal vector of the user in the central cell at
frequency f can be expressed as,
y ( 0 , f ) = signal + interference + noise = H ( 0 , f ) .times. x
( 0 , f ) + i = 1 18 .times. .times. H ( i , f ) .times. x ( i , f
) + n ( f ) ( 1 ) ##EQU00001##
where H.sup.(i,f).di-elect cons.C.sup.1.times.7, i=0, 1, . . . ,
18, denotes the channel matrix between the DRUs in the i-th cell
and the user in the central cell, x.sup.(i,f)=[x.sub.0.sup.(i,f),
x.sub.1.sup.(i,f), . . . , x.sub.6.sup.(i,f)].sup.T.di-elect
cons.C.sup.7.times.1, i=0, 1, . . . , 18 is the transmitted signal
vector of the DRUs in the i-th cell, n.sup.(f).di-elect
cons.C.sup.1.times.1 denotes the white noise vector with
distribution (0,.sigma..sub.n.sub.(f).sup.2I.sub.1). The
distributed antenna power constraint is considered, we have
E[|x.sub.n.sup.(i,f)|.sup.2].ltoreq.P.sub.n.sup.(i,f), n=0,1, . . .
,6, i=0,1, . . . ,18, (2)
where in DAS-SFR, x.sub.n.sup.(i, F.sup.1.sup.)=0, P.sub.n.sup.(i,
F.sup.1.sup.)=0 when (n=1, 2, . . . , 6 and i=1, 2, . . . , 7, 9,
11, 13, 15, 17), x.sub.n.sup.(i, F.sup.2.sup.)=0, P.sub.n.sup.(i,
F.sup.2.sup.)=0 when (n=1, 2, . . . , 6 and i=0, 2, 4, 6, 7, 8, 10,
11, 12, 14, 15, 16, 18), x.sub.n.sup.(i, F.sup.3.sup.)=0,
P.sub.n.sup.(i, F.sup.3.sup.)=0 when (n=1, 2, . . . , 6 and i=0, 1,
3, 5, 8, 9, 10, 12, 13, 14, 16, 17, 18), in DAS-HFR3 (frequency
reuse factor 3), x.sub.n.sup.(i,F.sup.1.sup.)=0,
P.sub.n.sup.(i,F.sup.1.sup.)=0 when (n=1, 2, . . . , 6 and i=1, 2,
. . . , 7, 9, 11, 13, 15, 17), x.sub.n.sup.(i,F.sup.2.sup.)=0,
P.sub.n.sup.(i,F.sup.2.sup.)=0 when (n=1, 2, . . . , 6 and i=0, 2,
4, 6, 7, 8, 10, 11, 12, 14, 15, 16, 18),
x.sub.n.sup.(i,F.sup.3.sup.)=0, P.sub.n.sup.(i,F.sup.3.sup.)=0 when
(n=1, 2, . . . , 6 and i=0, 1, 3, 5, 8, 9, 10, 12, 13, 14, 16, 17,
18),
in DAS-FFR,
[0051] x.sup.(i,f).noteq.0, P.sup.(i,f).noteq.0 when (n=0, 1, . . .
, 6 and i=0, 1, . . . , 18, f=F.sub.1, F.sub.2, F.sub.3), where
P.sub.n.sup.(i,f) denotes the power constraint of the n-th DRU in
the i-th cell for frequency band f.
[0052] The composite fading channel matrix H.sup.(i,f), i=0, 1, . .
. , 18, encompasses not only small-scale fading (fast fading) but
also large-scale fading (slow fading), which is modeled as
H ( i , f ) = H w ( i , f ) .times. L ( i , f ) = [ h 0 ( i , f ) ,
h 1 ( i , f ) , .times. , h 6 ( i , f ) ] diag .times. { l 0 ( i ,
f ) , l 1 ( i , f ) , .times. , l 6 ( i , f ) } ( 3 )
##EQU00002##
where, H.sub.w.sup.(i,f) and L.sup.(i,f) reflect the small-scale
channel fading and the large-scale channel fading between the DRUs
in the i-th cell and the user in the central cell, respectively.
{h.sub.j.sup.(i,f)|j=0, 1, . . . , 6; i=0, 1, . . . , 18;
f=F.sub.1, F.sub.2, F.sub.3} are independent and identically
distributed (i.i.d) circularly symmetric complex Gaussian variables
with zero mean and unit variance, and {I.sub.j.sup.(i,f)I|j=0, 1, .
. . , 6; i=0, 1, . . . , 18; f=F.sub.1, F.sub.2, F.sub.3} can be
modeled as
I.sub.n.sup.(i,f)= {square root over
([D.sub.n.sup.(i)].sup.-y.chi..sub.n.sup.(i,f))}, n=0,1, . . . ,6,
i=0,1, . . . ,18 (4)
[0053] Where D.sub.n.sup.(i) and .chi..sub.n.sup.(i,f) are
independent random variables representing the distance and the
shadowing between the user in the central cell and the n-th DRU in
the i-th cell, respectively. .gamma. denotes the path loss
exponent. {.chi..sub.j.sup.(i,f)|j=0, 1, . . . , 6; i=0, 1, . . . ,
18; f=F.sub.1, F.sub.2, F.sub.3} are i.i.d random variables with
probability density function (PDF)
f .chi. .function. ( .chi. ) = 1 2 .times. .pi. .times.
.lamda..sigma. .chi. .times. .chi. .times. exp .function. ( - ( ln
.times. .times. .chi. ) 2 2 .times. .lamda. 2 .times. .sigma. .chi.
2 ) , .chi. > 0 , ( 5 ) ##EQU00003##
[0054] Where .sigma..sub..chi. is the shadowing standard deviation
and
.lamda. = ln .times. .times. 10 10 . ##EQU00004##
[0055] Since the number of interfering sources is sufficiently
large and interfering sources are independent with each other, the
interference plus noise is assumed to be a complex Gaussian random
vector as follows:
N ( f ) = i = 1 18 .times. .times. H ( i , f ) .times. x ( i , f )
+ n ( f ) ( 6 ) ##EQU00005##
[0056] The variance of N is derived by Central Limit Theorem as
Var .function. ( N ( f ) ) = [ i = 1 18 .times. .times. n = 0 6
.times. .times. [ l n ( i , f ) ] 2 .times. P n ( i , f ) + .sigma.
n ( f ) 2 ] .times. I 1 = [ .sigma. ( f ) ] 2 .times. I 1 ( 7 )
##EQU00006##
[0057] Therefore, the received signal at the mobile station at a
given symbol duration is given by
y.sup.(0,f)=H.sub.w.sup.(0,f)L.sup.(0,f)x.sup.(0,f)+N.sup.(f)
(8)
[0058] Dynamic Power Allocation
[0059] In DAS-SFR, it is important to dynamically change the
frequency bands power of each DRU to cope with a dynamically
changing distribution of traffic and to balance the throughput in
each cell. Thus, it is necessary to dynamically change the
frequency bands power such that the maximum number of users in each
cell could be satisfied (number of users that can achieve the
targeted service bitrate). In this study we are interested in a
proper power allocation which maximizes the number of satisfied
users and their capacity. Without proper power allocation, there
may be cases of unbalanced capacity (throughput) where a few users
can have ultra-high throughput and most of the users have ultra-low
throughput. In some cases, for the existence of very large
interference, some users will be always unsatisfied. Therefore, a
proper power allocation can increase the throughput of the rest of
the users. However, the number of unsatisfied users' throughput
will be decreased.
[0060] III. Achievable Capacity of Distributed Antenna System
[0061] If we assume that the channel state information is known
only at the receiver (CSIR) and the channel is ergodic, the ergodic
Shannon capacity at a given location of the target mobile station
for the central cell can be calculated by
C ( f ) = E H w ( 0 , f ) .function. [ log 2 .times. .times. det
.function. ( I 1 + 1 [ .sigma. ( f ) ] 2 .times. ( H w ( 0 , f )
.times. L ( 0 , f ) ) .times. P ( 0 , f ) .function. ( H w ( 0 , f
) .times. L ( 0 , f ) ) H ) ] ( 9 ) ##EQU00007##
where P.sup.(0,f) is the covariance matrix of the transmitted
vector x and given by diag{P.sub.0.sup.(0,f), P.sub.1.sup.(0,f), .
. . , P.sub.6.sup.(0,f)}. If ergodicity of the channel is assumed,
the ergodic capacity can be obtained as
C ( f ) = E H w ( 0 , f ) .function. [ log 2 .function. ( 1 + 1 [
.sigma. ( f ) ] 2 .times. i = 0 6 .times. .times. h i ( 0 , f ) 2
.function. [ l i ( 0 , f ) ] 2 .times. P i ( 0 , f ) ) ] = .intg.
.gamma. f = 0 .infin. .times. log 2 .function. ( 1 + .gamma. f )
.times. f .gamma. f .function. ( .gamma. f ) .times. d .times.
.times. .gamma. f ( 10 ) ##EQU00008##
where
.gamma. f = 1 [ .sigma. ( f ) ] 2 .times. i = 0 6 .times. h i ( 0 ,
f ) 2 .function. [ l i ( 0 , f ) ] 2 .times. P i ( 0 , f )
##EQU00009##
is a weighted chi-squared distributed random variable with p.d.f
given by
f .gamma. f .function. ( .gamma. f ) = i = 0 6 .times. [ .sigma. (
f ) ] 2 .times. .pi. , [ l i ( 0 , f ) ] 2 .times. P i ( 0 , f )
.times. exp .function. ( - [ .sigma. ( f ) ] 2 .times. .gamma. f [
l i ( 0 , f ) ] 2 .times. P i ( 0 , f ) ) , .times. where .times.
.times. .pi. i = k = 0 , k .noteq. i 6 .times. [ l i ( 0 , f ) ] 2
.times. P i ( 0 , f ) [ l i ( 0 , f ) ] 2 .times. P i ( 0 , f ) - [
l i ( 0 , f ) ] 2 .times. P k ( 0 , f ) ( 11 ) ##EQU00010##
[0062] Then the ergodic capacity for MISO vector channel can be
obtained in a simple form by
MISO .times. : .times. C ( f ) = - 1 ln .times. 2 .times. i = 0 6
.times. .pi. i .times. exp .function. ( - [ .sigma. ( f ) ] 2 [ l i
( 0 , f ) ] 2 .times. P i ( 0 , f ) ) .times. E .times. i
.function. ( - [ .sigma. ( f ) ] 2 [ l i ( 0 , f ) ] 2 .times. P i
( 0 , f ) ) , .times. .times. f = F 1 , F 2 , F 3 ( 12 )
##EQU00011##
where, Ei(t) is the exponential integral function
( E .times. i .function. ( t ) = - .intg. - x .infin. .times. e - t
/ tdt ) ##EQU00012##
and can be easily calculated with popular numerical tools such as
MATLAB and MAPLE.
[0063] Since the derivation for this MISO vector channel is a
generalization of a SISO channel, the ergodic capacity for SISO
channel is given, respectively, by
SISO .times. : .times. C ( f ) = - 1 ln .times. 2 .times. exp
.function. ( - [ .sigma. ( f ) ] 2 [ l 0 ( 0 , f ) ] 2 .times. P 0
( 0 , f ) ) .times. E .times. i .function. ( - [ .sigma. ( f ) ] 2
[ l 0 ( 0 , f ) ] 2 .times. P 0 ( 0 , f ) ) , .times. f = F 1 , F 2
, F 3 ( 13 ) ##EQU00013##
[0064] Hence, the total ergodic capacity of the system can be
obtained by adding the capacity of the individual carriers,
C.sub.total=C.sup.(F.sup.1.sup.)+C.sup.(F.sup.2.sup.)+C.sup.(F.sup.3.sup-
.) (14)
where, for DAS-SFR at the central cell, [0065]
C.sup.(F.sup.1.sup.):MISO, C.sup.(F.sup.2.sup.):SISO,
C.sup.(F.sup.3.sup.):SISO for DAS-HFR3 (frequency reuse factor 3)
at the central cell, [0066] C.sup.(F.sup.1.sup.):MISO,
C.sup.(F.sup.2.sup.):nothing, C.sup.(F.sup.3.sup.):nothing for
DAS-FFR at the central cell, [0067] C.sup.(F.sup.1.sup.):MISO,
C.sup.(F.sup.2.sup.):MISO, C.sup.(F.sup.3.sup.):MISO
[0068] In the following section, we present the analytical and
numerical results using a simulation to corroborate the theoretical
analysis.
[0069] IV. Formulation of Power Allocation
[0070] In this section, we formulate the power allocation problem
to maximize the number of satisfied users and also maximize the
total satisfied users capacity.
[0071] For the problem formulation we consider a service area with
nineteen cells shown in FIG. 2.
[0072] In a multiusers scenario, we can directly map the ergodic
capacity of each user to what we obtained in section III depending
on the position and the power. Therefore, having a number of f
resource blocks assigned to user k(N.sub.k.sup.RB(f)), the real
throughput at user k can be written in terms of bps (bit per
second) as follow,
C k real .function. ( P ) = W R .times. B .times. i = 1 3 .times. N
k R .times. B .function. ( F i ) C k ( F i ) .function. ( P ) ( 15
) ##EQU00014##
where, W.sub.RB is the resource block bandwidth.
C.sub.k.sup.(F.sup.i.sup.)(P) is the ergodic capacity of user k
where P={P.sub.n.sup.(i,f)|n=0, 1, . . . , 6, i=0, 1, . . . , 18,
f=1, 2, 3}.
[0073] We consider the following key performance indicators (KPIs)
in the power allocation system: [0074] 1. KPI.sub.SU (Number of
Satisfied Users): We can derive a metric defining a percent of
satisfied users (i.e., users that can achieve the targeted service
bit rate, for example, 1 Mbits/s). The percent of satisfied users
(out of m users) would be,
[0074] KPI S .times. U .function. ( P ) = k = 1 m .times. G k
.function. ( P ) N u .times. s .times. e .times. r total ( 16 )
##EQU00015## where N.sub.user.sup.total is total number of users
and
G k .function. ( P ) = { 1 when C k r .times. e .times. a .times. l
.function. ( P ) > C t .times. h 0 otherwise ##EQU00016##
[0075] Using these equations, C.sub.th is a threshold capacity
(targeted service bit rate) and G.sub.k(P) is unity when the
capacity for a user (indexed by k) exceeds the threshold capacity
and is equal to zero when the capacity is less than or equal to the
threshold capacity. [0076] 2. KPL.sub.CSU (Capacity of Satisfied
Users): The total capacity of satisfied users would be,
[0076] KPI CSU .function. ( P ) = k .di-elect cons. S .times. U
.times. S .times. C k r .times. e .times. a .times. l .function. (
P ) ( W ( F 1 ) + W ( F 2 ) + W ( F 3 ) ) / 3 ( 17 ) ##EQU00017##
where W.sub.f is the bandwidth of frequency band f and
SUS={k|G.sub.k=1, k=1, 2, . . . , m} is the satisfied users set. If
more than three carriers are utilized in a cell, the number of
carriers and the divisor in the denominator will increase as
appropriate.
[0077] Now, our QoS function is the weighted combination of the two
KPIs (cost factors) which we have already introduced. Obviously our
objective function is to maximize the QoS function.
Maximize P .times. Q .times. o .times. S .function. ( P ) = w 1 KP
.times. I S .times. U .function. ( P ) + w 2 KP .times. I C .times.
S .times. U .function. ( P ) ( 18 ) ##EQU00018##
[0078] We can further simplify the objective functions in Eq.18
based on the following arguments:
[0079] Use round robin scheduling and equal bandwidth frequency for
all frequency bands, therefore, we can rewrite the real capacity in
Eq. 15. as,
C k real .function. ( P ) = W R .times. B .times. i = 1 3 .times. N
k R .times. B .function. ( F i ) C k ( F i ) .function. ( P )
.times. .fwdarw. Round .times. .times. Robin .times. W R .times. B
.times. i = 1 3 .times. N R .times. B ( F i ) N u .times. s .times.
e .times. r ( F i ) .times. C k ( F i ) .function. ( P ) .times.
.fwdarw. W RB .times. N RB ( F i ) = W ( F i ) .times. i = 1 3
.times. W ( F i ) N u .times. s .times. e .times. r ( F i ) .times.
C k ( F i ) .function. ( P ) .times. .fwdarw. W ( F 1 ) = W ( F 2 )
= W ( F 3 ) = W F .times. W F .times. i = 1 3 .times. C k ( F i )
.function. ( P ) N u .times. s .times. e .times. r ( F i ) ( 19 )
##EQU00019##
[0080] Where N.sub.user.sup.(f) is the number of users which can be
supported by frequency band f.
[0081] Since it is not practical to calculate the ergodic capacity
for the individual users, the aforementioned simplification is
valid for the theoretical analysis and cannot be extended to
practical applications. However, in practice, the number of the
satisfied users and therefore the KPIs, are found based on the real
users' throughput (C.sub.k.sup.real(P)) after the power allocation
procedure.
[0082] Note that, the optimization problem variable (P) is
171=19.times.9, where the first term in the product is due to the
fact that we have 19 cells, and the second term is because each
cell of DAS-SFR has 9 changeable user frequency band powers. These
9 changeable user frequency band powers are comprised of 6
frequency band powers for the edge DRUs and 3 frequency band powers
for central DRUs.
[0083] We decrease the optimization problem variable from 171 to 1
in such a way that only the central DRU's frequency bands power,
which are not assigned to the edge DRUs, are perturbed. The central
DRU's F.sub.2 and F.sub.3 power, which are not assigned to the edge
DRUs, are perturbed for the central cell (eNB0) in a DAS-SFR
configuration.
[0084] So the optimization problem is simplified to,
Maximize .DELTA. .times. .times. P .times. QoS .function. ( .DELTA.
.times. P ) = w 1 KP .times. I S .times. U .function. ( .DELTA.
.times. P ) + w 2 KP .times. I C .times. S .times. U .function. (
.DELTA. .times. .times. P ) ( 20 ) ##EQU00020##
where in DAS-SF,
.times. .DELTA. .times. .times. P .function. ( d .times. B ) = P '
.function. ( dBm ) - P .function. ( d .times. Bm ) ##EQU00021## P n
( i , f ) = { P .times. when ( n = 0 , 1 , .times. , 6 and i = 0 ,
8 , 1 .times. 0 , 1 .times. 2 , 1 .times. 4 , 1 .times. 6 , 1
.times. 8 and f = F 1 ) .times. .times. or ( n = 0 , 1 , .times. ,
6 and i = 1 , 3 , 5 , 9 , 1 .times. 3 , 1 .times. 7 and .times. f =
F 2 ) .times. .times. or ( n = 0 , 1 , .times. , 6 and i = 2 , 4 ,
6 , 7 , 1 .times. 1 , 1 .times. 5 and .times. f = F 3 ) , P ' when
( n = 0 and 1 , 2 , .times. , 7 , 9 , 11 , 13 , 1 .times. 5 , 1
.times. 7 and .times. f = F 1 ) .times. .times. or ( n = 0 and
.times. i = 0 , 2 , 4 , 6 , 7 , 8 , 1 .times. 0 , 1 .times. 1 , 1
.times. 2 , 1 .times. 4 , 1 .times. 5 , 1 .times. 6 , 1 .times. 8
and f = F 2 ) .times. .times. or ( n = 0 and i = 0 , 1 , 3 , 5 , 8
, 9 , 1 .times. 0 , 1 .times. 2 , 1 .times. 3 , 1 .times. 4 , 1
.times. 6 , 1 .times. 7 , 1 .times. 8 and f = F 3 ) , 0 otherwise
.times. .times. .times. KPI S .times. U .function. ( .DELTA.
.times. P ) = k - 1 m .times. G k .function. ( .DELTA. .times. P )
N u .times. s .times. e .times. r total .times. .times. where
.times. .times. .times. G k .function. ( .DELTA. .times. P ) = { 1
when .times. .times. i = 1 3 .times. C k ( F i ) .function. (
.DELTA. .times. P ) N u .times. s .times. e .times. r ( F i ) >
C th W F , 0 otherwise .times. .times. .times. KP .times. I C
.times. S .times. U .function. ( .DELTA. .times. P ) = k .di-elect
cons. S .times. U .times. S .times. i = 1 3 .times. C k ( F i )
.function. ( .DELTA. .times. P ) N u .times. s .times. e .times. r
( F i ) ##EQU00021.2##
[0085] In our analysis, we assume that P is fixed and only P'
changes in magnitude.
[0086] In multiuser systems, we need to consider the different
resource allocation scenarios which were defined in section II.
B
[0087] In LTE systems, eNB distinguishes between the interior and
the exterior users based on their corresponding uplink power
received at the central DRU. Particularly in DAS-SFR, since none of
the DRUs except the central DRU operates in F.sub.2 and F.sub.3, it
is possible to apply the above-mentioned method (distinguishing
between the interior and the exterior users) using the received
CQIs (Channel Quality Indicator) from F.sub.2 and F.sub.3. To
implement these techniques, we propose a threshold T.sub.p as a
parameter in the eNB such that users with uplink power higher than
T.sub.p are assigned as interior users, and vice versa. In a
DAS-SFR, T.sub.p can play the same role as a threshold for CQI such
that users with CQI higher than T.sub.p are assigned as interior
users, and vice versa.
[0088] A. The Power Self-Optimization Algorithm
[0089] According to the above intuitive analysis, we propose a
power self-optimization (PSO) technique which is based on a simple
and decentralized algorithm that runs on the application layer.
[0090] In the PSO algorithm, the expected network gain, which is
based on one or both system KPIs, is used in order to determine
whether to increase or decrease the transmission power of the
central DRUs. To do so, the PSO technique uses the KPI associated
with each eNB to compute the system KPI. Finally, the central DRUs
are in charge of adjusting .DELTA.P based on system KPI by
performing the PSO algorithm. FIG. 4 depicts the block diagram of
the self-optimization algorithm. As illustrated in FIG. 4, both
KPIs are functions of .DELTA.P.
[0091] Observing the block diagram shown in FIG. 4, it is possible
to note that the transmission power is adjusted by comparing the
current KPI, calculated at the end of current phase, and the last
KPI, calculated at the end of last phase. Moreover, it is important
to highlight that the central DRUs have a predefined minimum and
maximum transmission power (p.sup.min and p.sup.max), which cannot
be exceeded by the algorithm. Thus, the self-optimization algorithm
increases or decreases the .DELTA.P step-by-step by p(dB) for each
central DRUs. Parameter t can take two values, 1 and -1, where 1
shows that algorithm starts by increasing the power level.
Conversely, -1 indicates that the algorithm starts by decreasing
the power level. Since we do not want the power to oscillate around
the optimal power forever, we define the parameter c to help the
algorithm stop.
[0092] Whenever the algorithm starts off by increasing the power
level, the central DRUs increase the .DELTA.P by the fixed
parameter p. The central DRU keep increasing the power by the fixed
parameters p as long as the current calculated KPI.sub.SU is
greater than the last calculated KPI.sub.SU. If the current
calculated KPI.sub.SU is equal to last calculated KPI.sub.SU, the
central DRUs keep increasing the power as long as the current
calculated KPI.sub.CSU is not smaller than the last calculated
KPI.sub.CSU, otherwise it decreases its power level. Note that
whenever the algorithm starts off by increasing the power level,
all the above mentioned statements should be reversed i.e. the
decreasing behavior should be changed to an increasing behavior and
vice versa.
[0093] The PSO algorithm seeks to maximize the number of satisfied
users meanwhile it seeks to maximize the capacity of the satisfied
users in order to have a better QoS. Even though some embodiments
do not achieve an optimal solution, the methods described herein
provide stable power updates toward the optimal solution. In other
embodiments, the optimal solution is obtained.
[0094] Referring to FIG. 4, KPI.sub.SU is the Key Performance
Indicator for Satisfied Users and KPI.sub.CSU is the key
performance indicator for the Capacity of Satisfied Users. .DELTA.P
is the change in power of the carriers. By adjusting the power of
the carriers, the number of satisfied users and the capacity of the
satisfied users can be increased or optimized. Initially, t is set
to 1, c is set to zero, .DELTA.P=0, and p=1 (i.e., the power
increments are made in 1 dBm steps). In the illustrated embodiment,
the maximum and minimum values of power (measured in dBm in an
embodiment) are 20 and -10, respectively. In some implementations,
the maximum and minimum power are set by the user and the values
provided herein are merely given by way of example. Thus, depending
on the system parameters, different values will be utilized for the
maximum and minimum power. One of ordinary skill in the art would
recognize many variations, modifications, and alternatives.
[0095] A measurement of the KPI.sub.SU given .DELTA.P (initially
zero, for which the power of the various carriers is equal) is made
and the result is assigned to KPI*.sub.SU. Thus, the performance
for the users in a given cell is measured to determine the number
of satisfied users in the cell. The capacity for the satisfied
users is also measured at this value of .DELTA.P (KPI.sub.CSU given
.DELTA.P) and assigned to KPI*.sub.CSU.
[0096] The difference in power is then modified (.DELTA.P+(t*p)) in
order to iterate on the difference in power. t is an updating index
that has values of positive or negative one, indicating if the
difference in power is being increased or decreased. Referring to
FIG. 4, movement through the left hand side of the loop results in
increases in power and movement through the right hand side of the
loop results in decreases in power.
[0097] In order to determine if the power is in the correct range,
a comparison is made between .DELTA.P and the maximum power
(.DELTA.P>p.sup.max), between .DELTA.P and the minimum power
(.DELTA.P<p.sup.min), and that an oscillation indicator (c) is
not reached. If any of these conditions are true, then the method
is terminated. Otherwise, if the power is within the predetermined
range (less than maximum power and greater than the minimum power)
and oscillation has not been detected, the method continues.
[0098] A measurement is made of the number of satisfied users given
the new .DELTA.P (KPI.sub.SU(.DELTA.P)) and this measured value is
compared to the previous number of satisfied users. If the change
in power (an increase in this example) has resulted in a decrease
in the number of satisfied users, then the right hand loop is used
to toggle the updating index (t), which will enable the power to be
decreased in the subsequent flow.
[0099] If, on the other hand, the number of satisfied users given
the new .DELTA.P is greater than or equal to the previous number of
satisfied users, indicating no change or an increase in the number
of satisfied users, the method proceeds to the next comparison to
determine if the number of satisfied users given the new .DELTA.P
is equal to the previous number of satisfied users. If the
comparison is not equal, then the left hand side of the loop is
used to increase the power differential in the subsequent flow.
[0100] If the number of satisfied users given the new .DELTA.P is
equal to the previous number of satisfied users, then a measurement
is made of the capacity of the satisfied users and this value is
compared to the previous capacity. If the measured capacity is less
than the previous capacity, the right hand side of the loop is used
to decrease the power differential in the subsequent flow. If the
measured capacity is greater than or equal to the previous
capacity, then the left hand side of the loop is used to increase
the power differential in the subsequent flow.
[0101] Referring to FIG. 1, the method illustrated in FIG. 4 will
be applied in relation to the carriers used in the central antenna
(eNB0) of the cell (i.e., hexagon). For each cell, the carrier used
in the peripheral portions of the cell will be used as a reference
and the other carriers will have their power set by optimizing the
number and capacity of satisfied users using the algorithm
described herein. In some embodiments, the carriers used in the
central antenna that are not used in the peripheral portions of the
cell will have the same power, providing a single .DELTA.P for the
central antenna with the carrier used in the peripheral portions of
the cell providing the reference. In some implementations, the
carriers used only in the central antenna can have differing powers
with the algorithm applied to the carriers individually (e.g.,
F.sub.1 compared to F.sub.3 and F.sub.2 compared to F.sub.3 for the
rightmost cell in FIG. 1A).
[0102] Referring to FIG. 1A, F.sub.1 is the reference for the top
left cell, F.sub.2 is the reference for the bottom left cell, and
F.sub.3 is the reference for the rightmost cell. One of ordinary
skill in the art would recognize many variations, modifications,
and alternatives. Embodiments of the present invention provide
methods and systems in which the number of carriers in a cell can
be increased, thereby increasing bandwidth. The algorithm is then
used to set the power level of the added carriers to a level that
reduced interference with adjacent cells to an acceptable
level.
[0103] It should be appreciated that the specific steps illustrated
in FIG. 4 provide a particular method of increasing a number and
capacity of satisfied users by varying power between carriers
according to an embodiment of the present invention. Other
sequences of steps may also be performed according to alternative
embodiments. For example, alternative embodiments of the present
invention may perform the steps outlined above in a different
order. Moreover, the individual steps illustrated in FIG. 4 may
include multiple sub-steps that may be performed in various
sequences as appropriate to the individual step. Furthermore,
additional steps may be added or removed depending on the
particular applications. One of ordinary skill in the art would
recognize many variations, modifications, and alternatives.
[0104] V. Analytical and Simulation Results
[0105] FIGS. 5A-5B represent the ergodic capacity of a cellular
DAS's central cell for different frequency reuse techniques versus
the normalized distance from the eNB0 DRU0 in the direction of the
worst case position X, for a path loss exponent of 3.76. Each
scenario is plotted for the individual capacities
C.sup.(F.sup.1.sup.), C.sup.(F.sup.2.sup.), C.sup.(F.sup.3.sup.)
and also for the total capacity C.sub.total. These figures show an
interesting non-monotonic relationship between capacity and the
normalized distance from the base station. This is due to the fact
that the signal from a distributed antenna module becomes dominant
around 0.6R.
[0106] As it can be observed in FIG. 5A, when applying the SFR
methods, by increasing .DELTA.P from -10 dB to 20 dB, the central
cell's C.sup.(F.sup.2.sup.) and C.sup.(F.sup.3.sup.) increase.
This, however, increases the interference associated with the edge
DAUs of the neighboring cells which are using F.sub.2 and F.sub.3
as their main frequency band. It is necessary to note that,
increasing .DELTA.P from -10 dB to 20 dB, significantly increases
the associated interference with the F.sub.1 frequency band in the
central cell, imposed from the neighboring cells, and thus
decreases the central cell's C.sup.(F.sup.1.sup.).
[0107] It is important to notice that, considering SFR methods, as
power increases, C.sub.total does not change harmonically, which
means the ergodic capacity associated with the cell's interior
regions increases, and that of the cell's exterior regions
decreases. Therefore, the users' distribution within the cell's
area plays a significant role when deciding the optimal
.DELTA.P.
[0108] A secondary consideration when deciding the optimal .DELTA.P
is the minimum required capacity (C.sub.th). As an example, with a
high C.sub.th (ergodic capacity=20 bit/Hz in FIGS. 5A and 5B), as
.DELTA.P increases, a wider radiance in a cell will be covered by
ergodic capacity higher than 20. With a low C.sub.th (ergodic
capacity=3 bit/Hz in FIGS. 5A and 5B), as .DELTA.P increases, a
shorter radiance in the cells will be covered by ergodic capacity
higher than 3.
[0109] The FFR method fully uses the frequency bands, therefore,
the cell's interior regions' achieved an ergodic capacity higher
than ergodic capacity in the cell's interior regions when applying
the HFR3 method. For example, at ergodic capacity=20, the FFR
method outperforms the HFR3 method, considering the users'
satisfaction probability. However, when applying the FFR method,
due to the interference caused by the neighboring cells, the edge
cells frequently experience dead spots. As an example, considering
the users' satisfaction probability, for ergodic capacity=3, the
HFR3 method outperforms the FFR method.
[0110] FIGS. 6A-D and 7A-7D demonstrate the two KPI.sub.SU and
KPI.sub.CSU for different .DELTA.P, considering four different user
distributions. The only parameter that is different in the
aforementioned figures is their C.sub.th, i.e. we consider low
C.sub.th=0.01 W.sub.RB and high C.sub.th=0.07W.sub.RB, in FIGS.
6A-6D and FIGS. 7A-7D, respectively. In theoretical analysis, the
interior region is distinguished from the exterior region, based on
T.sub.p. In other words, the region with ergodic capacity higher
than T.sub.p is considered as interior region, and the region with
ergodic capacity lower than T.sub.p is considered as exterior
region. This T.sub.p is associated to the region's ergodic capacity
of the frequency bands that are only allocated to the central DRUs.
We assume T.sub.p=2 (bit/Hz) in our theoretical analyses.
[0111] Since both KPI functions are dependent on G.sub.k, it is
reasonable to consider each of these functions as a criteria to
measure the QoS. We analyze two different cases, i.e. (w.sub.1=1,
w.sub.2=0) and (w.sub.1=0, w.sub.2=1). In the first case, we
presume KPI.sub.SU as our QoS function whereas in the second case
we consider KPI.sub.CSU as our QoS function.
[0112] We define our user distributions as depicted in Table 1 and
FIG. 8 where
N u .times. s .times. e .times. r total = i .times. X i .times. S i
, i .di-elect cons. { region .times. .times. A , region .times.
.times. B , region .times. .times. C , region .times. .times. D } ,
##EQU00022##
S.sub.i is the area of region i and X.sub.i=(# users of region
i)/S.sub.i. We perform Monte Carlo simulations to corroborate the
analytical results. It is assumed that the total number of users
(N.sub.user.sup.total) is 200.
[0113] As it is seen in FIGS. 6A-6D, when C.sub.th takes a low
value, i.e. C.sub.th=0.01 W.sub.RB, except for the FFR method,
applying the rest of the frequency reuse methods (HFR3, SFR)
results in the highest number of the satisfied users (KPI.sub.SU).
Note that, as .DELTA.P increases, when applying DAS-SFR, the number
of the satisfied users asymptotically decreases. The above
mentioned results hold for all four different user distributions:
FIG. 6A: User's Distribution=Uniformity; FIG. 6B: User's
Distribution=Dense at the Center; FIG. 6C: User's
Distribution=Dense at the middle of Center and Edge Cell; and FIG.
6D: User's Distribution=Dense at the Edge Cell.
[0114] As was shown in FIGS. 6A-6D, there exists an optimal
.DELTA.P at which the KPI.sub.CSU is maximum, for all four
different distributions. For instance, when applying the
DAS-SFR-Scenario 3 method, for the UD, DCD, DCED and DED, the
maximum KPI.sub.CSU happens at .DELTA.P=-5 dB, 2 dB, 8 dB, and -4
dB, respectively.
[0115] One has to consider, the optimal .DELTA.P is different for
dissimilar distribution scenarios. Moreover, the DAS-SFR-Scenario 3
method outperforms the other two DAS-SFR methods, for all the
distributions under consideration.
[0116] FIGS. 7A-7D reveal that, when C.sub.th takes a large value,
i.e. C.sub.th=0.07W.sub.RB, the FFR method outperforms the HFR3
method, considering the number of the satisfied users (KPI.sub.SU)
This corroborates our analytical results from FIGS. 5A-5B, as it
was explained previously. However, the DAS-SFR-Scenario 2 method
outperforms all the other methods, at different optimal .DELTA.P
values for dissimilar distribution scenarios.
[0117] As it can be perceived from FIGS. 7A-7D, there exists an
optimal .DELTA.P at which the KPI.sub.SU and KPI.sub.CSU are
maximum, for all four different distributions: FIG. 7A: User's
Distribution=Uniformity; FIG. 7B: User's Distribution=Dense at the
Center; FIG. 7C: User's Distribution=Dense at the middle of Center
and Edge Cell; and FIG. 7D: User's Distribution=Dense at the Edge
Cell. As an example, when applying the DAS-SFR-Scenario 2 method,
for the UD, DCD, DCED and DED, the maximum KPI.sub.SU and
KPI.sub.CSU happen at .DELTA.P=6 dB, 4 dB, -2 dB, and 13 dB,
respectively.
[0118] Note that, the optimal .DELTA.P is different for different
distribution scenarios. Moreover, the DAS-SFR-Scenario 2 method
outperforms the other two SFR methods, for all distributions under
consideration. Since the DAS-SFR-Scenario 2 uses all the frequency
bands in the interior cell region, along with the fact that the
users with throughput above the C.sub.th are mainly located in the
interior cell region, leads to the final conclusion that
DAS-SFR-Scenario 2 outperforms the other methods.
[0119] The capacity of the above mentioned architectures is also
investigated through system level simulations. We consider the
two-ring hexagonal cellular system with nineteen eNBs, such that
each cell has 7 DRUs, as depicted in FIG. 2, where the eNBs
distance is 500 meters. The 200 UEs are distributed for 4 user
distribution methods which are defined in Table 1. An eNB allocates
the available RBs to UEs by estimating the signaling and uplink
power of UEs. We use the simulation parameters listed in Table
2.
[0120] At a TTI (Transmission Time Interval) for the simulation,
the eNB in a cell gathers the CQI (Channel Quality Indicator)
information of UEs and allocates the RBs to each UE, using the
Round Robin scheduling technique. The throughput of a UE is
obtained based on the SINR of the UE in the assigned RB. In system
level simulation, SINR is determined by the path loss and lognormal
fading measured in RB. The throughput of a UE.sub.m is estimated
using the Shannon capacity as follows
C.sub.m.sup.(f)=W.sub.RB.sub.(f)log(1+SINR.sub.m.sup.(f)),
f=F.sub.1,F.sub.2,F.sub.3 (21)
where, W.sub.RB.sub.(f) is the bandwidth of RBs assigned to a UE
and SINR.sub.m.sup.(f) is the SINR of a UE.sub.m. The cell capacity
in each region is the total throughput of UEs in the corresponding
region and is expressed as follows
C t .times. o .times. t .times. a .times. l = i = 1 3 .times. m = 1
M .times. C m ( F i ) ( 22 ) ##EQU00023##
[0121] Where M is the number of UEs in a group.
[0122] The presented numerical results corroborate the analytical
results depicted in FIG. 6 and FIG. 7.
[0123] Embodiments of the present invention provide a new cell
architecture combining two inter-cell interference mitigation
techniques, Distributed Antenna System and Soft Frequency Reuse, to
improve cell edge user's throughput when the system has full
spectral efficiency. A power self-optimization algorithm that aims
at maximizing the number of satisfied users while trying to
increase their capacity was also proposed. In more detail, the
self-optimization algorithm uses the KPIs computed by the server in
the last phase and current phase to adjust the power level for the
next phase.
[0124] An analytical framework is derived to evaluate the user
throughput leading to tractable expressions. A natural extension of
this work is to address the cellular uplink. The overall capacity
increases by using the SFR technique, since the spectral efficiency
in the interior region is higher than that in the exterior region
when compared to HFR3 technique. The cell edge user's throughput
increases by using the SFR technique; since the interference signal
from neighbor cells is lower than that the time we use FFR
technique.
[0125] Analytical and simulation results demonstrated the advantage
of using the self-optimization algorithm instead of setting a fixed
power level. When a DAS-SFR without the PSO algorithm is
considered, the transmission power is set at the beginning of the
communication and remains the same during its entire network
lifetime. This characteristic can be negative considering a DAS-SFR
in a real environment where the inherent user distribution is not
constant.
[0126] Due to the fact that the inherent environment user
distribution is completely variable, the PSO algorithm always
guarantees the maximum number of satisfied users during the
communication, while the algorithm serves to maximize their
capacity as well.
TABLE-US-00001 TABLE 1 X.sub.i/n, i .di-elect cons. {A, B, C, D}
R.sub.1 R.sub.2 UD.sup.(1) DCD.sup.(2) DCED.sup.(3) DED.sup.(4)
Region A 0 0.25 1 7 1 1 Region B 0.25 0.50 1 1 7 1 Region C 0.50
0.75 1 1 1 1 Region D 0.75 1 1 1 1 7 .sup.(1)Uniform Distribution
.sup.(2)DCD: Dense at the Center Distribution .sup.(3)DCED: Dense
at the mid. of Cent. And Edge cell Distribution .sup.(4)DED: Dense
at the Edge cell Distribution
TABLE-US-00002 TABLE 2 Simulation Parameters PARAMETERS VALUE
Channel Bandwidth for each 5 MHz Frequency Part Carrier Frequency
2.14 GHz FFT size 1024 Number of Resource Blocks 25 for each
Frequency Part Subcarrier Spacing 15 kHz Cellular Layout Hexagonal
grid, 19 sites Inter-eNB Distance 500 meters Log-normal Shadowing 8
dB Propagation loss 128.1 + 37.6 log.sub.10(R(km)) White Noise
Power Density -174 dBm/Hz Scheduling Round Robin TTI 1 ms T.sub.p
(CQI) 2 CQI
* * * * *