U.S. patent application number 15/621177 was filed with the patent office on 2018-12-13 for massive multiple-input multiple-output (m-mimo) wireless distribution system (wds) and related methods for optimizing the m-mimo wds.
The applicant listed for this patent is Corning Incorporated. Invention is credited to Xiaojun Liang, Anthony Ng' Oma.
Application Number | 20180359006 15/621177 |
Document ID | / |
Family ID | 62791831 |
Filed Date | 2018-12-13 |
United States Patent
Application |
20180359006 |
Kind Code |
A1 |
Liang; Xiaojun ; et
al. |
December 13, 2018 |
MASSIVE MULTIPLE-INPUT MULTIPLE-OUTPUT (M-MIMO) WIRELESS
DISTRIBUTION SYSTEM (WDS) AND RELATED METHODS FOR OPTIMIZING THE
M-MIMO WDS
Abstract
Embodiments of the disclosure relate to a massive multiple-input
multiple-output (M-MIMO) wireless distribution system (WDS) and
related methods for optimizing the M-MIMO WDS. In one aspect, the
M-MIMO WDS includes a plurality of remote units each deployed at a
location and includes one or more antennas to serve a remote
coverage area. At least one remote unit can have a different number
of the antennas from at least one other remote unit in the M-MIMO
WDS. In another aspect, a selected system configuration including
the location and number of the antennas associated with each of the
remote units can be determined using an iterative algorithm that
maximizes a selected system performance indicator of the M-MIMO
WDS. As such, it may be possible to optimize the selected system
performance indicator at reduced complexity and costs, thus helping
to enhance user experiences in the M-MIMO WDS.
Inventors: |
Liang; Xiaojun; (Painted
Post, NY) ; Ng' Oma; Anthony; (Horseheads,
NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Corning Incorporated |
Corning |
NY |
US |
|
|
Family ID: |
62791831 |
Appl. No.: |
15/621177 |
Filed: |
June 13, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04W 88/085 20130101;
H04B 7/0608 20130101; H04W 88/12 20130101; H04B 7/0682 20130101;
H04B 7/0691 20130101; H04B 7/024 20130101; H04B 7/0413 20130101;
H04B 7/02 20130101 |
International
Class: |
H04B 7/0413 20060101
H04B007/0413; H04B 7/06 20060101 H04B007/06; H04B 7/024 20060101
H04B007/024 |
Claims
1. A massive multiple-input multiple-output (M-MIMO) wireless
distribution system (WDS), comprising: a plurality of remote units
each configured to be deployed at a location to serve a respective
remote coverage area; and a central unit communicatively coupled to
each of the plurality of remote units over a communications link
among a plurality of communications links, the central unit
configured to: encode a received downlink communications signal to
generate a downlink MIMO communications signal; and distribute the
downlink MIMO communications signal over the plurality of
communications links to the plurality of remote units; each of the
plurality of remote units comprising one or more antennas
configured to distribute the downlink MIMO communications signal to
at least one client device located in the respective remote
coverage area; and at least one remote unit among the plurality of
remote units comprises a different number of antennas from at least
one other remote unit among the plurality of remote units.
2. The M-MIMO WDS of claim 1, wherein each of the plurality of
remote units is further configured to form at least one radio
frequency (RF) beam to distribute the downlink MIMO communications
signal to the at least one client device located in the respective
remote coverage area.
3. The M-MIMO WDS of claim 1, wherein the location corresponding to
each of the plurality of remote units is determined based on client
device density distribution throughout a coverage area of the
M-MIMO WDS to maximize system capacity of the M-MIMO WDS.
4. The M-MIMO WDS of claim 1, wherein the at least one remote unit
among the plurality of remote units is configured to include a
higher number of the antennas than the at least one other remote
unit among the plurality of remote units when the respective remote
coverage area served by the at least one remote unit has a higher
client device density than the respective remote coverage area
served by the at least one other remote unit among the plurality of
remote units.
5. The M-MIMO WDS of claim 1, wherein the at least one remote unit
among the plurality of remote units is configured to include a
lesser number of the antennas than the at least one other remote
unit among the plurality of remote units when the respective remote
coverage area served by the at least one remote unit has a lower
client device density than the respective remote coverage area
served by the at least one other remote unit among the plurality of
remote units.
6. The M-MIMO WDS of claim 1, wherein: each of the plurality of
remote units is further configured to: receive at least one uplink
communications signal from the at least one client device located
in the respective remote coverage area; and provide the at least
one uplink communications signal to the central unit over the
communications link among the plurality of communications links;
and the central unit is further configured to combine a plurality
of uplink communications signals received from the plurality of
remote units.
7. The M-MIMO WDS of claim 6, wherein: the central unit comprises
electrical-to-optical (E/O) converters configured to convert the
downlink MIMO communications signal into a plurality of downlink
optical fiber-based MIMO communications signals for distribution to
the plurality of remote units over a downlink optical fiber-based
communications medium; the plurality of remote units comprises:
remote unit optical-to-electrical (O/E) converters configured to
convert the plurality of downlink optical fiber-based MIMO
communications signals into the downlink MIMO communications
signal; and remote unit E/O converters configured to convert the
plurality of uplink communications signals into a plurality of
uplink optical fiber-based communications signals; and the central
unit further comprises 0/E converters configured to convert the
plurality of uplink optical fiber-based communications signals
received from the plurality of remote units into the plurality of
uplink communications signals.
8. A method for optimizing a selected performance indication of a
massive multiple-input multiple-output (M-MIMO) wireless
distribution system (WDS), comprising: generating an initial system
configuration based on at least one initial system parameter of a
M-MIMO WDS, wherein the initial system configuration comprises a
plurality of configuration parameter groups corresponding to a
plurality of remote units in the M-MIMO WDS, respectively;
providing the plurality of configuration parameter groups to a
performance-estimation function configured to estimate a selected
system performance indicator of the M-MIMO WDS based on the
plurality of configuration parameter groups; generating an initial
estimation of the selected system performance indicator by the
performance-estimation function according to the initial system
configuration; updating one or more selected configuration
parameter groups among the plurality of configuration parameter
groups to generate at least one updated system configuration;
providing the plurality of configuration parameter groups
comprising the one or more selected configuration parameter groups
to the performance-estimation function; generating at least one
updated estimation of the selected system performance indicator by
the performance-estimation function according to the at least one
updated system configuration; determining a selected system
configuration between the initial system configuration and the at
least one updated system configuration corresponding to a higher
selected system performance indicator among the initial estimation
of the selected system performance indicator and the at least one
updated estimation of the selected system performance indicator;
and configuring the plurality of remote units based on the selected
system configuration.
9. The method of claim 8, further comprising outputting the
selected system configuration for configuring the plurality of
remote units based on the selected system configuration.
10. The method of claim 8, further comprising: providing the
plurality of configuration parameter groups to the
performance-estimation function configured to estimate a system
capacity of the M-MIMO WDS based on the plurality of configuration
parameter groups; estimating an initial system capacity for the
initial system configuration by the performance-estimation
function; updating the one or more selected configuration parameter
groups among the plurality of configuration parameter groups to
generate the at least one updated system configuration; providing
the plurality of configuration parameter groups comprising the one
or more selected configuration parameter groups to the
performance-estimation function; estimating at least one updated
system capacity for the at least one updated system configuration
by the performance-estimation function; determining the selected
system configuration between the initial system configuration and
the at least one updated system configuration corresponding to a
higher system capacity among the initial system capacity and the at
least one updated system capacity; and configuring the plurality of
remote units based on the selected system configuration.
11. The method of claim 10, further comprising outputting the
selected system configuration for configuring the plurality of
remote units based on the selected system configuration.
12. The method of claim 8, wherein the at least one initial system
parameter comprises one or more system parameters selected from the
group consisting of: total number of antennas; total number of
remote units; system layout; client device density distribution;
and coverage area radio frequency (RF) survey.
13. The method of claim 8, wherein each of the plurality of
configuration parameter groups comprises: location coordinates of a
corresponding remote unit among the plurality of remote units; and
a number of antennas to be provided in the corresponding remote
unit among the plurality of remote units.
14. The method of claim 8, wherein the performance-estimation
function is proportionally related to a weighting function
representing one or more spatial dependent parameters selected from
the group consisting of: system layout information; client device
density distribution; and signal propagation environment.
15. A non-transitory computer-readable medium comprising software
with instructions configured to: generate an initial system
configuration based on at least one initial system parameter of a
massive multiple-input multiple-output (M-MIMO) wireless
distribution system (WDS), wherein the initial system configuration
comprises a plurality of configuration parameter groups
corresponding to a plurality of remote units in the M-MIMO WDS,
respectively; provide the plurality of configuration parameter
groups to a performance-estimation function configured to estimate
a selected system performance indicator of the M-MIMO WDS based on
the plurality of configuration parameter groups; generate an
initial estimation of the selected system performance indicator by
the performance-estimation function according to the initial system
configuration; update one or more selected configuration parameter
groups among the plurality of configuration parameter groups to
generate at least one updated system configuration; provide the
plurality of configuration parameter groups comprising the one or
more selected configuration parameter groups to the
performance-estimation function; generate at least one updated
estimation of the selected system performance indicator by the
performance-estimation function according to the at least one
updated system configuration; and determine a selected system
configuration between the initial system configuration and the at
least one updated system configuration corresponding to a higher
selected system performance indicator among the initial estimation
of the selected system performance indicator and the at least one
updated estimation of the selected system performance
indicator.
16. The non-transitory computer-readable medium of claim 15,
wherein the instructions are further configured to output the
selected system configuration for configuring the plurality of
remote units based on the selected system configuration.
17. The non-transitory computer-readable medium of claim 15,
wherein the instructions are further configured to: provide the
plurality of configuration parameter groups to the
performance-estimation function configured to estimate a system
capacity of the M-MIMO WDS based on the plurality of configuration
parameter groups; estimate an initial system capacity for the
initial system configuration by the performance-estimation
function; update the one or more selected configuration parameter
groups among the plurality of configuration parameter groups to
generate the at least one updated system configuration; provide the
plurality of configuration parameter groups comprising the one or
more selected configuration parameter groups to the
performance-estimation function; estimate at least one updated
system capacity for the at least one updated system configuration
by the performance-estimation function; and determine the selected
system configuration between the initial system configuration and
the at least one updated system configuration corresponding to a
higher system capacity among the initial system capacity and the at
least one updated system capacity.
18. The non-transitory computer-readable medium of claim 17,
further comprising outputting the selected system configuration for
configuring the plurality of remote units based on the selected
system configuration.
19. The non-transitory computer-readable medium of claim 15,
wherein the at least one initial system parameter comprises one or
more system parameters selected from the group consisting of: total
number of antennas; total number of remote units; system layout;
client device density distribution; and coverage area radio
frequency (RF) survey.
20. The non-transitory computer-readable medium of claim 15,
wherein each of the plurality of configuration parameter groups
comprises: location coordinates of a corresponding remote unit
among the plurality of remote units; and number of antennas to be
provided in the corresponding remote unit among the plurality of
remote units.
21. The non-transitory computer-readable medium of claim 15,
wherein the performance-estimation function is proportionally
related to a weighting function representing one or more spatial
dependent parameters selected from the group consisting of: system
layout information; client device density distribution; and signal
propagation environment.
Description
BACKGROUND
[0001] The disclosure relates generally to optimizing performance
of a wireless distribution system (WDS), and more particularly to
enhancing WDS system capacity by reducing radio frequency (RF)
interference among multiple users and among multiple antenna.
[0002] Wireless customers are increasingly demanding digital data
services, such as streaming video signals. At the same time, some
wireless customers use their wireless communications devices in
areas that are poorly serviced by conventional cellular networks,
such as inside certain buildings or areas where there is little
cellular coverage. One response to the intersection of these two
concerns has been the use of DASs. DASs include remote units
configured to receive and transmit communications signals to client
devices within an antenna range of the remote units. DASs can be
particularly useful when deployed inside buildings or other indoor
environments where the wireless communications devices may not
otherwise be able to effectively receive RF signals from a
source.
[0003] In this regard, FIG. 1 illustrates a distribution of
communications services to remote coverage areas 100(1)-100(N) of a
WDS provided in the form of a DAS 102, wherein `N` is the number of
remote coverage areas. These communications services can include
cellular services, wireless services, such as RF identification
(RFID) tracking, Wireless Fidelity (Wi-Fi), local area network
(LAN), and wireless LAN (WLAN), wireless solutions (Bluetooth,
Wi-Fi Global Positioning System (GPS), signal-based, and others)
for location-based services, and combinations thereof, as examples.
The remote coverage areas 100(1)-100(N) may be remotely located. In
this regard, the remote coverage areas 100(1)-100(N) are created by
and centered on remote units 104(1)-104(N) connected to a central
unit 106 (e.g., a head-end equipment, a head-end controller, or a
head-end unit). The central unit 106 may be communicatively coupled
to a signal source 108, for example, a base transceiver station
(BTS) or a baseband unit (BBU). In this regard, the central unit
106 receives downlink communications signals 110D from the signal
source 108 to be distributed to the remote units 104(1)-104(N). The
remote units 104(1)-104(N) are configured to receive the downlink
communications signals 110D from the central unit 106 over a
communications medium 112 to be distributed to the respective
remote coverage areas 100(1)-100(N) of the remote units
104(1)-104(N). Each of the remote units 104(1)-104(N) may include
an RF transmitter/receiver and at least one respective antenna
114(1)-114(N) operably connected to the RF transmitter/receiver to
wirelessly distribute the communications services to client devices
116 within the respective remote coverage areas 100(1)-100(N). The
remote units 104(1)-104(N) are also configured to receive uplink
communications signals 110U from the client devices 116 in the
respective remote coverage areas 100(1)-100(N) to be distributed to
the signal source 108. The size of each of the remote coverage
areas 100(1)-100(N) is determined by the amount of RF power
transmitted by the respective remote units 104(1)-104(N), receiver
sensitivity, antenna gain, and RF environment, as well as by RF
transmitter/receiver sensitivity of the client devices 116. The
client devices 116 usually have a fixed maximum RF receiver
sensitivity, so that the above-mentioned properties of the remote
units 104(1)-104(N) mainly determine the size of the respective
remote coverage areas 100(1)-100(N).
[0004] The client devices 116 in any of the remote coverage areas
100(1)-100(N) may be running bandwidth-hungry applications, such as
high-definition (HD) mobile video, virtual reality (VR), and
augmented reality (AR), that drive the demand for high-capacity
wireless access. Moreover, multiple client devices 116 may be
running such bandwidth-hungry applications concurrently, thus
further increasing the demand for data throughput in each of the
remote coverage areas 100(1)-100(N). As a result, the wireless
communications industry has adopted multiple-input multiple-output
(MIMO) technology to help meet the increasing bandwidth demand by
the client devices 116. In this regard, each of the remote units
104(1)-104(N) may employ multiple antennas to distribute multiple
streams of the downlink communications signals 110D concurrently.
For example, each of the remote units 104(1)-104(N) may employ two
antennas to concurrently transmit two streams of the downlink
communications signals 110D, thus doubling the data throughput in
the remote coverage areas 100(1)-100(N). When the remote units
104(1)-104(N) distribute the multiple streams of the downlink
communications signals 110D concurrently to multiple client devices
116, the remote units 104(1)-104(N) are said to be communicating
the downlink communications signals 110D based on multiuser MIMO
(MU-MIMO) technology.
[0005] The MU-MIMO technology can help provide increased data
rate/throughput, enhanced reliability, improved energy efficiency,
and/or reduced interference in the remote coverage areas
100(1)-100(N). As such, the MU-MIMO technology has been
incorporated into recent and evolving wireless communications
standards, such as long-term evolution (LTE) and LTE-Advanced.
However, to fully benefit from the enhancements provided by the
MU-MIMO technology, each of the multiple client devices 116 needs
to employ an equal number of antennas as the remote units
104(1)-104(N). Unfortunately, it may become more difficult to add
additional antennas in the client devices 116 due to space
limitations and complexity. As a result, it may become difficult to
scale the MU-MIMO technology beyond the capabilities of the client
device 116. Accordingly, the wireless communications industry is
adopting a new antenna technology known as massive MIMO (M-MIMO),
which may scale up the MU-MIMO technology by orders of magnitude,
to meet the increasing bandwidth demands by the client devices
116.
[0006] No admission is made that any reference cited herein
constitutes prior art. Applicant expressly reserves the right to
challenge the accuracy and pertinency of any cited documents.
SUMMARY
[0007] Embodiments of the disclosure relate to a massive
multiple-input multiple-output (M-MIMO) wireless distribution
system (WDS) and related methods for optimizing the M-MIMO WDS. In
one aspect, the M-MIMO WDS includes a plurality of remote units
each deployed at a location and includes one or more antennas to
serve a remote coverage area. In examples discussed herein, the
location and a number of the antennas associated with each of the
remote units are adapted to a non-uniform client device density
distribution. As such, at least one remote unit can have a
different number of the antennas from at least one other remote
unit in the M-MIMO WDS. In another aspect, a selected system
configuration including the location and the number of the antennas
associated with each of the remote units can be determined using an
iterative algorithm. The iterative algorithm utilizes a
performance-estimation function to determine the selected system
configuration that maximizes a selected system performance
indicator (e.g., system capacity) of the M-MIMO WDS. By configuring
the remote units based on the non-uniform client device density
distribution and determining the selected system configuration
using the iterative algorithm, it may be possible to optimize the
selected system performance indicator at reduced complexity and
costs, thus helping to enhance user experiences in the M-MIMO
WDS.
[0008] In this regard, in one aspect, a M-MIMO WDS is provided. The
M-MIMO WDS includes a plurality of remote units each configured to
be deployed at a location to serve a respective remote coverage
area. The M-MIMO WDS also includes a central unit communicatively
coupled to each of the plurality of remote units over a
communications link among a plurality of communications links. The
central unit is configured to encode a received downlink
communications signal to generate a downlink MIMO communications
signal. The central unit is also configured to distribute the
downlink MIMO communications signal over the plurality of
communications links to the plurality of remote units. Each of the
plurality of remote units comprises one or more antennas configured
to distribute the downlink MIMO communications signal to at least
one client device located in the respective remote coverage area.
At least one remote unit among the plurality of remote units
comprises a different number of antennas from at least one other
remote unit among the plurality of remote units.
[0009] In another aspect, a method for optimizing a selected
performance indication of a M-MIMO WDS is provided. The method
includes generating an initial system configuration based on at
least one initial system parameter of a M-MIMO WDS. The initial
system configuration comprises a plurality of configuration
parameter groups corresponding to a plurality of remote units in
the M-MIMO WDS, respectively. The method also includes providing
the plurality of configuration parameter groups to a
performance-estimation function configured to estimate the selected
system performance indicator of the M-MIMO WDS based on the
plurality of configuration parameter groups. The method also
includes generating an initial estimation of the selected system
performance indicator by the performance-estimation function
according to the initial system configuration. The method also
includes updating one or more selected configuration parameter
groups among the plurality of configuration parameter groups to
generate at least one updated system configuration. The method also
includes providing the plurality of configuration parameter groups
comprising the one or more selected configuration parameter groups
to the performance-estimation function. The method also includes
generating at least one updated estimation of the selected system
performance indicator by the performance-estimation function
according to the at least one updated system configuration. The
method also includes determining a selected system configuration
between the initial system configuration and the at least one
updated system configuration corresponding to a higher selected
system performance indicator among the initial estimation of the
selected system performance indicator and the at least one updated
estimation of the selected system performance indicator. The method
also includes configuring the plurality of remote units based on
the selected system configuration.
[0010] In another aspect, non-transitory computer-readable medium
including software with instructions is provided. The
non-transitory computer-readable medium including software with
instructions can generate an initial system configuration based on
at least one initial system parameter of a M-MIMO WDS. The initial
system configuration comprises a plurality of configuration
parameter groups corresponding to a plurality of remote units in
the M-MIMO WDS, respectively. The non-transitory computer-readable
medium including software with instructions can also provide the
plurality of configuration parameter groups to a
performance-estimation function configured to estimate a selected
system performance indicator of the M-MIMO WDS based on the
plurality of configuration parameter groups. The non-transitory
computer-readable medium including software with instructions can
also generate an initial estimation of the selected system
performance indicator by the performance-estimation function
according to the initial system configuration. The non-transitory
computer-readable medium including software with instructions can
also update one or more selected configuration parameter groups
among the plurality of configuration parameter groups to generate
at least one updated system configuration. The non-transitory
computer-readable medium including software with instructions can
also provide the plurality of configuration parameter groups
comprising the one or more selected configuration parameter groups
to the performance-estimation function. The non-transitory
computer-readable medium including software with instructions can
also generate at least one updated estimation of the selected
system performance indicator by the performance-estimation function
according to the at least one updated system configuration. The
non-transitory computer-readable medium including software with
instructions can also determine a selected system configuration
between the initial system configuration and the at least one
updated system configuration corresponding to a higher selected
system performance indicator among the initial estimation of the
selected system performance indicator and the at least one updated
estimation of the selected system performance indicator.
[0011] Additional features and advantages will be set forth in the
detailed description which follows and, in part, will be readily
apparent to those skilled in the art from the description or
recognized by practicing the embodiments as described in the
written description and claims hereof, as well as the appended
drawings.
[0012] It is to be understood that both the foregoing general
description and the following detailed description are merely
exemplary and are intended to provide an overview or framework to
understand the nature and character of the claims.
[0013] The accompanying drawings are included to provide a further
understanding of the disclosure, and are incorporated in and
constitute a part of this specification. The drawings illustrate
one or more embodiments, and together with the description serve to
explain principles and operation of the various embodiments.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 is a schematic diagram of an exemplary wireless
distribution system (WDS), which may be a distributed antenna
system (DAS) for example;
[0015] FIG. 2A is a schematic diagram of an exemplary conventional
fully distributed (FD) massive multiple-input multiple-output
(M-MIMO) (FD-M-MIMO) system;
[0016] FIG. 2B is a schematic diagram of an exemplary conventional
cluster distributed (CD) M-MIMO (CD-M-MIMO) system that can help
reduce complexity and costs of the conventional FD-M-MIMO system of
FIG. 2A;
[0017] FIG. 3 is a schematic diagram of an exemplary M-MIMO WDS
configured to support a plurality of client devices distributed
non-uniformly throughout a coverage area of the M-MIMO WDS;
[0018] FIG. 4 is a flowchart illustrating an exemplary process for
optimizing a selected performance indicator of the M-MIMO WDS of
FIG. 3 based on a non-uniform client device density
distribution;
[0019] FIG. 5 is a schematic diagram of an exemplary computer
system including one or more non-transitory computer-readable media
for storing software instructions to implement a
performance-estimation function for optimizing the selected
performance indicator of FIG. 4;
[0020] FIG. 6 is flowchart of an exemplary iterative numerical
process that can be employed by the computer system of FIG. 5 to
iteratively evaluate a performance-estimation function to determine
a selected system configuration for maximizing system capacity of
the M-MIMO WDS of FIG. 3;
[0021] FIGS. 7A and 7B are plots providing an exemplary network
capability comparison between the M-MIMO WDS of FIG. 3, the
conventional CD-M-MIMO system of FIG. 2B, and a conventional
DAS;
[0022] FIG. 8 is a schematic diagram of an exemplary WDS provided
in the form of an optical fiber-based WDS that can be configured as
the M-MIMO WDS of FIG. 3 to support the plurality of client devices
distributed non-uniformly throughout the coverage area of the
M-MIMO WDS; and
[0023] FIG. 9 is a partial schematic cut-away diagram of an
exemplary building infrastructure in which a WDS, such as the WDS
of FIG. 8, including a plurality of remote units configured and
deployed based on a selected system configuration determined via
the process of FIG. 4 to maximize a selected system performance
indicator (e.g., network capacity) of the WDS.
DETAILED DESCRIPTION
[0024] Embodiments of the disclosure relate to a massive
multiple-input multiple-output (M-MIMO) wireless distribution
system (WDS) and related methods for optimizing the M-MIMO WDS. In
one aspect, the M-MIMO WDS includes a plurality of remote units
each deployed at a location and includes one or more antennas to
serve a remote coverage area. In examples discussed herein, the
location and a number of the antennas associated with each of the
remote units are adapted to a non-uniform client device density
distribution. As such, at least one remote unit can have a
different number of the antennas from at least one other remote
unit in the M-MIMO WDS. In another aspect, a selected system
configuration including the location and the number of the antennas
associated with each of the remote units can be determined using an
iterative algorithm. The iterative algorithm utilizes a
performance-estimation function to determine the selected system
configuration that maximizes a selected system performance
indicator (e.g., system capacity) of the M-MIMO WDS. By configuring
the remote units based on the non-uniform client device density
distribution and determining the selected system configuration
using the iterative algorithm, it may be possible to optimize the
selected system performance indicator at reduced complexity and
costs, thus helping to enhance user experiences in the M-MIMO
WDS.
[0025] Before discussing exemplary aspects of a M-MIMO WDS and
related methods for optimizing performance of the M-MIMO WDS, a
brief discussion on M-MIMO technology and conventional M-MIMO
system topology are first provided with reference to FIGS. 2A-2B.
The discussion of specific exemplary aspects of a M-MIMO WDS and
related methods for optimizing performance of the M-MIMO WDS starts
below with reference to FIG. 3.
[0026] M-MIMO is an emerging antenna technology developed by the
wireless communications industry as one of the key enabling
technologies for the upcoming fifth-generation (5G) wireless
communications systems. In a M-MIMO-based communications system,
one or more antenna arrays include hundreds or even thousands of
antennas employed to simultaneously communicate with a large number
of client devices, such as smart phones, laptops, etc., using the
same radio frequency (RF) spectral resource. An M-MIMO antenna
system relies on spatial multiplexing for communicating with the
large number of client devices. By coherently pre-coding a wireless
communications signal over the antenna array(s), it is possible to
form and direct multiple RF beams toward multiple client devices
simultaneously. Depending on an actual bandwidth requirement of a
selected client device among the large number of client devices, an
appropriate number of the antennas in the antenna array(s) may be
used to form and transmit an RF beam towards the selected client
device. The selected client device, on the other hand, does not
need to employ a matching number of antennas for receiving the RF
beam directed to it. In this regard, the M-MIMO antenna system can
overcome the scalability limitations associated with existing
multi-user MIMO (MU-MIMO) antenna systems, thus significantly
improving spectral efficiency of the 5G wireless communications
systems to provide increased capacity and enhanced user
experiences.
[0027] The M-MIMO antenna system may be incorporated into a WDS to
support 5G wireless communications in an indoor environment.
However, deployment of co-located large-scale M-MIMO antenna
systems in the indoor environment based on a centralized
architecture presents several challenges, including the large
antenna array form factor (at frequencies below 6 gigahertz (GHz))
and lower performance due to increased signal losses coming from
building walls. As such, distributed M-MIMO antenna architectures
become more preferable in the indoor environment due to cost and
performance benefits over the centralized architecture.
[0028] In this regard, FIG. 2A is a schematic diagram of an
exemplary conventional fully distributed (FD) M-MIMO (FD-M-MIMO)
system 200. The conventional FD-M-MIMO system 200 includes a
plurality of remote units 202 coupled to a central unit 204 via a
plurality of communication links 206, respectively. The remote
units 202 include a plurality of antennas 208, respectively. The
remote units 202 may be geographically distributed within a
coverage boundary of a wireless cell 210 based on a predefined user
distribution profile. In this regard, the antennas 208 in the
remote units 202 collectively form an M-MIMO antenna array for
simultaneously distributing multiple data streams at the same RF
frequency to multiple client devices 212 located within the
coverage boundary of the wireless cell 210.
[0029] The conventional FD-M-MIMO system 200 can provide higher
network capacity, since the antennas 208 can transmit the multiple
data streams at the same RF frequency at the same time. However,
since each of the remote units 202 is connected to the central unit
204 by a dedicated communication link 206, system complexity as
well as hardware and installation costs of the conventional
FD-M-MIMO system 200 may increase significantly. Thus, an
alternative M-MIMO system architecture has been developed by the
industry to help reduce the complexity and costs of the
conventional FD-M-MIMO system 200.
[0030] In this regard, FIG. 2B is a schematic diagram of an
exemplary conventional cluster distributed (CD) M-MIMO (CD-M-MIMO)
system 214 that can help reduce the complexity and costs of the
conventional FD-M-MIMO system 200 of FIG. 2A. Common elements
between FIGS. 2A and 2B are shown therein with common element
numbers and will not be re-described herein.
[0031] The conventional CD-M-MIMO system 214 includes a plurality
of remote units 216 coupled to a central unit 218 via a plurality
of communication links 220, respectively. The remote units 216 may
be geographically distributed within the coverage boundary of the
wireless cell 210 based on the predefined user distribution
profile. The spatial distribution of the remote units 216 is
designed such that good coverage of the wireless cell 210 is
obtained. In contrast to the remote units 202 in the conventional
FD-M-MIMO system 200 of FIG. 2A, each of the remote units 216
includes a plurality of antennas 222. A number of the antennas 222
included in each of the remote units 216 is configured to be the
same. For example, as shown in FIG. 2B, each of the remote units
216 includes six antennas. Similar to the conventional FD-M-MIMO
system 200 of FIG. 2A, the antennas 222 in each of the remote units
216 collectively form an M-MIMO antenna array for simultaneously
distributing multiple data streams at the same RF frequency to the
multiple client devices 212 located within the coverage boundary of
the wireless cell 210. The conventional CD-M-MIMO system 214 has a
lower system complexity and a smaller installation cost compared to
the conventional FD-M-MIMO system 200 of FIG. 2A as the number of
the remote units 216 and the communication links 220 are
reduced.
[0032] As discussed above, the remote units 202 in the conventional
FD-M-MIMO system 200 and the remote units 216 in the conventional
CD-M-MIMO system 214 are geographically distributed within the
coverage boundary of the wireless cell 210 based on the predefined
user distribution profile. Often time, the predefined user
distribution profile assumes uniform distribution of the multiple
client devices 212 throughout the wireless cell 210. However, the
multiple client devices 212 are more likely to be distributed
non-uniformly in most indoor environments. For example, a UE
density in a conference room can reach three persons per
square-meter (3 persons/m.sup.2). In contrast, the UE density in a
cubical area may be just 0.15 persons/m.sup.2. Experiments have
shown that the average UE density in a typical office building is
approximately 0.05 persons/m.sup.2. In this regard, the complexity
of non-uniform UE distribution in the indoor environment
necessitates advanced M-MIMO system architecture adapted to
effectively utilize network infrastructure and RF spectral
resources based on non-uniform distribution of the multiple client
devices 212.
[0033] In this regard, FIG. 3 is a schematic diagram of an
exemplary M-MIMO WDS 300 configured to support a plurality of
client devices 302 distributed non-uniformly throughout a coverage
area 304 of the M-MIMO WDS 300. As is further discussed below in
FIG. 3, the M-MIMO WDS 300 differs from the conventional FD-M-MIMO
system 200 of FIG. 2A and the conventional CD-M-MIMO system 214 of
FIG. 2B in two aspects. First, the M-MIMO WDS 300 includes a
plurality of remote units 306(1)-306(N) to which radio resources
(e.g., antennas) are strategically allocated to maximize a selected
system performance indicator (e.g., network capacity) based on a
non-uniform client device density distribution. Second, geographic
locations (e.g., location coordinates) of the remote units
306(1)-306(N) are determined according to the non-uniform client
device density distribution and adapted to an indoor wireless
signal propagation environment. In this regard, the M-MIMO WDS 300
is configured based on a non-uniform distributed (ND) M-MIMO
(ND-M-MIMO) architecture. Further, as discussed later with
reference to FIG. 4, an iterative algorithm employing a
performance-related objective function can be utilized to determine
radio resource allocations and geographic locations for the remote
units 306(1)-306(N) in the M-MIMO WDS 300.
[0034] The M-MIMO WDS 300 configured based on the ND-M-MIMO
architecture is advantageous over the conventional FD-M-MIMO system
200 of FIG. 2A and the conventional CD-M-MIMO system 214 of FIG. 2B
in a variety of aspects. First, the M-MIMO WDS 300 can be
implemented with a significant reduction in network infrastructure
and installation cost. Second, the M-MIMO WDS 300 can significantly
improve network capacity at minimal cost. Third, the M-MIMO WDS 300
provides greater architectural flexibility and scalability, thus
making it possible to adapt the M-MIMO WDS 300 to support future
wireless technologies (e.g. 5G technology) at minimal cost. For
example, given that the M-MIMO WDS 300 is agnostic to operating RF
carrier frequency, the M-MIMO WDS 300 can be adapted to support
millimeter-wave 5G networks at minimal cost. In addition, it may be
possible to further increase system capacity and/or reliability of
the M-MIMO WDS 300 by adding more antennas to the remote units
306(1)-306(N). Furthermore, the M-MIMO WDS 300 can support all
forms of base station functional splits and is also agnostic to
front-haul and mid-haul transmission technologies (e.g., fiber
optical-based transmission technology).
[0035] With continuing reference to FIG. 3, the remote units
306(1)-306(N) are configured to be deployed at a plurality of
locations (x.sub.1,y.sub.1)-(x.sub.N,y.sub.N) to serve a plurality
of remote coverage areas 308(1)-308(N), respectively. The
respective location for each of the remote units 306(1)-306(N) is
represented by a pair of coordinates (x.sub.i,y.sub.i)
(1.ltoreq.i.ltoreq.N). In a non-limiting example, the pair of
coordinates (x.sub.i,y.sub.i) can correspond to a pair of
longitude-latitude coordinates as determined by such system as the
Global Positioning System (GPS). In another non-limiting example,
the pair of coordinates (x.sub.i,y.sub.i) can be represented by a
pair of Cartesian coordinates that are arbitrarily determined based
on a layout map of the coverage area 304. It should be appreciated
other coordinate systems (e.g., Polar coordinate system) may also
be used to represent the pair of coordinates (x.sub.i,y.sub.i) for
each of the remote units 306(1)-306(N).
[0036] The M-MIMO WDS 300 includes a central unit 310
communicatively coupled to the remote units 306(1)-306(N) over a
plurality of communications links 312(1)-312(N), respectively. The
communications links 312(1)-312(N) may be fiber optical-based
communications links or any other type of communications links. The
central unit 310 is configured to encode a received downlink
communications signal 314 to generate a downlink MIMO
communications signal 316 and provide the downlink MIMO
communications signal 316 to the remote units 306(1)-306(N) over
the communications links 312(1)-312(N). The central unit 310 may
employ a baseband unit to process (e.g., pre-code) the downlink
MIMO communications signal 316 prior to distributing the downlink
MIMO communications signal 316 to the remote units 306(1)-306(N).
In this regard, the remote units 306(1)-306(N) in the M-MIMO WDS
300 are configured to simultaneously transmit the downlink MIMO
communications signal 316 in the same RF spectrum (e.g., channel or
band).
[0037] Each of the remote units 306(1)-306(N) includes one or more
antennas 318(1)-318(M) configured to distribute the downlink MIMO
communications signal 316 to at least one of the client devices 302
located in a respective coverage area among the remote coverage
areas 308(1)-308(N). In a non-limiting example, a remote unit
located in any of the remote coverage areas 308(1)-308(N) can
transmit the downlink MIMO communications signal 316 concurrently
from a subset of the antennas 318(1)-318(M) (e.g., two, three, or
four) to a respective client device among the client devices 302 if
the respective client device is equipped with an equal number
(e.g., two, three, or four) of antennas. In this regard, the remote
unit is transmitting the downlink MIMO communications signal 316
via MIMO. Alternatively, the remote unit located in any of the
remote coverage areas 308(1)-308(N) can utilize the subset of the
antennas 318(1)-318(M) (e.g., two, three, or four) to form an RF
beam for distributing the downlink MIMO communications signal 316
to the respective client device among the client devices 302 if the
respective client device is not equipped with an equal number of
antennas. In this regard, the remote unit is transmitting the
downlink MIMO communications signal 316 via RF beamforming. By
transmitting the downlink MIMO communications signal 316 using MIMO
and/or RF beamforming, the remote units 306(1)-306(N) in the M-MIMO
WDS 300 can adapt flexibly to the receiving capabilities of the
client devices 302. As a result, the client devices 302 may be able
to receive the downlink MIMO communications signal 316 in any of
the remote coverage areas 308(1)-308(N) with a desired RF signal
quality (e.g., signal-to-noise ratio (SNR)) and data
throughput.
[0038] In addition to distributing the downlink MIMO communications
signal 316 to the client devices 302, each of the remote units
306(1)-306(N) is configured to receive at least one uplink
communications signal 320 from at least one of the client devices
302 and provide the received uplink communications signal 320 to
the central unit 310 over a respective communications link among
the communications links 312(1)-312(N). The central unit 310 may
combine the uplink communications signal 320 received from the
remote units 306(1)-306(N) and/or perform additional signal
processing (e.g., filtering, frequency conversion, and signal
conversion).
[0039] As mentioned earlier, the M-MIMO WDS 300 is configured based
on the ND-M-MIMO architecture adapted to the non-uniform client
device density distribution throughout the coverage area 304. In a
non-limiting example, remote coverage area 308(1) (e.g., a
conference room) has a higher client device density than remote
coverage area 308(2) (e.g., a hallway). In the same non-limiting
example, the remote coverage area 308(2) has a higher client device
density than remote coverage area 308(N) (e.g., a cubical area).
Accordingly, the demand for data throughput in the remote coverage
area 308(1) would be higher than that in the remote coverage area
308(2). Similarly, the demand for data throughput in the remote
coverage area 308(2) would be higher than that in the remote
coverage area 308(N). In this regard, to optimize user experience
in the M-MIMO WDS 300, it may be necessary to maximize system
capacity of the M-MIMO WDS 300 throughout the coverage area 304. In
examples discussed hereinafter, the system capacity of the M-MIMO
WDS 300 refers to an aggregated data throughput in the remote
coverage areas 308(1)-308(N).
[0040] In one aspect, the locations
(x.sub.1,y.sub.1)-(x.sub.N,y.sub.N) of the remote units
306(1)-306(N) are strategically determined based on the client
device density distribution throughout the coverage area 304. For
example, the location (x.sub.1,y.sub.1) of remote unit 306(1) can
correspond to a center point of the remote coverage are 308(1). In
another aspect, given that the remote coverage area 308(1) has a
higher client device density than the remote coverage area 308(2),
the remote unit 306(1) can be configured to include more antennas
than remote unit 306(2). Likewise, since the remote coverage area
308(2) has a higher client device density than the remote coverage
area 308(N), the remote unit 306(2) can be configured to include
more antennas than remote unit 306(N). In this regard, given the
non-uniform client device density distribution in the coverage area
304, at least one remote among the remote units 306(1)-306(N)
(e.g., the remote unit 306(1)) is configured to include a different
number of antennas from at least one other remote unit among the
remote units 306(1)-306(N) (e.g., the remote unit 306(2)). By
strategically determining the locations
(x.sub.1,y.sub.1)-(x.sub.N,y.sub.N) of the remote units
306(1)-306(N) and allocating a respective number of the antennas
318(1)-318(N) to each of the remote units 306(1)-306(N) based on
the non-uniform client device density distribution, it may be
possible to maximize the system capacity of the M-MIMO WDS 300 at
reduced complexity and cost, while preserving flexibility and
scalability for supporting future RF spectrums and/or
communications technologies.
[0041] The locations (x.sub.1,y.sub.1)-(x.sub.N,y.sub.N) of the
remote units 306(1)-306(N) and the respective number of the
antennas 318(1)-318(N) allocated to each of the remote units
306(1)-306(N) can be determined systematically based on a process.
In this regard, FIG. 4 is a flowchart illustrating an exemplary
process 400 for optimizing a selected performance indicator of the
M-MIMO WDS 300 of FIG. 3 based on the non-uniform client device
density distribution.
[0042] With reference to FIG. 4, the process 400 includes
generating an initial system configuration based on at least one
initial system parameter of the M-MIMO WDS 300 (block 402). The
initial system configuration includes a plurality of configuration
parameter groups P.sub.1-P.sub.N corresponding to the remote units
306(1)-306(N), respectively. In a non-limiting example, a
configuration parameter group Pi (1.ltoreq.i.ltoreq.N) among the
configuration parameter groups P.sub.1-P.sub.N includes
configuration parameters (x.sub.i, y.sub.i, n.sub.i)
(1.ltoreq.i.ltoreq.N). Among the configuration parameters,
(x.sub.i, y.sub.i) represents respective location coordinates of a
remote unit 306(i) and n.sub.i represents a respective number of
antennas provided in the remote unit 306(i) (1.ltoreq.i.ltoreq.N).
Accordingly, the configuration parameter groups P.sub.1-P.sub.N may
be expressed as (x.sub.1, y.sub.1, n.sub.1)-(x.sub.N, y.sub.N,
n.sub.N) for the remote units 306(1)-306(N), respectively. In
another non-limiting example, the initial system parameter used to
generate the initial system configuration includes such parameters
of the M-MIMO WDS 300 as total number of antennas, total number of
remote units, system layout, client device density distribution,
and/or coverage area RF survey.
[0043] Next, the configuration parameter groups P.sub.1-P.sub.N are
provided to a performance-estimation function f (P.sub.1-P.sub.N),
which is configured to estimate the selected system performance
indicator of the M-MIMO WDS 300 based on the configuration
parameter groups P.sub.1-P.sub.N (block 404). The
performance-estimation function f (P.sub.1-P.sub.N), which may be
implemented by a computing device (e.g., a personal computer, a
laptop, etc.) based on software instructions stored in a
non-transitory computer-readable medium, will be further discussed
later with reference to FIG. 5. The performance-estimation function
f (P.sub.1-P.sub.N) generates an initial estimation of the selected
system performance indicator according to the initial system
configuration (block 406).
[0044] Subsequently, one or more selected configuration parameter
groups among the configuration parameter groups P.sub.1-P.sub.N are
updated to generate at least one updated system configuration
(block 408). As will be further discussed later with reference to
FIG. 5, the selected configuration parameter groups among the
configuration parameter groups P.sub.1-P.sub.N may be changed based
on predetermined iteration steps. The configuration parameter
groups P.sub.1-P.sub.N, which now include the updated selected
configuration parameter groups, are provided to the
performance-estimation function f (P.sub.1-P.sub.N) (block 410) The
performance-estimation function f (P.sub.1-P.sub.N) generates at
least one updated estimation of the selected system performance
indicator according to the updated system configuration (block
412).
[0045] Next, a selected system configuration between the initial
system configuration and the updated system configuration can be
determined (block 414). The selected system configuration
corresponds to a higher selected system performance indicator
between the initial estimation of the selected system performance
indicator and the updated estimation of the selected system
performance indicator determined by the performance-estimation
function f (P.sub.1-P.sub.N). Then, it is possible to configure the
remote units 306(1)-306(N) based on the selected system
configuration, thus maximizing the selected system performance
indicator in the M-MIMO WDS 300 (block 416). The selected system
configuration may be output to, for example a printer, a computer
monitor, a storage media, etc., prior to configuring the remote
units 306(1)-306(N) in the M-MIMO WDS 300.
[0046] In a non-limiting example, the selected system performance
indicator refers to the system capacity of the M-MIMO WDS 300. In
this regard, the process 400 can be utilized to optimize the system
capacity of the M-MIMO WDS 300. Accordingly, in block 404, the
configuration parameter groups P.sub.1-P.sub.N are provided to the
performance-estimation function f (P.sub.1-P.sub.N), which is
configured to estimate the system capacity of the M-MIMO WDS 300
based on the configuration parameter groups P.sub.1-P.sub.N. In
block 406, the performance-estimation function f (P.sub.1-P.sub.N)
generates an initial system capacity according to the initial
system configuration. In block 408, one or more selected
configuration parameter groups among the configuration parameter
groups P.sub.1-P.sub.N are updated to generate at least one updated
system configuration. In block 410, the configuration parameter
groups P.sub.1-P.sub.N, which now include the updated selected
configuration parameter groups, are provided to the
performance-estimation function f (P.sub.1-P.sub.N). In block 412,
the performance-estimation function f (P.sub.1-P.sub.N) generates
at least one updated system capacity according to the updated
system configuration. In block 414, the selected system
configuration between the initial system configuration and the
updated system configuration can be determined. The selected system
configuration corresponds to a higher system capacity between the
initial system capacity and the updated system capacity determined
by the performance-estimation function f (P.sub.1-P.sub.N). In
block 416, the remote units 306(1)-306(N) are configured based on
the selected system configuration to maximize the system capacity
in the M-MIMO WDS 300.
[0047] As mentioned earlier, a computing device (e.g., a personal
computer, a laptop, etc.) may implement the performance-estimation
function f (P.sub.1-P.sub.N) based on software instructions stored
in a non-transitory computer-readable medium. In this regard, FIG.
5 is a schematic diagram of an exemplary computer system 500
including one or more non-transitory computer-readable media
502(1)-502(4) for storing software instructions to implement the
performance-estimation function f (P.sub.1-P.sub.N) of FIG. 4.
Common elements between FIGS. 3, 4, and 5 are shown therein with
common element numbers and will not be re-described herein.
[0048] With reference to FIG. 5, the non-transitory
computer-readable media 502(1)-502(4) further include a hard drive
502(1), an on-board memory system 502(2), a compact disc 502(3),
and a floppy disk 502(4). Each of the non-transitory
computer-readable media 502(1)-502(4) may be configured to store
the software instructions to implement the performance-estimation
function f (P.sub.1-P.sub.N). The computer system 500 also includes
a keyboard 504 and a computer mouse 506 for inputting the software
instructions onto the non-transitory computer-readable media
502(1)-502(4). The keyboard 504 and the computer mouse 506 may also
be used to input the initial system parameter of the M-MIMO WDS
300, which can be used to generate the initial system configuration
in block 402 of FIG. 4. The computer system 500 also includes a
monitor 508 for outputting the selected system configuration for
configuring the remote units 306(1)-306(N). Further, the computer
system 500 includes a processor 510 configured to read the software
instructions from the non-transitory computer-readable media
502(1)-502(4) and execute the software instructions to implement
the performance-estimation function f (P.sub.1-P.sub.N). While the
computer system 500 is illustrated as a single device, the computer
system 500 may also be a computer network deployed according to a
centralized topology or a distributed topology.
[0049] When the process 400 of FIG. 4 is utilized to optimize the
system capacity of the M-MIMO WDS 300, the performance-estimation
function f (P.sub.1-P.sub.N) can be used to calculate an average
data rate of a client device over the coverage area 304 for a given
configuration parameter group of the configuration parameter groups
P.sub.1-P.sub.N. The performance-estimation function f
(P.sub.1-P.sub.N) is a function of the configuration parameter
groups P.sub.1-P.sub.N and can be expressed as equation Eq. 1
below.
f ( P 1 - P N ) = B .intg. .intg. W ( x , y ) dxdy .intg. .intg.
log 2 [ 1 + ( .lamda. 4 .pi. ) r P s P n k = 1 N n k q d k r ( x ,
y , x k , y k ) ] W ( x , y ) dxdy ( Eq . 1 ) ##EQU00001##
[0050] In the equation Eq. 1 above, B represents a channel
bandwidth of the downlink MIMO communications signal 316 to be
distributed by the remote units 306(1)-306(N) in the coverage area
304. For example, the channel bandwidth of the downlink MIMO
communications signal 316 can be 20 megahertz (MHz) in such
wireless communications systems as long-term evolution (LTE).
W(x,y) is a weighting function representing such
spatially-dependent parameters as system layout information, client
device density distribution, and signal propagation environment at
selected locations in the coverage area 304 of the M-MIMO WDS 300.
According to the non-limiting example discussed earlier with
reference to FIG. 3, the remote coverage area 308(1) (e.g., a
conference room) has a higher client device density than the remote
coverage area 308(2) (e.g., a hallway), and the remote coverage
area 308(2) has a higher client device density than the remote
coverage area 308(N) (e.g., a cubical area). In this regard, the
remote coverage area 308(1) would be assigned a higher weighting
factor than the remote coverage area 308(2), and the remote
coverage area 308(2) would be assigned a higher weighting factor
that the remote coverage area 308(N). Accordingly, the remote
coverage area 308(1) would correspond to a higher weight than the
remote coverage area 308(2) in the weighting function W(x,y) and
the remote coverage area 308(2) would correspond to a higher weight
than the remote coverage area 308(N) in the weighting function
W(x,y). In this regard, the performance-estimation function f
(P.sub.1-P.sub.N) is proportionally related to the weighting
function W(x,y). The double integration .intg..intg.W(x,y)dxdy
represents a normalized weighting function (e.g., total weight)
throughout the coverage area 304.
[0051] With continuing reference to the equation Eq. 1, P.sub.s and
P.sub.n represent signal power and noise power in the coverage area
304, respectively. d.sub.k.sup.r(x, y, x.sub.k, y.sub.k) represents
a distance between the location (x.sub.k, y.sub.k) of the remote
unit 306(K) (1.ltoreq.k.ltoreq.N) and a selected location (x,y) in
the coverage area 304. r represents an exponential of a free-space
propagation attenuation model. n.sub.k.sup.q represents a number of
antennas in the remote unit 306(K) (1.ltoreq.k.ltoreq.N), wherein q
represents expected signal coherency between multiple copies of the
downlink MIMO communications signal 316 received by one of the
client devices 302 at the location (x,y). In a non-limiting
example, q is a positive decimal number between 1 (inclusive) and 2
(inclusive), with 1 representing the least coherency and 2
representing the highest coherency. Notably, the parameters B,
P.sub.s, P.sub.n, r, and q are predetermined and fixed for each
execution of the process 400.
[0052] Given that the performance-estimation function f
(P.sub.1-P.sub.N) is dependent of the configuration parameter
groups P.sub.1-P.sub.N, a change to any of the configuration
parameter groups P.sub.1-P.sub.N may result in a significant change
in the result of the performance-estimation function f
(P.sub.1-P.sub.N) and consequently the system capacity of the
M-MIMO WDS 300. Notably, there exists a vast number of possible
combinations of the configuration parameter groups P.sub.1-P.sub.N.
As such, to determine a selected configuration parameter group of
the configuration parameter groups P.sub.1-P.sub.N that can
maximize the system capacity of the M-MIMO WDS 300, it may be
necessary to evaluate the vast number of possible combinations of
the configuration parameter groups P.sub.1-P.sub.N. The computer
system 500 may be configured via the software instructions stored
in the non-transitory computer-readable media 502(1)-502(4) to help
determine the selected configuration parameter group of the
configuration parameter groups P.sub.1-P.sub.N for maximizing the
system capacity of the M-MIMO WDS 300. More specifically, the
software instructions may be so programmed to iteratively test the
performance-estimation function f (P.sub.1-P.sub.N) based on
strategically selected combinations of the configuration parameter
groups P.sub.1-P.sub.N among the vast number of possible
combinations of the configuration parameter groups P.sub.1-P.sub.N.
The performance-estimation function f (P.sub.1-P.sub.N) may
converge quickly (e.g., in one to two minutes for the coverage area
304 of approximately 1200 square meters).
[0053] FIG. 6 is flowchart of an exemplary iterative numerical
process 600 that can be employed by the computer system 500 of FIG.
5 to iteratively evaluate the performance-estimation function f
(P.sub.1-P.sub.N) of FIG. 4 to determine the selected system
configuration for maximizing the system capacity of the M-MIMO WDS
300 of FIG. 3. According to the iterative numerical process 600,
one or more initial system parameters (e.g., total number of
antennas, total number of remote units, system layout, client
device density distribution, and/or coverage area RF survey of the
M-MIMO WDS 300) are input into the computer system 500 (block 602).
Next, the computer system 500 generates a random initial system
configuration including the configuration parameter groups
P.sub.1-P.sub.N (block 604). Next, the computer system 500
estimates the system capacity corresponding to the random initial
system configuration using the performance-estimation function f
(P.sub.1-P.sub.N) (block 606). The computer system 500 may store
the system capacity corresponding to the random initial system
configuration in a first programmable variable VAR.sub.1. Next, the
computer system 500 updates one or more selected configuration
parameter groups among the configuration parameter groups
P.sub.1-P.sub.N to generate an updated system configuration (block
608). In a non-limiting example, the selected configuration
parameter group can be updated as shown below.
x k ( i + 1 ) = x k ( i ) + .DELTA. x .differential. f
.differential. x k , y k ( i + 1 ) = y k ( i ) + .DELTA. y
.differential. f .differential. y k , n k ( i + 1 ) = n k ( i ) +
.DELTA. n .differential. f .differential. n k ##EQU00002##
[0054] In the expression above, (x.sub.k.sup.(i),
y.sub.k.sup.(i),n.sub.k.sup.(i)) are the location and antenna count
of remote unit 306(K) (1.ltoreq.k.ltoreq.N) of FIG. 3 in an i-th
iteration of the iterative numerical process 600. Similarly,
(x.sub.k.sup.(i+1), y.sub.k.sup.(i+1),n.sub.k.sup.(i+1)) are the
updated location and antenna count of the remote unit 306(K) for an
(i+1)-th iteration of the iterative numerical process 600.
Parameters .DELTA.x, .DELTA.y, .DELTA.n represent iteration step
sizes between the i-th iteration and the (i+1)-th iteration.
[0055] The computer system 500 then estimates the system capacity
corresponding to the updated system configuration using the
performance-estimation function f (P.sub.1-P.sub.N) (block 610).
The computer system 500 may store the system capacity corresponding
to the updated system configuration in a second programmable
variable VAR.sub.2. The computer system 500 then checks to see if
the system capacity nears saturation (block 612). In this regard,
the computer system 500 may compare the second programmable
variable VAR.sub.2 against the first programmable variable
VAR.sub.1 to determine whether the second programmable variable
VAR.sub.2 is higher than the first programmable variable
VAR.sub.1.
[0056] If the second programmable variable VAR.sub.2 is higher than
the first programmable variable VAR.sub.1 by a predefined
threshold, the computer system 500 may conclude that the updated
system configuration can lead to a better system capacity.
Accordingly, the computer system 500 may copy the second
programmable variable VAR.sub.2 to the first programmable variable
VAR.sub.1 and return to block 608 for the next iteration.
[0057] If the second programmable variable VAR.sub.2 is higher than
the first programmable variable VAR.sub.1 by less than the
predefined threshold, the computer system 500 may conclude that the
system capacity has been maximized. Accordingly, the computer
system 500 may output the optimization results stored in the second
programmable variable VAR.sub.2 (block 614). The computer system
500 then concludes the iterative numerical process 600.
[0058] However, if the second programmable variable VAR.sub.2 is
lower than the first programmable variable VAR.sub.1, the computer
system 500 may conclude that the updated system configuration does
not lead to a better system capacity. Accordingly, the computer
system 500 may discard the updated system configuration, reset the
second programmable variable VAR.sub.2 to zero, and return to block
608 for the next iteration.
[0059] Using the iterative numerical process 600, it may be
possible to quickly determine the selected system configuration
including an optimal combination of the configuration parameter
groups P.sub.1-P.sub.N to maximize the system capacity of the
M-MIMO WDS 300. It should be appreciated that the iterative
numerical process 600 is not limited to maximizing the system
capacity of the M-MIMO WDS 300. By making necessary adjustments to
the performance-estimation function f (P.sub.1-P.sub.N), the
iterative numerical process 600 may be used to optimize any
selected system performance indicator of the M-MIMO WDS 300.
[0060] The M-MIMO WDS 300 of FIG. 3 optimized based on the process
400 of FIG. 4 and/or the iterative numerical process 600 of FIG. 6
can provide higher system capacity over a conventional distributed
antenna system (DAS) and the conventional CD-M-MIMO system 214 of
FIG. 2B. In this regard, FIGS. 7A and 7B are plots providing an
exemplary network capability comparison between the M-MIMO WDS 300
of FIG. 3, the conventional CD-M-MIMO system 214 of FIG. 2B, and a
conventional DAS.
[0061] In a non-limiting example, the network capacity comparisons
as illustrated in FIGS. 7A and 7B are based on a total of twelve
antennas provided in each of the systems. The conventional DAS
includes six remote units each having two antennas. The
conventional CD-M-MIMO system 214 includes three remote units each
having four antennas. The M-MIMO WDS 300 includes three remote
units having two, five, and five antennas, respectively. Notably,
according to previous discussions with reference to FIG. 3, the two
remote units with five antennas are located in remote coverage
areas with higher client device densities, while the remote unit
with two antennas is located in a remote coverage area with a lower
client device density.
[0062] FIG. 7A includes a first capacity-probability curve 702, a
second capacity-probability curve 704, and a third
capacity-probability curve 706. The first capacity-probability
curve 702 illustrates a network capability of the conventional DAS
at various probabilities. The second capacity-probability curve 704
illustrates a network capability of the conventional CD-M-MIMO
system 214 at various probabilities. The third capacity-probability
curve 706 illustrates a network capability of the M-MIMO WDS 300 at
various probabilities. As shown in FIG. 7A, at fifty percent (50%)
probability, the network capacity of the M-MIMO WDS 300 is higher
than the network capacity of the conventional CD-M-MIMO system 214
by 85 megabits per second (Mbps).
[0063] FIG. 7B includes a first bar graph 708, a second bar graph
710, and a third bar graph 712 corresponding to the conventional
DAS, the conventional CD-M-MIMO system 214, and the M-MIMO WDS 300,
respectively. FIG. 7B shows that an average cell edge data rate of
the M-MIMO WDS 300 is higher than an average cell edge data rate of
the conventional CD-M-MIMO system 214 by 27 Mbps. FIG. 7B also
shows that an average data rate of the M-MIMO WDS 300 is higher
than an average data rate of the conventional CD-M-MIMO system 214
by 85 Mbps. FIG. 7B further shows that an average peak data rate of
the M-MIMO WDS 300 is higher than an average peak data rate of the
conventional CD-M-MIMO system 214 by 215 Mbps. In summary, the
M-MIMO WDS 300 can bring an approximately 37% capacity gain as
compared to the conventional CD-M-MIMO system 214. Further, the
M-MIMO WDS 300 can bring an approximately 2.3 times higher capacity
than the conventional DAS, even with fewer remote units.
[0064] FIG. 8 is a schematic diagram of an exemplary WDS 800
provided in the form of an optical fiber-based WDS that can be
configured as the M-MIMO WDS 300 of FIG. 3 to support the client
devices 302 distributed non-uniformly throughout the coverage area
304 of the M-MIMO WDS 300. The WDS 800 includes an optical fiber
for distributing communications services for multiple frequency
bands. The WDS 800 in this example is comprised of three main
components. A plurality of radio interfaces provided in the form of
radio interface modules (RIMs) 802(1)-802(M) are provided in a
central unit 804 to receive and process one or more downlink
communications signals 806D(1)-806D(R) prior to optical conversion
into downlink optical fiber-based communications signals. The
downlink communications signals 806D(1)-806D(R) may be received
from a base station as an example. The RIMs 802(1)-802(M) provide
both downlink and uplink interfaces for signal processing. The
notations "1-R" and "1-M" indicate that any number of the
referenced component, 1-R and 1-M, respectively, may be provided.
The central unit 804 is configured to accept the RIMs 802(1)-802(M)
as modular components that can easily be installed and removed or
replaced in the central unit 804. In one example, the central unit
804 is configured to support up to twelve RIMs 802(1)-802(12). Each
of the RIMs 802(1)-802(M) can be designed to support a particular
type of radio source or range of radio sources (i.e., frequencies)
to provide flexibility in configuring the central unit 804 and the
WDS 800 to support the desired radio sources.
[0065] For example, one RIM 802 may be configured to support the
Personalized Communications System (PCS) radio band. Another RIM
802 may be configured to support the 800 MHz radio band. In this
example, by inclusion of the RIMs 802(1)-802(M), the central unit
804 could be configured to support and distribute communications
signals on both PCS and Long-Term Evolution (LTE) 700 radio bands,
as an example. The RIMs 802(1)-802(M) may be provided in the
central unit 804 that support any frequency bands desired,
including, but not limited to, the US Cellular band, PCS band,
Advanced Wireless Service (AWS) band, 700 MHz band, Global System
for Mobile communications (GSM) 900, GSM 1800, and Universal Mobile
Telecommunications System (UMTS). The RIMs 802(1)-802(M) may also
be provided in the central unit 804 that support any wireless
technologies desired, including, but not limited to, Code Division
Multiple Access (CDMA), CDMA200, 1.times.RTT, Evolution-Data Only
(EV-DO), UMTS, High-speed Packet Access (HSPA), GSM, General Packet
Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), Time
Division Multiple Access (TDMA), LTE, iDEN, and Cellular Digital
Packet Data (CDPD).
[0066] The RIMs 802(1)-802(M) may be provided in the central unit
804 that support any frequencies desired, including, but not
limited to, US FCC and Industry Canada frequencies (824-849 MHz on
uplink and 869-894 MHz on downlink), US FCC and Industry Canada
frequencies (1850-1915 MHz on uplink and 1930-1995 MHz on
downlink), US FCC and Industry Canada frequencies (1710-1755 MHz on
uplink and 2110-2155 MHz on downlink), US FCC frequencies (698-716
MHz and 776-787 MHz on uplink and 728-746 MHz on downlink), EU R
& TTE frequencies (880-915 MHz on uplink and 925-960 MHz on
downlink), EU R & TTE frequencies (1710-1785 MHz on uplink and
1805-1880 MHz on downlink), EU R & TTE frequencies (1920-1980
MHz on uplink and 2110-2170 MHz on downlink), US FCC frequencies
(806-824 MHz on uplink and 851-869 MHz on downlink), US FCC
frequencies (896-901 MHz on uplink and 929-941 MHz on downlink), US
FCC frequencies (793-805 MHz on uplink and 763-775 MHz on
downlink), and US FCC frequencies (2495-2690 MHz on uplink and
downlink).
[0067] With continuing reference to FIG. 8, the central unit 804
may convert the downlink communications signals 806D(1)-806D(R)
into a downlink MIMO communications signal 807 and provide the
downlink MIMO communications signal 807 to a plurality of optical
interfaces provided in the form of optical interface modules (OIMs)
808(1)-808(N) in this embodiment to convert the downlink MIMO
communications signal 807 into a plurality of downlink optical
fiber-based MIMO communications signals 810D(1)-810D(R). The
notation "1-N" indicates that any number of the referenced
component 1-N may be provided. The OIMs 808(1)-808(N) may be
configured to provide a plurality of optical interface components
(OICs) that contain optical-to-electrical (O/E) and
electrical-to-optical (E/O) converters, as will be described in
more detail below. The OIMs 808(1)-808(N) support the radio bands
that can be provided by the RIMs 802(1)-802(M), including the
examples previously described above.
[0068] The OIMs 808(1)-808(N) each include E/O converters to
convert the downlink MIMO communications signal 807 into the
downlink optical fiber-based MIMO communications signals
810D(1)-810D(R). The downlink optical fiber-based MIMO
communications signals 810D(1)-810D(R) are communicated over a
downlink optical fiber-based communications medium 812D to a
plurality of remote units 814(1)-814(S). The remote units
814(1)-814(S) may be configured and deployed based on the selected
system configuration determined via the process 400 of FIG. 4 to
maximize the selected system performance indicator (e.g., network
capacity). The notation "1-S" indicates that any number of the
referenced component 1-S may be provided. Remote unit O/E
converters provided in the remote units 814(1)-814(S) convert the
downlink optical fiber-based MIMO communications signals
810D(1)-810D(R) back into the downlink MIMO communications signal
807. The downlink MIMO communications signal 807 is provided to
antennas 816(1)-816(S) in the remote units 814(1)-814(S) to client
devices in the reception range of the antennas 816(1)-816(S).
[0069] The remote units 814(1)-814(S) receive a plurality of uplink
communications signals from the client devices through the antennas
816(1)-816(S). Remote unit E/O converters are also provided in the
remote units 814(1)-814(S) to convert the uplink communications
signals 818U(1)-818U(S) into a plurality of uplink optical
fiber-based communications signals 810U(1)-810U(S). The remote
units 814(1)-814(S) communicate the uplink optical fiber-based
communications signals 810U(1)-810U(S) over an uplink optical
fiber-based communications medium 812U to the OIMs 808(1)-808(N) in
the central unit 804. The OIMs 808(1)-808(N) include O/E converters
that convert the received uplink optical fiber-based communications
signals 810U(1)-810U(S) into a plurality of uplink communications
signals 820U(1)-820U(S), which are processed by the RIMs
802(1)-802(M) and provided as the uplink communications signals
820U(1)-820U(S). The central unit 804 may provide the uplink
communications signals 820U(1)-820U(S) to a base station or other
communications system.
[0070] Note that the downlink optical fiber-based communications
medium 812D and the uplink optical fiber-based communications
medium 812U connected to each of the remote units 814(1)-814(S) may
be a common optical fiber-based communications medium, wherein for
example, wave division multiplexing (WDM) is employed to provide
the downlink optical fiber-based MIMO communications signals
810D(1)-810D(R) and the uplink optical fiber-based communications
signals 810U(1)-810U(S) on the same optical fiber-based
communications medium.
[0071] The WDS 800 of FIG. 8 may be provided in an indoor
environment, as illustrated in FIG. 9. FIG. 9 is a partial
schematic cut-away diagram of an exemplary building infrastructure
900 in which a WDS, such as the WDS 800 of FIG. 8, including a
plurality of remote units configured and deployed based on the
selected system configuration determined via the process 400 of
FIG. 4 to maximize the selected system performance indicator (e.g.,
network capacity) of the WDS 800. The building infrastructure 900
in this embodiment includes a first (ground) floor 902(1), a second
floor 902(2), and a third floor 902(3). The floors 902(1)-902(3)
are serviced by a central unit 904 to provide antenna coverage
areas 906 in the building infrastructure 900. The central unit 904
is communicatively coupled to a base station 908 to receive
downlink communications signals 910D from the base station 908. The
central unit 904 is communicatively coupled to a plurality of
remote units 912 to distribute the downlink communications signals
910D to the remote units 912 and to receive uplink communications
signals 910U from the remote units 912, as previously discussed
above. The downlink communications signals 910D and the uplink
communications signals 910U communicated between the central unit
904 and the remote units 912 are carried over a riser cable 914.
The riser cable 914 may be routed through interconnect units (ICUs)
916(1)-916(3) dedicated to each of the floors 902(1)-902(3) that
route the downlink communications signals 910D and the uplink
communications signals 910U to the remote units 912 and also
provide power to the remote units 912 via array cables 918.
[0072] Unless otherwise expressly stated, it is in no way intended
that any method set forth herein be construed as requiring that its
steps be performed in a specific order. Accordingly, where a method
claim does not actually recite an order to be followed by its
steps, or it is not otherwise specifically stated in the claims or
descriptions that the steps are to be limited to a specific order,
it is in no way intended that any particular order be inferred.
[0073] It will be apparent to those skilled in the art that various
modifications and variations can be made without departing from the
spirit or scope of the invention. Since modifications,
combinations, sub-combinations and variations of the disclosed
embodiments incorporating the spirit and substance of the invention
may occur to persons skilled in the art, the invention should be
construed to include everything within the scope of the appended
claims and their equivalents.
* * * * *