CN108964733B - Beam forming method and heterogeneous cloud wireless access network based on same - Google Patents

Beam forming method and heterogeneous cloud wireless access network based on same Download PDF

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CN108964733B
CN108964733B CN201810635634.2A CN201810635634A CN108964733B CN 108964733 B CN108964733 B CN 108964733B CN 201810635634 A CN201810635634 A CN 201810635634A CN 108964733 B CN108964733 B CN 108964733B
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CN108964733A (en
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左加阔
杨龙祥
鲍楠
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Nanjing University of Posts and Telecommunications
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    • 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
    • H04B7/08—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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses a beam forming method and a heterogeneous cloud wireless access network based on the same, wherein the method comprises the following steps: calculating the data transmission rate of a cellular user, calculating the data transmission rate of an RRH user, calculating the total data transmission rate and the total power consumption of a heterogeneous cloud wireless access network, determining the beamforming vector joint optimization problem of the MBS and the RRH, and solving the beamforming vector joint optimization problem of the MBS and the RRH; the heterogeneous cloud wireless access network comprises a baseband processing unit pool and a plurality of macro cellular networks, wherein each macro cellular network comprises a macro base station MBS, a plurality of wireless remote radio frequency modules RRH, a plurality of cellular users and a plurality of RRH users; the method converts the original optimization problem into a second-order cone planning problem which is easy to process, thereby improving the energy efficiency of the heterogeneous cloud wireless access network, inhibiting the interference existing in the network and reducing the total power consumption of the network.

Description

Beam forming method and heterogeneous cloud wireless access network based on same
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a beam forming method and a heterogeneous cloud wireless access network based on the beam forming method.
Background
With the dramatic increase in the number of intelligent mobile devices, and the emergence of various wireless applications along with mobile social networking and Internet of Things (IoT) technologies, it is expected that global mobile data traffic will reach 587EB by 2021. Meanwhile, the total number of global mobile network access devices reaches 1000 billion in 2020, wherein the number of mobile terminals exceeds 100 billion. The rapid development of wireless networks has led to a dramatic increase in energy consumption and greenhouse gas emissions. Statistics show that 2% -10% of global energy consumption and 2% of global CO2 emissions are generated by the information and communication technology industry, with over 60% directly due to the radio access network [2 ]. Therefore, the next generation wireless network faces significant challenges in improving system capacity, ensuring user service quality, and reducing energy consumption. As a novel network, the heterogeneous cloud wireless access network provides a possible solution for solving the problems faced by the existing wireless network.
Heterogeneous cloud wireless access networks retain Macro Base stations (Macro Base Station MBS) deployed in traditional Macro cellular networks. The heterogeneous cloud wireless access network utilizes the MBS to relieve the capacity limit of a Fronthaul Link (Fronthaul Link) and realize the seamless coverage of the macro cellular network. However, in the heterogeneous cloud Radio access network, when a Remote Radio Head (RRH) and an MBS operate in an underlay mode, there is severe inter-layer interference between the two, and the interference reduces the overall performance of the network. To overcome this problem, multiple antenna techniques may be employed to improve spatial resource multiplexing and suppress inter-layer interference. In the research on the multi-antenna heterogeneous cloud radio access network, documents [1-5] assume that only one macro cellular network and one MBS exist in the heterogeneous cloud radio access network, and the structure of the researched HC-RAN radio access network is relatively simple. Document [6] studies the case where multiple macro cellular networks exist in the heterogeneous cloud radio access network, but it is assumed herein that the beamforming vectors of the MBSs are known, and only the beamforming vectors of the RRHs are optimized, and the joint optimization problem of the beamforming vectors of the MBSs and the RRHs is not considered. Research aiming at the key technology of the multi-antenna HC-RAN is still in the initial stage, and how to solve the difficult problem in the HC-RAN by using the multi-antenna technology needs to be further researched.
Reference to the literature
[1]Mugen Peng,Hongyu Xiang,Yuanyuan Chen,et.al.Inter-tier interference suppression in heterogeneous cloud radio access networks.IEEE Access,2015,3:2441-2455.
[2]Yuanyuan Cheng,Shi Yan,Jinhe Zhou,et.al.Average bit error rate and sum capacity in heterogeneous cloud radio access networks.IEEE Vehicular Technology Conference,6-9 September 2015,Boston USA,1-5.
[3]Mugen Peng,Yuling Yu,Hongyu Xiang,et.al.Energy-efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks.IEEE Transactions on Multimedia,2016,18(5):879-892.
[4]Lifeng Wang,Kaikit Wong,Maged Elkashlan,et.al.Secrecy and energy efficiency in massive MIMO aided heterogeneous C-RAN:a new look at interference.IEEE Journal of Selected Topics in Signal Processing,2016,10(8):1375-1389.
[5]Na Chen,Bo Rong,Xiaran Zhang,et.al.Scalable and flexible massive MIMO precoding for 5G H-CRAN.IEEE Wireless Communications,2017,24(1):46-52.
[6]Kaiwei Wang,Wuyang Zhou,and Shiwen Mao.On joint BBU/RRH resource allocation in heterogeneous Cloud-RANs.IEEE Internet of Things Journal,2017,4(3):749-759.
Disclosure of Invention
The invention aims to provide a heterogeneous cloud wireless access network and a beam forming method applied to the network, aiming at the problems in the prior art, and under the condition that the intra-cell interference, the inter-cell interference and the transmission power constraint are considered, the energy efficiency of the network is taken as an optimization target, and the beam forming vectors of MBS and RRH are jointly optimized.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method of beamforming comprising the steps of:
step A, calculating the data transmission rate of a cellular user;
step B, calculating the data transmission rate of the RRH user;
step C, calculating the total data transmission rate and the total power consumption of the heterogeneous cloud wireless access network;
step D, determining a beam forming vector joint optimization problem of the MBS and the RRH;
and E, solving the beam forming vector joint optimization problem of the MBS and the RRH.
Specifically, in step a, the data transmission rate of the cellular user k is calculated by the following formula:
Figure GDA0003342288290000021
where M ═ {1,2, …, M } denotes a set of all macrocellular network constituents, Nm＝{1,2,…,NmDenotes the set of all RRHs in the macro cellular network m, Jm＝{1,2,…,JmDenotes the set of all RRH users in the macro cellular network m, Km＝{1,2,…,KmDenotes in the macro cellular network mThe set of all cellular users is composed of,
Figure GDA0003342288290000031
for the beamforming vectors for MBS to cellular user k in macro cellular network m,
Figure GDA0003342288290000032
for macro cellular networksmMiddle MBS to cellular userkOf beam forming vectors of, whereinm≠m,k≠k；
Figure GDA0003342288290000033
For the channel vector, T, between MBS and cell user k in the macro cell network m1The number of antennas allocated for each MBS;
Figure GDA0003342288290000034
is the interference channel vector between RRH n and cellular user k in the macro cellular network m,
Figure GDA0003342288290000035
Figure GDA0003342288290000036
beamforming vector for RRH N to RRH user j in macro cellular network m, where N ═ 1,2, …, Nm}；T2The number of antennas allocated to each RRH;
Figure GDA0003342288290000037
for macro cellular networksmC represents a complex field, (-) and an interference channel vector between MBS in the macro cellular network m and cellular user k in the macro cellular network mTIndicating transposition.
Specifically, in step B, the data transmission rate of the RRH user j is calculated by the following formula:
Figure GDA0003342288290000038
wherein the content of the first and second substances,
Figure GDA0003342288290000039
for the channel vector between RRH N and RRH user j in macro cellular network m, N ═ {1,2, …, Nm}；
Figure GDA00033422882900000310
Macro cellular networkmThe interfering channel vector between the MBS in (MBS) and RRH user j in macro cellular network m.
Specifically, in step C, the total data transmission rate of the RRH user and the cellular user in the heterogeneous cloud radio access network is:
Figure GDA00033422882900000311
the total power consumption of the RRH and the MBS in the heterogeneous cloud wireless access network is as follows:
Figure GDA00033422882900000312
wherein the content of the first and second substances,
Figure GDA00033422882900000313
represents the data transmission rate of the RRH user j in the macro cellular network m;
Figure GDA00033422882900000314
represents the data transmission rate of cellular user k in the macro cellular network m;
Figure GDA00033422882900000315
representing a vector
Figure GDA00033422882900000316
Is the square of the 2-norm of (c),
Figure GDA00033422882900000317
representing a vector
Figure GDA00033422882900000318
2-norm of.
Specifically, in step D, the method for determining the beamforming vector optimization problem of the MBS and the RRH is to express the optimization problem as:
Figure GDA0003342288290000041
Figure GDA0003342288290000042
Figure GDA0003342288290000043
wherein the content of the first and second substances,
Figure GDA0003342288290000044
and
Figure GDA0003342288290000045
the maximum transmission power threshold values of the RRH and the MBS in the macro cellular network m are respectively, and s.t. represents the meaning of the constraint condition.
Specifically, in step E, the method for solving the beamforming vector joint optimization problem of MBS and RRH includes the following steps:
step E1, by introducing auxiliary variables
Figure GDA0003342288290000046
α, β, ζ, the original optimization problems (5a), (5b), and (5c) are transformed (approximated) into the following optimization problems:
Figure GDA0003342288290000047
s.t.α≥ζβ (6b)
Figure GDA0003342288290000048
Figure GDA0003342288290000049
Figure GDA00033422882900000410
Figure GDA00033422882900000411
Figure GDA00033422882900000412
Figure GDA00033422882900000413
Figure GDA00033422882900000414
step E2, converting (approximating) the non-convex constraints (6b), (6E), and (6g) in step E1 into the following convex constraints:
Figure GDA0003342288290000051
Figure GDA0003342288290000052
Figure GDA0003342288290000053
wherein, pi,
Figure GDA0003342288290000054
Is a normal number;
step E3, transforming (approximating) the constraint (6c) in the step E1 into the following three inequalities:
Figure GDA0003342288290000055
Figure GDA0003342288290000056
Figure GDA0003342288290000057
wherein the content of the first and second substances,
Figure GDA0003342288290000058
is the new variable introduced;
step E4, transforming (approximating) the inequality (10) in the step E3 into the following second order cone constraint form:
Figure GDA0003342288290000059
wherein, thetalFor the new variables introduced, L ═ {0, 1,2, …, L +3}, L is a normal number, and the larger the value of L, the higher the accuracy of the approximation;
the inequality (11) in the above step E3 is converted (approximated) into the following second-order cone constraint form:
Figure GDA0003342288290000061
wherein the content of the first and second substances,
Figure GDA00033422882900000624
for the introduced new variable, D is {0, 1,2, …, D +3}, D is a normal number, and the larger the value of D, the higher the accuracy of the approximation;
step E5, solving the original optimization problem based on the beam forming method of the second-order cone constraint in step E4, that is, transforming the original optimization problems (5a), (5b), and (5c) into a second-order cone planning problem:
Figure GDA0003342288290000062
s.t.(6d),(6f),(6h),(6i),(7),(8),(9),(12),(13),(14)； (15b)
further, in step E5, the step of solving the original optimization problem by the second-order cone constraint-based beamforming method specifically includes the following steps:
in step S1, the number of initialization iterations t is 0,
Figure GDA0003342288290000063
π(0)，
Figure GDA0003342288290000064
step S2, based on
Figure GDA0003342288290000065
Solving the optimization problem to obtain a solution
Figure GDA0003342288290000066
Figure GDA0003342288290000067
ζ(t)，β(t)，
Figure GDA0003342288290000068
Step S3, update
Figure GDA0003342288290000069
And
Figure GDA00033422882900000610
Figure GDA00033422882900000611
step S4, let t be t + 1;
step S5, repeating steps S2 to S4 until the variable pi,
Figure GDA00033422882900000612
and
Figure GDA00033422882900000613
converging to obtain the optimal solution
Figure GDA00033422882900000614
And
Figure GDA00033422882900000615
wherein, t is the iteration number of the algorithm, and t is 0 to represent the 0 th iteration, i.e. the initialization stage;
Figure GDA00033422882900000616
when pi (0) is t ═ 0,
Figure GDA00033422882900000617
and an initial value of π;
Figure GDA00033422882900000618
π(t)，
Figure GDA00033422882900000619
Figure GDA00033422882900000620
ζ(t)，β(t)，
Figure GDA00033422882900000621
in each case at the t-th iteration,
Figure GDA00033422882900000622
π、
Figure GDA00033422882900000625
Figure GDA0003342288290000074
ζ、β、
Figure GDA0003342288290000072
and
Figure GDA0003342288290000073
the value of (a).
A heterogeneous cloud wireless access network based on the beam forming method comprises a baseband processing unit pool and a plurality of macro cellular networks; each Macro cellular network comprises a Macro Base Station (MBS), a plurality of Radio Remote Head (RRH), a plurality of cellular users and a plurality of RRH users; the base band processing unit pool is in communication connection with the MBS and the RRH through optical fibers; the macro base station is used for covering wireless signals in a wide area, and the RRH is used for covering wireless signals in a hot spot area or a blind spot area; the MBS and RRH respectively provide communication service for the cellular user and RRH user.
Specifically, the hot spot area is a heavy traffic area formed due to uneven distribution of space traffic load;
the blind spot region is a shadow region caused by an electric wave encountering an obstacle during propagation.
Compared with the prior art, the invention has the beneficial effects that: aiming at the problem that the heterogeneous cloud wireless access network in the prior art is not suitable for coexistence of a plurality of macro cellular networks, the invention provides the heterogeneous cloud wireless access network and a beam forming method based on second-order cone programming, which is applied to the heterogeneous cloud wireless access network.
Drawings
FIG. 1 is a flow chart of a beam forming method based on second order cone planning according to the present invention;
fig. 2 is a schematic structural diagram of a heterogeneous cloud wireless access network according to the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, the present embodiment provides a beamforming method, including the following steps:
step A, calculating the data transmission rate of a cellular user;
step B, calculating the data transmission rate of the RRH user;
step C, calculating the total data transmission rate and the total power consumption of the heterogeneous cloud wireless access network;
step D, determining a beam forming vector joint optimization problem of the MBS and the RRH;
and E, solving the beam forming vector joint optimization problem of the MBS and the RRH.
Specifically, let M ═ {1,2, …, M } denote a set of all MBS components (or a set of macro cellular networks), and in the mth macro cellular network, let N bem＝{1,2,…,NmDenotes the set of all RRHs, Km＝{1,2,…,KmDenotes the set of all cellular users, Jm＝{1,2,…,JmDenotes the set of all RRH users, where KmAnd JmRespectively, the total number of cellular users and RRH users.
In particular, it is assumed that each MBS and RRH is respectively provided with T1And T2The cellular user and the RRH user are respectively provided with one antenna; in the mth macro-cellular network, for the beamforming vectors for MBS to cellular user k in macro-cellular network m,
Figure GDA0003342288290000081
for macro cellular networksmThe beamforming vector for cellular user k by the middle MBS,
Figure GDA0003342288290000082
for the channel vector between MBS and cellular user k in the macro cellular network m,
Figure GDA0003342288290000083
for the beamforming vector for RRH n to RRH user j in the macro cellular network m,
Figure GDA0003342288290000084
is the interference channel vector between RRH n and cellular user k in the macro cellular network m,
Figure GDA0003342288290000085
and
Figure GDA0003342288290000086
the transmitted signals for cellular user k and RRH user j respectively,
Figure GDA0003342288290000087
is the channel vector between RRH n and RRH user j;
Figure GDA0003342288290000088
for the interfering channel vector between MBS and RRH user j, C represents the complex field.
Specifically, in the mth macro cellular network, the signal received by the cellular user k is:
Figure GDA0003342288290000089
wherein
Figure GDA00033422882900000810
For macro cellular networksmThe interference channel vector between MBS in the macro cellular network m and cellular user k in the macro cellular network m,
Figure GDA00033422882900000811
for received noise, (.)TDenotes transposition, CN (0,1) denotes a complex gaussian distribution with a mean vector of 0 and a covariance of 1. ThenCellular user (K ∈ K)m) The data transmission rate of (1) is:
Figure GDA00033422882900000812
specifically, in the mth macro cellular network, the signal received by the RRH user j is:
Figure GDA00033422882900000813
wherein the content of the first and second substances,
Figure GDA00033422882900000814
macro cellular networkmThe interfering channel vector between the MBS in (MBS) and RRH user j in macro cellular network m,
Figure GDA00033422882900000815
is a channel vector between RRH N and RRH user j in macro cellular network m, where N ═ 1,2, …, Nm}；
Figure GDA00033422882900000816
Is the received noise. Then RRH user J (J ∈ J)m) The data transmission rate of (1) is:
Figure GDA0003342288290000091
wherein the content of the first and second substances,
Figure GDA0003342288290000092
is the channel vector between RRH n and RRH user j in the macro cellular network m,
Figure GDA0003342288290000093
macro cellular networkmThe interfering channel vector between the MBS in (MBS) and RRH user j in macro cellular network m.
Specifically, the total data transmission rate of the RRH user and the cellular user in the heterogeneous cloud wireless access network is as follows:
Figure GDA0003342288290000094
the total power consumption of the RRHs and the MBSs in the heterogeneous cloud wireless access network is as follows:
Figure GDA0003342288290000095
wherein the content of the first and second substances,
Figure GDA0003342288290000096
represents the data transmission rate of the RRH user j in the macro cellular network m;
Figure GDA0003342288290000097
represents the data transmission rate of cellular user k in the macro cellular network m; i | · | purple wind2Representing the 2-norm of the vector.
Specifically, the joint beamforming problem of MBSs and RRHs in the heterogeneous cloud radio access network can be expressed as:
Figure GDA0003342288290000098
Figure GDA0003342288290000099
Figure GDA00033422882900000910
wherein
Figure GDA00033422882900000911
And
Figure GDA00033422882900000912
the maximum transmission power threshold values of RRHs and MBS in the macro cellular network m are respectively, and s.t. represents the meaning of the constraint condition.
The original optimization problems (5a), (5b) and (5c) are non-convex, fractional programming problems which are usually difficult to solve, and in order to solve the problems, the embodiment introduces auxiliary variables
Figure GDA00033422882900000913
α, β, ζ, transforming (approximating) the original optimization problem into the following optimization problem:
Figure GDA00033422882900000914
s.t.α≥ζβ (6b)
Figure GDA0003342288290000101
Figure GDA0003342288290000102
Figure GDA0003342288290000103
Figure GDA0003342288290000104
Figure GDA0003342288290000105
Figure GDA0003342288290000106
Figure GDA0003342288290000107
further, in the optimization problems (6a) to (6i), an objective function and a constraint condition(6c) All of (6d), (6f), (6h) and (6i) are convex, while the constraints (6b), (6e) and (6g) are non-convex, and these three non-convex constraints need to be processed, defining functions g (x, y) xy and
Figure GDA0003342288290000108
wherein f (x, y) ≧ g (x, y). It is apparent that f (x, y) is a convex function when
Figure GDA0003342288290000109
When f (x, y) is g (x, y); based on the above analysis, the constraints (6b), (6e), (6g) can be transformed (approximated) to:
Figure GDA00033422882900001010
Figure GDA00033422882900001011
Figure GDA00033422882900001012
wherein, pi,
Figure GDA00033422882900001013
Is a normal number;
in addition, other constraints are linear or quadratic cone-shaped except for the constraint (6c), and in order to express the constraint (6c) as quadratic cone-shaped, the constraint (6c) is converted (approximated) into the following three inequalities:
Figure GDA00033422882900001014
Figure GDA00033422882900001015
Figure GDA00033422882900001016
wherein the content of the first and second substances,
Figure GDA0003342288290000111
is the new variable introduced;
further, the above equation (10) can be converted (approximated) into the following second order cone constraint form:
Figure GDA0003342288290000112
wherein theta islFor the new variables introduced, L ═ {0, 1,2, …, L +3}, L is a normal number, and the larger the value of L, the higher the accuracy of the approximation;
further, the above equation (11) can also be converted (approximated) into the following second order cone constraint form:
Figure GDA0003342288290000113
wherein the content of the first and second substances,
Figure GDA0003342288290000115
for the new variables introduced, D ═ {0, 1,2, …, D +3}, D is a normal number, and the larger the value of D, the higher the accuracy of the approximation.
In this embodiment, the original optimization problems (5a), (5b), and (5c) are converted into a transformed second-order cone programming problem, that is:
Figure GDA0003342288290000114
s.t.(6d),(6f),(6h),(6i),(7),(8),(9),(12),(13),(14) (15b)
specifically, the specific steps of solving the original optimization problem by the beam forming method based on quadratic programming of the embodiment are as follows:
step S1, initializationThe number of iterations t is 0,
Figure GDA0003342288290000121
π(0)，
Figure GDA0003342288290000122
step S2, based on
Figure GDA0003342288290000123
Solving the optimization problem to obtain a solution
Figure GDA0003342288290000124
Figure GDA0003342288290000125
ζ(t)，β(t)，
Figure GDA0003342288290000126
Step S3, update
Figure GDA0003342288290000127
And
Figure GDA0003342288290000128
Figure GDA0003342288290000129
step S4, let t be t + 1;
step S5, repeating steps S2 to S4 until the variable pi,
Figure GDA00033422882900001210
and
Figure GDA00033422882900001211
converging to obtain the optimal solution
Figure GDA00033422882900001212
And
Figure GDA00033422882900001213
wherein, t is the iteration number of the algorithm, and t is 0 to represent the 0 th iteration, i.e. the initialization stage;
Figure GDA00033422882900001214
Figure GDA00033422882900001215
when t is equal to 0, the signal is transmitted,
Figure GDA00033422882900001216
and an initial value of π;
Figure GDA00033422882900001217
Figure GDA00033422882900001218
ζ(t)，β(t)，
Figure GDA00033422882900001219
in each case at the t-th iteration,
Figure GDA00033422882900001220
Figure GDA00033422882900001224
ζ、β、
Figure GDA00033422882900001222
and
Figure GDA00033422882900001223
the value of (a).
According to the beam forming optimization method based on the second-order cone programming, the original optimization problem is converted into the easily-processed second-order cone programming problem, so that the energy efficiency of the heterogeneous cloud wireless access network is improved, the interference existing in the network is suppressed, and the total power consumption of the network is reduced.
Example 2
As shown in fig. 2, the present embodiment provides a heterogeneous cloud radio access network based on the above beamforming method, where the network is composed of a baseband processing unit pool and a plurality of macro cellular networks; each Macro cellular network comprises a Macro Base Station (MBS), a plurality of Radio Remote Head (RRH), a plurality of cellular users and a plurality of RRH users; the base band processing unit pool is in communication connection with the MBS and the RRH through optical fibers; the macro base station is used for covering wireless signals in a wide area, and the RRH is used for covering wireless signals in a hot spot area or a blind spot area; the MBSs and the RRHs respectively provide communication services for the cellular subscribers and the RRH subscribers; the MBSs and the RRHs respectively represent a plurality of MBS and a plurality of RRHs.
Specifically, the hot spot area is a heavy traffic area formed due to uneven distribution of space traffic load;
the blind spot region is a shadow region caused by an electric wave encountering an obstacle during propagation.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (2)

1. A method of beamforming, comprising the steps of:
step A, calculating the data transmission rate of a cellular user;
the data transmission rate of the cellular user is calculated by the following formula:
Figure FDA0003342288280000011
where M ═ {1,2, …, M } denotes a set of all M macrocellular networks, Nm＝{1,2,…,NmDenotes all N in the macro cellular network mmSet of RRHs, Jm＝{1,2,…,JmDenotes all J's in the macro cellular network mmSet of RRH users, Km＝{1,2,…,KmDenotes all K in the macro cellular network mmA set of one or more cellular users,
Figure FDA0003342288280000012
for the beamforming vectors for MBS to cellular user k in macro cellular network m,
Figure FDA0003342288280000013
for macro cellular networksmMiddle MBS to cellular userkOf beam forming vectors of, whereinm≠m,k≠k；
Figure FDA0003342288280000014
For the channel vector, T, between MBS and cell user k in the macro cell network m1The number of antennas allocated for each MBS;
Figure FDA0003342288280000015
Figure FDA0003342288280000016
is the interference channel vector between RRH n and cellular user k in the macro cellular network m,
Figure FDA0003342288280000017
Figure FDA0003342288280000018
beamforming vector for RRH N to RRH user j in macro cellular network m, where N ═ 1,2, …, Nm}；T2The number of antennas allocated to each RRH;
Figure FDA0003342288280000019
for macro cellular networksmC represents a complex field, (-) and an interference channel vector between MBS in the macro cellular network m and cellular user k in the macro cellular network mTRepresenting a transpose;
step B, calculating the data transmission rate of the RRH user;
the data transmission rate of the RRH user j is calculated by the following formula:
Figure FDA00033422882800000110
wherein the content of the first and second substances,
Figure FDA00033422882800000111
Figure FDA00033422882800000112
for the channel vector between RRH N and RRH user j in macro cellular network m, N ═ {1,2, …, Nm}；
Figure FDA00033422882800000113
Macro cellular networkmInterference channel vectors between MBS in (MBS) and RRH user j in macro cellular network m;
step C, calculating the total data transmission rate and the total power consumption of the heterogeneous cloud wireless access network;
the total data transmission rate of the RRH user and the cellular user in the heterogeneous cloud wireless access network is as follows:
Figure FDA0003342288280000021
the total power consumption of the RRH and the MBS in the heterogeneous cloud wireless access network is as follows:
Figure FDA0003342288280000022
wherein the content of the first and second substances,
Figure FDA0003342288280000023
represents the data transmission rate of the RRH user j in the macro cellular network m;
Figure FDA0003342288280000024
represents the data transmission rate of cellular user k in the macro cellular network m;
Figure FDA0003342288280000025
representing a vector
Figure FDA0003342288280000026
Is the square of the 2-norm of (c),
Figure FDA0003342288280000027
representing a vector
Figure FDA0003342288280000028
2-norm of (d);
step D, determining a beam forming vector joint optimization problem of the MBS and the RRH;
expressing the optimization problem as:
Figure FDA0003342288280000029
Figure FDA00033422882800000210
Figure FDA00033422882800000211
wherein the content of the first and second substances,
Figure FDA00033422882800000212
and
Figure FDA00033422882800000213
maximum transmission power threshold values of RRH and MBS in the macro cellular network m are respectively, and s.t. represents the meaning of constraint conditions;
step E, solving the beamforming vector joint optimization problem of MBS and RRH, comprising the following steps:
step E1, by introducing auxiliary variables
Figure FDA00033422882800000214
α, β, ζ, the original optimization problem formula (5a), formula (5b), formula (5c) is converted into the following optimization problem:
Figure FDA00033422882800000215
s.t.α≥ζβ (6b)
Figure FDA00033422882800000216
Figure FDA00033422882800000217
Figure FDA00033422882800000218
Figure FDA0003342288280000031
Figure FDA0003342288280000032
Figure FDA0003342288280000033
Figure FDA0003342288280000034
step E2, converting the non-convex constraint conditions of formula (6b), formula (6E), and formula (6g) in step E1 into the following convex constraint conditions:
Figure FDA0003342288280000035
Figure FDA0003342288280000036
Figure FDA0003342288280000037
wherein, pi,
Figure FDA0003342288280000038
Is a normal number;
step E3, converting the constraint equation (6c) in the step E1 into the following three inequalities:
Figure FDA0003342288280000039
Figure FDA00033422882800000310
Figure FDA00033422882800000311
wherein the content of the first and second substances,
Figure FDA00033422882800000312
is the new variable introduced;
step E4, converting the inequality (10) in the step E3 into a second-order cone constraint form as follows:
Figure FDA0003342288280000041
wherein, thetalFor the new variables introduced, L ═ {0, 1,2, …, L +3}, L being a normal number;
converting the inequality (11) in the step E3 into a second-order cone constraint form as follows:
Figure FDA0003342288280000042
wherein the content of the first and second substances,
Figure FDA0003342288280000043
for the new variables introduced, D ═ {0, 1,2, …, D +3}, D being a normal number;
step E5, solving the original optimization problem based on the beam forming method of the second-order cone constraint in step E4, that is, transforming the original optimization problem formula (5a), formula (5b), and formula (5c) into a second-order cone planning problem:
Figure FDA0003342288280000044
s.t.(6d),(6f),(6h),(6i),(7),(8),(9),(12),(13),(14)(15b)。
2. the beamforming method according to claim 1, wherein in step E5, the step of solving the original optimization problem by the beamforming method based on the second-order cone constraint specifically includes the following steps:
in step S1, the number of initialization iterations t is 0,
Figure FDA0003342288280000045
π(0)，
Figure FDA0003342288280000046
step S2, based on
Figure FDA0003342288280000047
Pi (t) solving the transformed optimization problem equations (15a) and (15b) to obtain solutions
Figure FDA0003342288280000051
ζ(t)，β(t)，
Figure FDA0003342288280000052
Figure FDA0003342288280000053
Step S3, update
Figure FDA0003342288280000054
And
Figure FDA0003342288280000055
Figure FDA0003342288280000056
step S4, let t be t + 1;
step S5, repeating steps S2 to S4 until the variable pi,
Figure FDA0003342288280000057
and
Figure FDA0003342288280000058
converging to obtain the optimal solution
Figure FDA0003342288280000059
And
Figure FDA00033422882800000510
wherein, t is the iteration number of the algorithm, and t is 0 to represent the 0 th iteration, i.e. the initialization stage;
Figure FDA00033422882800000511
when pi (0) is t ═ 0,
Figure FDA00033422882800000512
and an initial value of π;
Figure FDA00033422882800000513
π(t)，
Figure FDA00033422882800000514
Figure FDA00033422882800000515
ζ(t)，β(t)，
Figure FDA00033422882800000516
in each case at the t-th iteration,
Figure FDA00033422882800000517
π、
Figure FDA00033422882800000522
Figure FDA00033422882800000523
ζ、β、
Figure FDA00033422882800000520
and
Figure FDA00033422882800000521
the value of (a).
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