CN109039410B - Beam forming method of heterogeneous cloud wireless access network and communication network - Google Patents

Beam forming method of heterogeneous cloud wireless access network and communication network Download PDF

Info

Publication number
CN109039410B
CN109039410B CN201810678376.6A CN201810678376A CN109039410B CN 109039410 B CN109039410 B CN 109039410B CN 201810678376 A CN201810678376 A CN 201810678376A CN 109039410 B CN109039410 B CN 109039410B
Authority
CN
China
Prior art keywords
mbs
rrh
constraint
optimization problem
access network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810678376.6A
Other languages
Chinese (zh)
Other versions
CN109039410A (en
Inventor
左加阔
杨龙祥
鲍楠
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN201810678376.6A priority Critical patent/CN109039410B/en
Publication of CN109039410A publication Critical patent/CN109039410A/en
Application granted granted Critical
Publication of CN109039410B publication Critical patent/CN109039410B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • H04B7/043—Power distribution using best eigenmode, e.g. beam forming or beam steering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/24—Cell structures
    • H04W16/28—Cell structures using beam steering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/243—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account interferences
    • H04W52/244—Interferences in heterogeneous networks, e.g. among macro and femto or pico cells or other sector / system interference [OSI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/38—TPC being performed in particular situations
    • H04W52/42—TPC being performed in particular situations in systems with time, space, frequency or polarisation diversity

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention provides a beam forming method of a heterogeneous cloud wireless access network and a communication network using the same, wherein the method comprises the steps of calculating the data transmission rate of a macro cell user (MU); calculating the data transmission rate of a wireless remote radio unit user (RU) and calculating the total energy efficiency of the heterogeneous cloud wireless access network; determining a beamforming vector joint optimization problem of MBS and RRHs; and a final step of solving the beamforming problem. The communication network using the method comprises a baseband processing unit pool, a macro base station MBS, a plurality of wireless remote radio frequency units RRHs, a plurality of macro cellular users and a plurality of RRH users. Aiming at the problem that the existing beam forming technical scheme is not suitable for a large-scale MIMO-assisted heterogeneous cloud wireless access network, the beam forming method of the large-scale MIMO-assisted heterogeneous cloud wireless access network takes the total energy efficiency of the maximized network as an optimization target, improves the energy efficiency of the heterogeneous cloud wireless access network, and reduces the total power consumption of MBS and RRHs.

Description

Beam forming method of heterogeneous cloud wireless access network and communication network
Technical Field
The invention relates to the technical field of wireless communication, in particular to a beam forming method of a large-scale MIMO (multiple input multiple output) assisted heterogeneous cloud wireless access network and a communication network for realizing the method.
Background
With the rapid development of the internet of things and the mobile internet, the existing wireless communication system faces the needs of spectrum resource shortage, excessive energy consumption and difficulty in meeting the future communication development of the transmission rate. Therefore, how to utilize limited resources to greatly increase the transmission rate and reduce the energy consumption becomes a problem to be considered in the next generation wireless communication system.
As a new network architecture, a heterogeneous cloud wireless access network is considered as one of the most promising solutions to meet the requirements of future wireless communication networks. The heterogeneous cloud wireless access network uses the advantages of the control and service plane separation technology realized by high-power nodes in the heterogeneous network and the efficient local service supporting of the wireless remote radio frequency unit in the cloud wireless access network for reference, and can remarkably improve the performance of the wireless network by utilizing the advanced cooperative signal processing technology. In addition, the heterogeneous cloud wireless access network has the characteristics of low cost, flexible network deployment, high resource utilization rate and the like. Meanwhile, the newly emerging large-scale multiple-input multiple-output (MIMO) technology can improve the capacity and the spectrum efficiency of a communication system by multiples and obtain higher space diversity capability by configuring dozens or even hundreds of antennas at the base station side. Therefore, the heterogeneous cloud wireless access network is combined with the large-scale MIMO technology, the respective technical advantages are exerted, and the requirement of high-performance communication in the next generation wireless network can be met.
In view of the above, a new method for beam forming in a heterogeneous cloud radio access network is needed to solve the above problem.
Disclosure of Invention
The invention aims to provide a large-scale MIMO-assisted heterogeneous cloud wireless access network beam forming method, namely, a large-scale MIMO technology is introduced into a heterogeneous cloud wireless access network, the total energy efficiency of the network is maximized as an optimization target, and under the condition that power constraint is considered, the beam forming vectors of a macro base station and a wireless remote radio frequency unit are jointly optimized.
In order to achieve the above object, the present invention provides a beam forming method for a heterogeneous cloud radio access network, including the following steps:
s1, calculating the data transmission rate of the MU of the macro cell user
Figure GDA0003068967970000021
S2, calculating data transmission rate of RU
Figure GDA0003068967970000022
S3, calculating the total energy efficiency xi of the heterogeneous cloud wireless access network:
Figure GDA0003068967970000023
wherein the content of the first and second substances,
R(wj，vk) Is the total data transmission rate of RUs and MUs in a heterogeneous cloud wireless access network, the RUs representing a number of RUs, the MUs representing a number of MUs,
P(wj，vk) The total power consumption of a radio remote radio unit RRH and a macro base station MBS in the heterogeneous cloud wireless access network is calculated;
s4, determining the beamforming vector joint optimization problem of MBS and RRHs, wherein the optimization problem is expressed as:
Figure GDA0003068967970000024
constraint (1):
Figure GDA0003068967970000025
constraint (2):
Figure GDA0003068967970000026
wherein, PRRHAnd PMBSRespectively the maximum transmitting power threshold values of all RRHs and MBS, the RRHsRepresents a number of RRHs;
s5, solving the beam forming problem, and equating the optimization problem in the step S4 as the following convex optimization problem:
Figure GDA0003068967970000031
constraint (1), constraint (2) and
constraint (3):
Figure GDA0003068967970000032
constraint (4):
Figure GDA0003068967970000033
constraint (5):
Figure GDA0003068967970000034
constraint (6):
Figure GDA0003068967970000035
wherein j represents the index number of the RU; k andkall represent index numbers of the MU, where k ≠k(ii) a J represents the total number of RUs; k represents the total number of MU; w is ajRepresenting a beam formed by N beamforming vectors wn,jThe formed cumulative beamforming vectors are then used to form,
Figure GDA0003068967970000036
a beamforming vector for RRH n versus RU j, where n represents the index number of the RRH;
Figure GDA0003068967970000037
beamforming vector, v, for MBS to MU k k For MBS to MUkA beamforming vector;
Figure GDA0003068967970000038
is interference channel vector between MBS and RU j;
Figure GDA0003068967970000039
is the channel vector between MBS and MU k;
Figure GDA00030689679700000310
representing a channel consisting of N channel vectors hn,jThe accumulated channel vector of the component(s),
Figure GDA00030689679700000311
is the channel vector between RRH n and RU j;
Figure GDA00030689679700000312
representing a channel consisting of N channel vectors gn,kThe accumulated channel vector of the component(s),
Figure GDA00030689679700000313
is an interference channel vector between RRH n and MU k;
Figure GDA00030689679700000314
represents TMA column vector of dimensions;
Figure GDA00030689679700000315
represents TRA row vector of dimensions, where TMIndicating the number of antennas, T, with which the MBS is equippedRRepresenting the number of antennas provided for the RRH; the number of the lambda-beams is increased,
Figure GDA00030689679700000316
to introduce an auxiliary variable.
As a further improvement of the present invention, the optimization problem (a) is solved in step S5 by transforming into the following optimization problem:
Figure GDA00030689679700000317
constraint (1):
Figure GDA0003068967970000041
constraint (2):
Figure GDA0003068967970000042
as a further improvement of the present invention, the optimization problem (C) is further equivalent to the optimization problem (B), wherein the constraint conditions are:
constraint (7):
Figure GDA0003068967970000043
constraint (3):
Figure GDA0003068967970000044
constraint (8):
Figure GDA0003068967970000045
constraint (4):
Figure GDA0003068967970000046
constraint (1) and constraint (2).
As a further improvement of the invention, the non-convex constraint (7) and the non-convex constraint (8) are respectively approximated to the following convex constraints:
constraint (5):
Figure GDA0003068967970000047
constraint (6):
Figure GDA0003068967970000048
as a further improvement of the present invention, the step of solving the optimization problem (B) is as follows:
step 1: initialization
Figure GDA0003068967970000049
λ，j∈J，k∈K；
Step 2: solving forThe convex optimization problem (B) is solved
Figure GDA00030689679700000410
vk，wj；
And step 3: updating according to the solution in step 2
Figure GDA00030689679700000411
And
Figure GDA00030689679700000412
and 4, step 4: repeating the steps 2-3 until
Figure GDA00030689679700000413
And
Figure GDA00030689679700000414
converging;
and 5: updating
Figure GDA0003068967970000051
Step 6, repeating the steps 2-5 until the lambda is converged to obtain an optimal solution
Figure GDA0003068967970000052
And
Figure GDA0003068967970000053
respectively represents wjAnd vkAnd (4) corresponding optimal solution.
In order to achieve the above object, the present invention further provides a heterogeneous cloud wireless access network beam forming method based on massive MIMO assistance, comprising the following steps:
s1, calculating a data transmission rate of the MU, where the data transmission rate of the MU k is:
Figure GDA0003068967970000054
wherein j denotes the RU index number(ii) a k andkall represent index numbers of the MU, where k ≠k，
Figure GDA0003068967970000055
Beamforming vector, v, for MBS to MU kkFor MBS to MUkThe number of the beamforming vectors is determined,
Figure GDA0003068967970000056
for the channel vector between MBS and MU k,
Figure GDA0003068967970000057
Figure GDA0003068967970000058
is an interference channel vector between RRH n and MU k, where n represents the index number of the RRH,
Figure GDA0003068967970000059
Figure GDA00030689679700000510
beamforming vector for RRH N to RU J, J denotes the total number of RUs, K denotes the total number of MUs, N denotes the total number of RRHs, MU K, where K ∈ K, C denotes the complex field, (g)TRepresenting a transpose; the MU is a macro cellular user, the MUs are a plurality of macro cellular users, the MBS is a macro base station, and the RRH is a wireless remote radio frequency unit;
s2, calculating a data transmission rate of RU, the data transmission rate of RU j being:
Figure GDA00030689679700000511
wherein the content of the first and second substances,
Figure GDA00030689679700000512
is the channel vector between RRH n and RU j,
Figure GDA00030689679700000513
for interference signals between MBS and RU jA lane vector, RU J, where J ∈ J; the RUs are wireless remote radio frequency unit users, and the RUs are a plurality of wireless remote radio frequency unit users;
s3, calculating the total energy efficiency of the heterogeneous cloud wireless access network, wherein the total data transmission rate of the RUs and MUs in the heterogeneous cloud wireless access network is as follows:
Figure GDA0003068967970000061
the total power consumption of the RRH and the MBS in the heterogeneous cloud wireless access network is as follows:
Figure GDA0003068967970000062
then, the total energy efficiency of the heterogeneous cloud wireless access network is:
Figure GDA0003068967970000063
s4, determining a beamforming vector joint optimization problem of MBS and RRHs, wherein the large-scale MIMO-assisted heterogeneous cloud wireless access network beamforming vector joint optimization problem can be expressed as:
Figure GDA0003068967970000064
Figure GDA0003068967970000065
Figure GDA0003068967970000066
wherein, PRRHAnd PMBSRespectively the maximum transmitting power threshold values of all RRHs and MBS; the RRHs are a plurality of RRHs;
s5, solving the beam forming problem, converting the solving of the non-convex and non-linear optimization problem (6) into the solving of the following optimization problem (7)
Figure GDA0003068967970000067
s.t.(6b),(6c) (7b)
The objective function in the optimization problem (7) is non-convex, and auxiliary variables are introduced for solving conveniently
Figure GDA0003068967970000068
Figure GDA0003068967970000069
The optimization problem (7) can be equivalent to the following optimization problem:
Figure GDA00030689679700000610
Figure GDA00030689679700000611
Figure GDA00030689679700000612
Figure GDA0003068967970000071
Figure GDA0003068967970000072
s.t.(6b),(6c) (8f)
in the optimization problem (8), the objective function and constraints (6b), (6c), (8c), (8e) are all convex, while constraints (8b) and (8d) are non-convex, and constraints (8b) and (8d) can be approximated as convex constraints as follows:
Figure GDA0003068967970000073
Figure GDA0003068967970000074
from the above analysis, the optimization problem (7) can be finally equivalent to the following convex optimization problem, namely:
Figure GDA0003068967970000075
s.t.(6b),(6c),(8c),(8e),(9),(10) (11b)
wherein the content of the first and second substances,kindex number, w, representing MUjRepresenting a beam formed by N beamforming vectors wn,jForming a cumulative beamforming vector;
Figure GDA0003068967970000076
beamforming vectors for MBS to MU k; MBS with large scale antenna array, TMIndicating the number of antennas, T, with which the MBS is equippedRRepresenting the number of antennas provided for the RRH;
Figure GDA0003068967970000077
is interference channel vector between MBS and RU j;
Figure GDA0003068967970000078
is the channel vector between MBS and MU k;
Figure GDA0003068967970000079
representing a channel consisting of N channel vectors hn,jA constituent accumulated channel vector; the number of the lambda-beams is increased,
Figure GDA00030689679700000710
to introduce an auxiliary variable.
As a further improvement of the invention, the steps for solving the original optimization problem (6) are as follows:
step 1: initialization
Figure GDA00030689679700000711
λ，j∈J，k∈K；
Step 2: solving the convex optimization problem (B) to obtain a solution
Figure GDA00030689679700000712
vk，wj；
And step 3: updating according to the solution in step 2
Figure GDA00030689679700000713
And
Figure GDA00030689679700000714
and 4, step 4: repeating the steps 2-3 until
Figure GDA0003068967970000081
And
Figure GDA0003068967970000085
converging;
and 5: updating
Figure GDA0003068967970000082
Step 6, repeating the steps 2-5 until the lambda is converged to obtain an optimal solution
Figure GDA0003068967970000083
And
Figure GDA0003068967970000084
respectively represents wjAnd vkAnd (4) corresponding optimal solution.
In order to achieve the above object, the present invention further provides a communication network, where the communication network includes a baseband processing unit pool, a macro base station MBS, a radio remote unit RRH, a plurality of macro cell users, and a plurality of RRH users, where the MBS provides wide area radio signal coverage, the RRH provides radio signal coverage in a hot spot area or a fringe area, the MBS is equipped with a large-scale antenna array, and the macro cell users and the RRH users are equipped with at least one antenna, and the communication network may implement the beam forming method according to any one of the foregoing embodiments.
As a further improvement of the invention, the MBS is provided with a large-scale antenna array, the number of the antennas is TM, the RRH is provided with TR antennas, wherein TM＞＞TR。
As a further improvement of the invention, the number of antennas TMNot less than one hundred.
The invention has the beneficial effects that: aiming at the problem that the existing beam forming technical scheme is not suitable for a large-scale MIMO-assisted heterogeneous cloud wireless access network, the beam forming method of the large-scale MIMO-assisted heterogeneous cloud wireless access network is provided, the method takes the total energy efficiency of the network as an optimization target, and inhibits the interference in the heterogeneous cloud wireless access network by performing combined optimization on the beam forming vectors of MBS and RRHs, so that the energy efficiency of the heterogeneous cloud wireless access network is improved, and the total power consumption of MBS and RRHs is reduced.
Drawings
Fig. 1 is a schematic diagram of a communication network implementing the method of the present invention.
Fig. 2 is a flowchart of a massive MIMO-assisted heterogeneous cloud wireless access network beamforming method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
It should be emphasized that in describing the present invention, various formulas and constraints are identified with consistent labels, but the use of different labels to identify the same formula and/or constraint is not precluded and is provided for the purpose of more clearly illustrating the features of the present invention.
As shown in fig. 1 and fig. 2, the present invention provides a heterogeneous cloud radio access network based on massive MIMO assistance. The communication network comprises a baseband processing unit pool, aMacro Base Station (Macro Base Station MBS), N Radio Remote Heads (RRHs), K Macro cellular users (Macro cellular users are denoted by MU), and J RRH users (RRH users are denoted by RU). MBS provides wide-area wireless signal coverage, while RRHs are primarily responsible for wireless signal coverage in hot spot areas or edge areas. MBS and RRHs provide communication services for MUs and RUs, respectively. MUs and RUs denote MU and RU, respectively. Suppose that the MBS is provided with a large-scale antenna array with the number of antennas TM(TM can take on hundreds of values) and RRH is provided with TRRoot antenna (T)M＞＞TR) Wherein T isMIndicating the number of antennas, T, with which the MBS is equippedRIndicating the number of antennas provided for the RRH, and one antenna for all users, as shown in fig. 1. Let N ═ {1,2, …, N } denote the set of all RRHs, K ═ {1,2, …, K } denotes the set of all MUs, J ═ {1,2, …, J } denotes the set of all RUs, and RRHs denote several RRHs.
Order to
Figure GDA0003068967970000091
For the beamforming vector of MBS to MU k,
Figure GDA0003068967970000092
the signal for MU k is sent for MBS,
Figure GDA0003068967970000093
is the channel vector between MBS and MU k. Then, the signal received by MU k is:
Figure GDA0003068967970000094
wherein
Figure GDA0003068967970000095
Is an interference channel vector between RRH n and MU k, where n represents the index number of the RRH,
Figure GDA0003068967970000101
a beamforming vector for RRH N versus RU j, N representing the total number of RRHs,
Figure GDA0003068967970000102
for signals sent by the RRHs to RU j,
Figure GDA0003068967970000103
for received noise, CN (0,1) denotes a complex Gaussian distribution obeying a mean vector of 0 and a covariance of 1, C denotes a complex field, (g)TIndicating transposition. Wherein the content of the first and second substances,
Figure GDA0003068967970000104
represents TMA column vector of dimensions;
Figure GDA0003068967970000105
represents TRA row vector of dimensions; the data transmission rate of MU k is:
Figure GDA0003068967970000106
the signal received by RU j is:
Figure GDA0003068967970000107
wherein the content of the first and second substances,
Figure GDA0003068967970000108
Figure GDA0003068967970000109
is the channel vector between RRH n and RU j,
Figure GDA00030689679700001010
for the interfering channel vector between MBS and RU j,
Figure GDA00030689679700001011
is the received noise. Then, the data transmission rate for RU j is:
Figure GDA00030689679700001012
therefore, the total data transmission rate of RUs and MUs in the heterogeneous cloud wireless access network is:
Figure GDA00030689679700001013
the total power consumption of RRHs and MBS in the heterogeneous cloud wireless access network is as follows:
Figure GDA00030689679700001014
then, the total energy efficiency of the heterogeneous cloud wireless access network is:
Figure GDA0003068967970000111
the MBS and RRHs joint beamforming problem in the heterogeneous cloud wireless access network can be expressed as:
Figure GDA0003068967970000112
Figure GDA0003068967970000113
Figure GDA0003068967970000114
wherein P isRRHAnd PMBSRespectively the maximum transmitting power threshold values of all RRHs and MBS; j represents the index number of the RU; k andkall represent the index number of the MU, wherein k ≠k。
Before solving the optimization problem (6) or (a), the following differential optimization problem is introduced:
Figure GDA0003068967970000115
s.t.(6b),(6c) (7b)
order to
Figure GDA0003068967970000116
And
Figure GDA0003068967970000117
to optimize the optimal solution of problem (6) or (A),
Figure GDA0003068967970000118
according to the theory of non-linear programming, if and only if
Figure GDA0003068967970000119
When true, optimization problem (6) or (a) and optimization problem (7) or (C) have the same solution. Therefore, solving the optimization problem (6) or (a) can be converted into solving the optimization problem (7) or (C).
The objective function of the optimization problem (7) or (C) is non-convex, and auxiliary variables are introduced to facilitate solution
Figure GDA0003068967970000121
The optimization problem (7) or (C) may be equivalent to the following optimization problem:
Figure GDA0003068967970000122
Figure GDA0003068967970000123
Figure GDA0003068967970000124
Figure GDA0003068967970000125
Figure GDA0003068967970000126
s.t.(6b),(6c) (8f)
in the optimization problem (8) or (B), other constraints and objective functions are convex except for the constraints (8B) and (8 d). Constraints (8b) and (8d) are processed below to define functions g (x, y) xy and
Figure GDA0003068967970000127
wherein f (x, y) ≧ g (x, y). It is apparent that f (x, y) is a convex function when
Figure GDA0003068967970000128
When f (x, y) is g (x, y). Based on the above analysis, constraints (8b) and (8d) can be approximated as:
Figure GDA0003068967970000129
Figure GDA00030689679700001210
from the above analysis, the optimization problem (7) can be finally equivalent to the following optimization problem, namely:
Figure GDA00030689679700001211
s.t.(6b),(6c),(8c),(8e),(9),(10) (11b)
as shown in fig. 2, the specific steps of solving the original optimization problem (6) or (a) by the beam forming method provided by the present scheme are as follows:
step 1: initialization
Figure GDA0003068967970000131
λ，j∈J，k∈K；
Step 2: solving convex optimization problem(11) Get a solution
Figure GDA0003068967970000132
vk，wj；
And step 3: updating according to the solution in step 2
Figure GDA0003068967970000133
And
Figure GDA0003068967970000134
and 4, step 4: repeating the steps 2-3 until
Figure GDA0003068967970000135
And
Figure GDA0003068967970000136
converging;
and 5: updating
Figure GDA0003068967970000137
Step 6, repeating the steps 2-5 until the convergence lambda is converged to obtain the optimal solution
Figure GDA0003068967970000138
And
Figure GDA0003068967970000139
respectively represents wjAnd vkAnd (4) corresponding optimal solution.
Wherein the ratio of lambda to lambda is,
Figure GDA00030689679700001310
to introduce an auxiliary variable.
Figure GDA00030689679700001311
Is introduced for approximating the non-convex constraint (7) as a convex constraint (5);
Figure GDA00030689679700001312
is to be measured byThe convex constraint (8) is introduced approximately for the convex constraint (6); λ is introduced to transform the objective function in the form of a fraction in the optimization problem a into the objective function in the form of a difference in the optimization problem C.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.

Claims (10)

1. A beam forming method of a heterogeneous cloud wireless access network is characterized by comprising the following steps:
s1, calculating the data transmission rate of the MU of the macro cell user
Figure FDA0003068967960000011
S2, calculating data transmission rate of RU
Figure FDA0003068967960000012
S3, calculating the total energy efficiency xi of the heterogeneous cloud wireless access network:
Figure FDA0003068967960000013
wherein the content of the first and second substances,
R(wj，vk) Is the total data transmission rate of RUs and MUs in a heterogeneous cloud wireless access network, the RUs representing a number of RUs, the MUs representing a number of MUs,
P(wj，vk) The total power consumption of a radio remote radio unit RRH and a macro base station MBS in the heterogeneous cloud wireless access network is calculated;
s4, determining the beamforming vector joint optimization problem of MBS and RRHs, wherein the optimization problem is expressed as:
Figure FDA0003068967960000014
constraint (1):
Figure FDA0003068967960000015
constraint (2):
Figure FDA0003068967960000016
wherein, PRRHAnd PMBSRespectively the maximum transmitting power threshold values of all RRHs and MBS, wherein the RRHs represent a plurality of RRHs;
s5, solving the beam forming problem, and equating the optimization problem in the step S4 as the following convex optimization problem:
Figure FDA0003068967960000017
constraint (1), constraint (2) and
constraint (3):
Figure FDA0003068967960000021
constraint (4):
Figure FDA0003068967960000022
constraint (5):
Figure FDA0003068967960000023
constraint (6):
Figure FDA0003068967960000024
wherein j represents the index number of the RU; k andkall represent index numbers of the MU, where k ≠k(ii) a J watchShowing the total number of RUs; k represents the total number of MU; w is ajRepresenting a beam formed by N beamforming vectors wn,jThe formed cumulative beamforming vectors are then used to form,
Figure FDA0003068967960000025
a beamforming vector for RRH n versus RU j, where n represents the index number of the RRH;
Figure FDA0003068967960000026
beamforming vector, v, for MBS to MU k k For MBS to MUkA beamforming vector;
Figure FDA0003068967960000027
is interference channel vector between MBS and RU j;
Figure FDA0003068967960000028
is the channel vector between MBS and MU k;
Figure FDA0003068967960000029
representing a channel consisting of N channel vectors hn,jThe accumulated channel vector of the component(s),
Figure FDA00030689679600000210
is the channel vector between RRH n and RU j;
Figure FDA00030689679600000211
representing a channel consisting of N channel vectors gn,kThe accumulated channel vector of the component(s),
Figure FDA00030689679600000212
is an interference channel vector between RRH n and MU k;
Figure FDA00030689679600000213
represents TMA column vector of dimensions;
Figure FDA00030689679600000214
represents TRA row vector of dimensions, where TMIndicating the number of antennas, T, with which the MBS is equippedRRepresenting the number of antennas provided for the RRH; the number of the lambda-beams is increased,
Figure FDA00030689679600000215
to introduce an auxiliary variable.
2. The beamforming method according to claim 1, wherein: the optimization problem (a) is solved in step S5 as follows:
Figure FDA00030689679600000216
constraint (1):
Figure FDA00030689679600000217
constraint (2):
Figure FDA00030689679600000218
3. the beamforming method according to claim 2, wherein: further equating the optimization problem (C) to the optimization problem (B), wherein the constraints are:
constraint (7):
Figure FDA0003068967960000031
constraint (3):
Figure FDA0003068967960000032
constraint (8):
Figure FDA0003068967960000033
constraint (4):
Figure FDA0003068967960000034
constraint (1) and constraint (2).
4. The beamforming method according to claim 3, wherein: respectively approximating the non-convex constraint condition (7) and the non-convex constraint condition (8) as the following convex constraint conditions:
constraint (5):
Figure FDA0003068967960000035
constraint (6):
Figure FDA0003068967960000036
5. the beamforming method according to any of claims 1-4, wherein: the steps for solving the optimization problem (B) are as follows:
step 1: initialization
Figure FDA0003068967960000037
λ，j∈J，k∈K；
Step 2: solving the convex optimization problem (B) to obtain a solution
Figure FDA0003068967960000038
vk，wj；
And step 3: updating according to the solution in step 2
Figure FDA0003068967960000039
And
Figure FDA00030689679600000310
and 4, step 4: repeating the steps 2-3 until
Figure FDA00030689679600000311
And
Figure FDA00030689679600000312
converging;
and 5: updating
Figure FDA00030689679600000313
Step 6, repeating the steps 2-5 until the lambda is converged to obtain an optimal solution
Figure FDA00030689679600000314
And
Figure FDA00030689679600000315
respectively represents wjAnd vkAnd (4) corresponding optimal solution.
6. A large-scale MIMO-assisted heterogeneous cloud wireless access network beam forming method is characterized by comprising the following steps:
s1, calculating a data transmission rate of the MU, where the data transmission rate of the MU k is:
Figure FDA0003068967960000041
wherein j represents the index number of the RU; k andkall represent index numbers of the MU, where k ≠k，
Figure FDA0003068967960000042
Beamforming vector, v, for MBS to MU k k For MBS to MUkThe number of the beamforming vectors is determined,
Figure FDA0003068967960000043
for the channel vector between MBS and MU k,
Figure FDA0003068967960000044
Figure FDA0003068967960000045
is an interference channel vector between RRH n and MU k, where n represents the index number of the RRH,
Figure FDA0003068967960000046
Figure FDA0003068967960000047
beamforming vector for RRH N to RU J, J denotes the total number of RUs, K denotes the total number of MUs, N denotes the total number of RRHs, MU K, where K ∈ K, C denotes the complex field, (g)TRepresenting a transpose; the MU is a macro cellular user, the MUs are a plurality of macro cellular users, the MBS is a macro base station, and the RRH is a wireless remote radio frequency unit;
s2, calculating a data transmission rate of RU, the data transmission rate of RU j being:
Figure FDA0003068967960000048
wherein the content of the first and second substances,
Figure FDA0003068967960000049
Figure FDA00030689679600000410
is the channel vector between RRH n and RU j,
Figure FDA00030689679600000411
is an interference channel vector between MBS and RU J, wherein J belongs to J; the RUs are wireless remote radio frequency unit users, and the RUs are a plurality of wireless remote radio frequency unit users;
s3, calculating the total energy efficiency of the heterogeneous cloud wireless access network, wherein the total data transmission rate of the RUs and MUs in the heterogeneous cloud wireless access network is as follows:
Figure FDA00030689679600000412
the total power consumption of the RRH and the MBS in the heterogeneous cloud wireless access network is as follows:
Figure FDA0003068967960000051
then, the total energy efficiency of the heterogeneous cloud wireless access network is:
Figure FDA0003068967960000052
s4, determining a beamforming vector joint optimization problem of MBS and RRHs, wherein the large-scale MIMO-assisted heterogeneous cloud wireless access network beamforming vector joint optimization problem can be expressed as:
Figure FDA0003068967960000053
Figure FDA0003068967960000054
Figure FDA0003068967960000055
wherein, PRRHAnd PMBSRespectively the maximum transmitting power threshold values of all RRHs and MBS; the RRHs are a plurality of RRHs;
s5, solving the beam forming problem, converting the solving of the non-convex and non-linear optimization problem (6) into the solving of the following optimization problem (7)
Figure FDA0003068967960000056
s.t.(6b),(6c) (7b)
The objective function in the optimization problem (7) is non-convex, and auxiliary variables are introduced for solving conveniently
Figure FDA0003068967960000057
Figure FDA0003068967960000058
The optimization problem (7) can be equivalent to the following optimization problem:
Figure FDA0003068967960000059
Figure FDA00030689679600000510
Figure FDA00030689679600000511
Figure FDA00030689679600000512
Figure FDA00030689679600000513
s.t.(6b),(6c) (8f)
in the optimization problem (8), the objective function and constraints (6b), (6c), (8c), (8e) are all convex, while constraints (8b) and (8d) are non-convex, and constraints (8b) and (8d) can be approximated as convex constraints as follows:
Figure FDA0003068967960000061
Figure FDA0003068967960000062
from the above analysis, the optimization problem (7) can be finally equivalent to the following convex optimization problem, namely:
Figure FDA0003068967960000063
s.t.(6b),(6c),(8c),(8e),(9),(10)(11b)
wherein the content of the first and second substances,kindex number, w, representing MUjRepresenting a beam formed by N beamforming vectors wn,jForming a cumulative beamforming vector;
Figure FDA0003068967960000064
beamforming vectors for MBS to MU k; MBS with large scale antenna array, TMIndicating the number of antennas, T, with which the MBS is equippedRRepresenting the number of antennas provided for the RRH;
Figure FDA0003068967960000065
is interference channel vector between MBS and RU j;
Figure FDA0003068967960000066
is the channel vector between MBS and MU k;
Figure FDA0003068967960000067
representing a channel consisting of N channel vectors hn,jA constituent accumulated channel vector; the number of the lambda-beams is increased,
Figure FDA0003068967960000068
to introduce an auxiliary variable.
7. The beamforming method according to claim 6, wherein: the steps for solving the original optimization problem (6) are as follows:
step 1: initialization
Figure FDA0003068967960000069
λ，j∈J，k∈K；
Step 2: solving the convex optimization problem (B) to obtain a solution
Figure FDA00030689679600000610
vk，wj；
And step 3: updating according to the solution in step 2
Figure FDA00030689679600000611
And
Figure FDA00030689679600000612
and 4, step 4: repeating the steps 2-3 until
Figure FDA00030689679600000613
And
Figure FDA00030689679600000614
converging;
and 5: updating
Figure FDA00030689679600000615
Step 6, repeating the steps 2-5 until the lambda is converged to obtain an optimal solution
Figure FDA00030689679600000616
And
Figure FDA00030689679600000617
respectively represents wjAnd vkAnd (4) corresponding optimal solution.
8. A communication network comprising a pool of baseband processing units, a macro base station MBS, a radio remote unit RRH, a plurality of macro-cellular users and a plurality of RRH users, the MBS providing wide area radio signal coverage, the RRH providing radio signal coverage in hot spot or edge areas, the MBS being equipped with a large scale antenna array, the macro-cellular users and the RRH users being equipped with at least one antenna, characterized in that: the communication network may implement the beamforming method of any of claims 1-4 or claims 6-7.
9. The communication network of claim 8, wherein: MBS is provided with large-scale antenna array with the number of antennas being TMRRH is provided with TRA root antenna, wherein TM＞＞TR。
10. The communication network of claim 9, wherein: the number of antennas TMNot less than one hundred.
CN201810678376.6A 2018-06-27 2018-06-27 Beam forming method of heterogeneous cloud wireless access network and communication network Active CN109039410B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810678376.6A CN109039410B (en) 2018-06-27 2018-06-27 Beam forming method of heterogeneous cloud wireless access network and communication network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810678376.6A CN109039410B (en) 2018-06-27 2018-06-27 Beam forming method of heterogeneous cloud wireless access network and communication network

Publications (2)

Publication Number Publication Date
CN109039410A CN109039410A (en) 2018-12-18
CN109039410B true CN109039410B (en) 2021-06-29

Family

ID=64610860

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810678376.6A Active CN109039410B (en) 2018-06-27 2018-06-27 Beam forming method of heterogeneous cloud wireless access network and communication network

Country Status (1)

Country Link
CN (1) CN109039410B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109818662A (en) * 2019-03-12 2019-05-28 电子科技大学 Mixed-beam manufacturing process in full duplex cloud access number energy integrated network
CN112911608B (en) * 2021-01-14 2021-11-23 浙江大学 Large-scale access method for edge-oriented intelligent network

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106211300A (en) * 2016-06-30 2016-12-07 南京理工大学 The effect of isomery cloud Radio Access Network safe perception energy and power distribution optimization method
CN106658514A (en) * 2016-10-26 2017-05-10 桂林电子科技大学 Energy efficiency and frequency spectrum efficiency balance method for micro base station super dense disposition heterogeneous network
CN108063632A (en) * 2018-02-27 2018-05-22 哈尔滨工业大学 Cooperation resource allocation methods based on efficiency in isomery cloud access network

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9125047B2 (en) * 2012-07-26 2015-09-01 Nec Laboratories America, Inc. Cloud-based radio access network for small cells

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106211300A (en) * 2016-06-30 2016-12-07 南京理工大学 The effect of isomery cloud Radio Access Network safe perception energy and power distribution optimization method
CN106658514A (en) * 2016-10-26 2017-05-10 桂林电子科技大学 Energy efficiency and frequency spectrum efficiency balance method for micro base station super dense disposition heterogeneous network
CN108063632A (en) * 2018-02-27 2018-05-22 哈尔滨工业大学 Cooperation resource allocation methods based on efficiency in isomery cloud access network

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Energy Efficient Power Allocation and User Association in Heterogeneous Cloud Radio Access Networks;Chunfeng Yuan，Yadong Li，Changchuan Yin;《2017 3rd IEEE International Conference on Computer and Communications》;20180326;第1-15页 *
Inter-Tier Interference Suppression in Heterogeneous Cloud Radio Access Networks;Mugen Peng，Hongyu Xiang,etal.;《IEEE Access》;20151102;第1-6页 *
异构无线网络节能机制研究与网络性能分析;张媛;《中国博士学位论文全文数据库（电子期刊)信息科技辑》;20180215;第I136-92页 *

Also Published As

Publication number Publication date
CN109039410A (en) 2018-12-18

Similar Documents

Publication Publication Date Title
CN111447631A (en) Satellite-ground combined beam forming and power distribution method based on non-orthogonal multiple access technology
CN109104225B (en) Large-scale MIMO beam domain multicast transmission method with optimal energy efficiency
CN110299937B (en) Beam forming method for uplink MIMO-NOMA wireless communication system
CN109194373B (en) Large-scale MIMO beam domain combined unicast and multicast transmission method
CN113114343B (en) High-energy-efficiency intelligent dynamic beam forming method for multi-beam satellite
CN109714091B (en) Iterative hybrid precoding method based on hierarchical design in millimeter wave MIMO system
CN110365388B (en) Low-complexity millimeter wave multicast beam forming method
CN104506224A (en) Low complexity three-dimensional (3D) beam forming algorithm based on angle-domain transformation
CN109039410B (en) Beam forming method of heterogeneous cloud wireless access network and communication network
Bhardwaj et al. Deep learning-based MIMO and NOMA energy conservation and sum data rate management system
CN104954054A (en) Method for eliminating cell-edge user interference of multi-cell system under C-RAN architecture
CN106788641A (en) A kind of pre-coding transmission method of information and energy joint transmission system
CN113395095B (en) Large-scale MIMO uplink transmission method assisted by dynamic super-surface antenna
CN112600593A (en) NOMA-based beam selection method
CN102790974B (en) Base station optimal position selection method in CoMP (coordinated multi-point) system network planning
Zhao et al. Joint hybrid beamforming and offloading for mmWave mobile edge computing systems
CN111464956A (en) C-RAN joint beam and power splitting design method based on forward link multicast transmission
CN115379478B (en) Robust energy consumption optimization method based on RIS auxiliary digital energy simultaneous transmission network
CN104821840B (en) A kind of anti-interference method of extensive multiple-input and multiple-output downlink system
CN113395094B (en) Dynamic super-surface antenna assisted large-scale MIMO uplink statistical transmission method
CN115065392A (en) Beam forming design method for realizing MISO downlink sum rate maximization under dirty paper coding condition
CN114978282A (en) Satellite power distribution method and satellite power multiple-input multiple-output distribution system
CN109194375B (en) FD-MIMO multi-cell downlink interference coordination method
CN107979397B (en) Antenna port selection method of distributed antenna system
CN109982336B (en) Method for constructing virtual macro base station by ultrahigh frequency small base station in 5G network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant