CN108964733B - A beamforming method and a heterogeneous cloud wireless access network based on the same - Google Patents

Abstract

The invention discloses a beamforming method and a heterogeneous cloud wireless access network based on the method. The method includes the following steps: calculating the data transmission rate of cellular users, calculating the data transmission rate of RRH users, calculating the heterogeneous cloud wireless The total data transmission rate and total power consumption of the access network, the joint optimization problem of determining the beamforming vectors of MBS and RRH, and the joint optimization problem of solving the beamforming vectors of MBS and RRH; the heterogeneous cloud wireless access network includes a baseband processing unit pool and multiple macro cellular networks, each macro cellular network includes a macro base station MBS, multiple wireless remote radio frequency modules RRH, multiple cellular users and multiple RRH users; this method transforms the original optimization problem into an easy-to-handle second-order Therefore, 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.

Description

A beamforming method and a heterogeneous cloud wireless access network based on the same

technical field

The invention belongs to the technical field of wireless communication, in particular to a beamforming method and a heterogeneous cloud wireless access network based on the method.

Background technique

With the sharp increase in the number of smart mobile devices and the emergence of various wireless applications with mobile social networking and Internet of Things (IoT) technologies, the global mobile data traffic is expected to reach 587EB by 2021. At the same time, the total number of mobile network access devices in the world will reach 100 billion by the end of 2020, of which the number of mobile terminals will exceed 10 billion. The rapid development of wireless networks has led to a rapid 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 ICT industry, of which more than 60% are directly attributable to radio access networks [2]. Therefore, next-generation wireless networks face major challenges in increasing system capacity, ensuring user service quality, and reducing energy consumption. As a new type of network, heterogeneous cloud wireless access network provides a possible solution to the problems faced by existing wireless networks.

The heterogeneous cloud radio access network retains the macro base station (Macro BaseStation MBS) deployed in the traditional macro cellular network. Heterogeneous cloud wireless access networks use MBS to alleviate the capacity limitation of fronthaul links and achieve seamless coverage of macro cellular networks. However, in the heterogeneous cloud wireless access network, the remote radio head (RRH) and the MBS work in the underlay mode, and there is severe inter-layer interference between them, which reduces the overall performance of the network. To overcome this problem, multi-antenna technology can be used to improve spatial resource multiplexing and suppress inter-layer interference. In the research on multi-antenna heterogeneous cloud wireless access network, the literatures [1-5] all assume that there is only one macrocellular network and one MBS in the heterogeneous cloud wireless access network, and the researched HC-RAN wireless access network architecture Simpler. Although the literature [6] studies the situation of multiple macrocellular networks in the heterogeneous cloud wireless access network, the paper assumes that the beamforming vector of the MBSs is known, and only optimizes the beamforming vector of the RRHs without considering the MBSs. A joint optimization problem of beamforming vectors with RRHs. The research on the key technology of multi-antenna HC-RAN is still in the initial stage, and how to use the multi-antenna technology to solve the difficult problems in HC-RAN needs further research.

references

[1] Mugen Peng, Hongyu Xiang, Yuanyuan Chen, et.al.Inter-tierinterference suppression in heterogeneous cloud radio access networks.IEEEAccess,2015,3:2441-2455.

[2] Yuanyuan Cheng, Shi Yan, Jinhe Zhou, et.al.Average bit error rate andsum capacity in heterogeneous cloud radio access networks.IEEE VehicularTechnology Conference,6-9 September 2015,Boston USA,1-5.

[3] Mugen Peng, Yuling Yu, Hongyu Xiang, et.al.Energy-efficient resourceallocation optimization for multimedia heterogeneous cloud radio accessnetworks.IEEE Transactions on Multimedia,2016,18(5):879-892.

[4] Lifeng Wang, Kaikit Wong, Maged Elkashlan, et.al.Secrecy and energyefficiency in massive MIMO aided heterogeneous C-RAN: a new look atinterference.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 massiveMIMO 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 resourceallocation in heterogeneous Cloud-RANs. IEEE Internet of Things Journal, 2017, 4(3):749-759.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a heterogeneous cloud wireless access network and a beamforming method applied to the network in view of the problems existing in the prior art. The energy efficiency of the network is the optimization goal, and the beamforming vectors of MBS and RRH are jointly optimized.

For achieving the above object, the technical scheme adopted in the present invention is:

A beamforming method, comprising the following steps:

Step A, calculates the data transmission rate of the cellular user;

Step B, calculating the data transmission rate of the RRH user;

Step C, calculating the total data transmission rate and total power consumption of the heterogeneous cloud wireless access network;

Step D, determine the joint optimization problem of the beamforming vectors of MBS and RRH;

Step E, solve the joint optimization problem of the beamforming vectors of MBS and RRH.

Specifically, in step A, the data transmission rate of the cellular user k is calculated by the following formula:

为宏蜂窝网络m中MBS对蜂窝用户k的波束成形向量，

为宏蜂窝网络m中MBS对蜂窝用户k的波束成形向量，其中m≠m,k≠k；

为宏蜂窝网络m中MBS与蜂窝用户k之间的信道向量，T₁为每个MBS配有的天线数量；

为宏蜂窝网络m中RRH n与蜂窝用户k之间的干扰信道向量，

为宏蜂窝网络m中RRH n对RRH用户j的波束成形向量，其中n＝{1，2，…，N_m}；T₂为每个RRH配有的天线数量；

为宏蜂窝网络m中的MBS与宏蜂窝网络m中的蜂窝用户k之间的干扰信道向量，C表示复数域，(·)^T表示转置。Among them, M={1,2,...,M} represents the set composed of all macro cellular networks, N _m ={1,2,...,N _m } represents the set composed of all RRHs in the macro cellular network m, J _m = {1,2,...,J _m } denotes the set composed of all RRH users in the macro cellular network m, K _m ={1,2,...,K _m } denotes the set composed of all cellular users in the macro cellular network m,

is the beamforming vector of the MBS in the macrocellular network m to the cellular user k,

is the beamforming vector of the MBS in the macrocellular network m to the cellular user k , where m ≠m, k ≠k;

is the channel vector between the MBS and the cellular user k in the macro cellular network m, and T ₁ is the number of antennas each MBS is equipped with;

is the interference channel vector between RRH n and cellular user k in the macrocellular network m,

is the beamforming vector of RRH n to RRH user j in the macrocellular network m, where n={1, 2, ..., N _m }; T ₂ is the number of antennas equipped with each RRH;

is the interference channel vector between the MBS in the macro cellular network m and the cellular user k in the macro cellular network m, C represents the complex domain, (·) ^T represents the transpose.

Specifically, in step B, the data transmission rate of the RRH user j is calculated by the following formula:

为宏蜂窝网络m中的RRH n与RRH用户j之间的信道向量，n＝{1，2，…，N_m}；

宏蜂窝网络m中的MBS与宏蜂窝网络m中的RRH用户j之间的干扰信道向量。in,

is the channel vector between RRH n and RRH user j in the macro cellular network m, n={1, 2,...,N _m };

Interference channel vector between MBS in macrocellular network m and RRH user j in macrocellular network m.

Specifically, in step C, the total data transmission rate of RRH users and cellular users in the heterogeneous cloud radio access network is:

The total power consumption of RRH and MBS in the heterogeneous cloud radio access network is:

表示宏蜂窝网络m中RRH用户j的数据传输速率；

表示宏蜂窝网络m中蜂窝用户k的数据传输速率；

表示向量

的2-范数的平方，

表示向量

的2-范数的平方。in,

represents the data transmission rate of RRH user j in the macrocellular network m;

represents the data transmission rate of cellular user k in macrocellular network m;

representation vector

The square of the 2-norm of ,

representation vector

The square of the 2-norm of .

Specifically, in step D, the method for determining the beamforming vector optimization problem of MBS and RRH is to express the optimization problem as:

和

分别为宏蜂窝网络m中RRH和MBS的最大发射功率门限值，s.t.表示约束条件的意思。in,

and

are the maximum transmit power thresholds of the RRH and MBS in the macro cellular network m respectively, and st represents the meaning of the constraint condition.

Specifically, in step E, the method for solving the joint optimization problem of the beamforming vectors of MBS and RRH includes the following steps:

α,β,ζ，将原优化问题(5a)、(5b)、(5c)转化(近似)为如下优化问题：Step E1, by introducing auxiliary variables

α, β, ζ, the original optimization problems (5a), (5b), (5c) are transformed (approximately) into the following optimization problems:

s.t.α≥ζβ (6b)

In step E2, the non-convex constraints (6b), (6e) and (6g) in the above step E1 are transformed (approximately) into the following convex constraints:

为正常数；Among them, π,

is a normal number;

In step E3, the constraint condition (6c) in the above-mentioned step E1 is transformed (approximately) into the following three inequalities:

为引入的新变量；in,

is the new variable introduced;

In step E4, the inequality (10) in the above-mentioned step E3 is transformed (approximately) into the following second-order cone constraint form:

Among them, θ _l is a new variable introduced, l={0, 1, 2, ..., L+3}, L is a constant, the larger the value of L, the higher the approximation accuracy;

Transform (approximate) the inequality (11) in the above step E3 into the following second-order cone constraint form:

为引入的新变量，d＝{0，1，2，…，D+3}，D为正常数，D的值越大，近似的精度越高；in,

is a new variable introduced, d={0, 1, 2, ..., D+3}, D is a constant, the larger the value of D, the higher the approximation accuracy;

Step E5, the original optimization problem is solved based on the beamforming method with the second-order cone constraint in the above-mentioned step E4, that is, the original optimization problems (5a), (5b), (5c) are transformed into second-order cone programming problems:

s.t.(6d),(6f),(6h),(6i),(7),(8),(9),(12),(13),(14);(15b)

Further, in step E5, the second-order cone constraint-based beamforming method to solve the original optimization problem specifically includes the following steps:

π(0)，

Step S1, initialization iteration times t=0,

π(0),

π(t)求解优化问题，得到解

ζ(t)，β(t)，

Step S2, according to

π(t) solve the optimization problem and get the solution

ζ(t), β(t),

和

Step S3, update

and

Step S4, let t=t+1;

和

收敛，即得到最优解

和

Step S5, repeat steps S2 to S4 until the variable π,

and

Convergence, that is, to get the optimal solution

and

π(0)为t＝0时，

和π的初始值；

π(t)，

ζ(t)，β(t)，

分别为第t次迭代时，

π、

ζ、β、

和

的取值。Among them, t is the number of iterations of the algorithm, and t=0 represents the 0th iteration, that is, the initialization stage;

When π(0) is t=0,

and the initial value of π;

π(t),

ζ(t), β(t),

are the t-th iteration, respectively,

pi,

ζ, β,

and

value of .

A heterogeneous cloud wireless access network based on the above beamforming method includes a baseband processing unit pool and multiple macro cellular networks; each macro cellular network includes a macro base station (Macro Base Station, MBS), a plurality of macro cellular networks. Wireless remote radio frequency module (Remote Radio Head, RRH), multiple cellular users and multiple RRH users; the baseband processing unit pool is connected with MBS and RRH through optical fiber communication; the macro base station is used for wide-area coverage wireless The RRH is used for coverage of wireless signals in a hotspot area or a blind spot area; a plurality of the MBSs and RRHs respectively provide communication services for the cellular users and the RRH users.

Specifically, the hotspot area is a busy area of business formed due to uneven distribution of spatial business loads;

The blind spot area is a shadow area caused by obstacles encountered by the radio waves during propagation.

Compared with the prior art, the beneficial effects of the present invention are: the present invention proposes a heterogeneous cloud wireless access network for the problem that the prior art heterogeneous cloud wireless access network is not suitable for the coexistence of multiple macro cellular networks. A network, and a beamforming method based on second-order cone planning applied to the network, in the case of considering intra-cellular interference and inter-cellular interference, the beamforming vectors of MBS and RRH are jointly optimized to improve the energy efficiency of the network, Suppress interference in heterogeneous cloud wireless access networks and reduce total network power consumption.

Description of drawings

1 is a flowchart of a beamforming 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 ways

The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Example 1

As shown in FIG. 1 , this embodiment provides a beamforming method, including the following steps:

Step A, calculates the data transmission rate of the cellular user;

Step B, calculating the data transmission rate of the RRH user;

Step C, calculating the total data transmission rate and total power consumption of the heterogeneous cloud wireless access network;

Step D, determine the joint optimization problem of the beamforming vectors of MBS and RRH;

Step E, solve the joint optimization problem of the beamforming vectors of MBS and RRH.

Specifically, let _M ={1, 2, . N _m } denotes the set composed of all RRHs, K _m ={1,2,...,K _m } denotes the set composed of all cellular users, J _m ={1,2,...,J _m } denotes the set composed of all RRH users set, where K _m and J _m are the total number of cellular users and RRH users, respectively.

为宏蜂窝网络m中MBS对蜂窝用户k的波束成形向量，

为宏蜂窝网络m中MBS与蜂窝用户k之间的信道向量，

为宏蜂窝网络m中RRH n对RRH用户j的波束成形向量，

为宏蜂窝网络m中RRH n与蜂窝用户k之间的干扰信道向量，

和

分别为蜂窝用户k和RRH用户j的发射信号，

为RRH n和RRH用户j之间的信道向量；

为MBS与RRH用户j之间的干扰信道向量，C表示复数域。Specifically, it is assumed that each MBS and RRH are equipped with T ₁ and T ₂ antennas respectively, and the cellular users and RRH users are respectively equipped with one antenna; the beamforming vector of k,

is the beamforming vector of the MBS in the macrocellular network m to the cellular user k,

is the channel vector between the MBS and cellular user k in the macrocellular network m,

is the beamforming vector of RRH n to RRH user j in the macrocellular network m,

is the interference channel vector between RRH n and cellular user k in the macrocellular network m,

and

are the transmitted signals of cellular user k and RRH user j, respectively,

is the channel vector between RRH n and RRH user j;

is the interference channel vector between MBS and RRH user j, and C represents the complex domain.

其中

为宏蜂窝网络m中的MBS与宏蜂窝网络m中的蜂窝用户k之间的干扰信道向量，

为接收到的噪声，(·)^T表示转置，CN(0,1)表示服从均值向量为0，协方差为1的复高斯分布。那么，蜂窝用户(k∈K_m)的数据传输速率为：Specifically, in the mth macrocellular network, the signal received by cellular user k is:

in

is the interference channel vector between the MBS in the macrocellular network m and the cellular user k in the macrocellular network m,

is the received noise, ( ) ^T represents the transpose, CN(0,1) represents a complex Gaussian distribution with a mean vector of 0 and a covariance of 1. Then, the data transmission rate of the cellular user (k∈K _m ) is:

其中,

宏蜂窝网络m中的MBS与宏蜂窝网络m中的RRH用户j之间的干扰信道向量,

为宏蜂窝网络m中的RRH n与RRH用户j之间的信道向量，其中，n＝{1，2，…，N_m}；

为接收到的噪声。那么，RRH用户j(j∈J_m)的数据传输速率为：Specifically, in the mth macrocellular network, the signal received by RRH user j is:

in,

the interference channel vector between the MBS in the macrocellular network m and the RRH user j in the macrocellular network m,

is the channel vector between RRH n and RRH user j in the macrocellular network m, where n={1, 2,...,N _m };

is the received noise. Then, the data transmission rate of RRH user j (j∈J _m ) is:

为宏蜂窝网络m中的RRH n与RRH用户j之间的信道向量，

宏蜂窝网络m中的MBS与宏蜂窝网络m中的RRH用户j之间的干扰信道向量。in,

is the channel vector between RRH n and RRH user j in the macrocellular network m,

Interference channel vector between MBS in macrocellular network m and RRH user j in macrocellular network m.

Specifically, the total data transmission rate of RRH users and cellular users in the heterogeneous cloud radio access network is:

The total power consumption of RRHs and MBSs in heterogeneous cloud radio access network is:

表示宏蜂窝网络m中RRH用户j的数据传输速率；

表示宏蜂窝网络m中蜂窝用户k的数据传输速率；||·||₂表示向量的2-范数。in,

represents the data transmission rate of RRH user j in the macrocellular network m;

represents the data transmission rate of cellular user k in the macrocellular network m; ||·|| ₂ represents the 2-norm of the vector.

Specifically, the joint beamforming problem of MBSs and RRHs in heterogeneous cloud radio access networks can be expressed as:

和

分别为宏蜂窝网络m中RRHs和MBS的最大发射功率门限值，s.t.表示约束条件的意思。in

and

are the maximum transmit power thresholds of the RRHs and MBS in the macro cellular network m, respectively, and st represents the meaning of the constraints.

α,β,ζ，将原优化问题转化(近似)为如下优化问题：The original optimization problems (5a), (5b), and (5c) are non-convex and fractional programming problems, which are usually difficult to solve. In order to facilitate the solution, this embodiment introduces auxiliary variables.

α, β, ζ, the original optimization problem is transformed (approximately) into the following optimization problem:

s.t.α≥ζβ (6b)

其中f(x,y)≥g(x,y)。显然f(x,y)是凸函数，当

时，有f(x,y)＝g(x,y)；基于上述分析，约束条件(6b)、(6e)、(6g)可以转化(近似)为：Further, in the above optimization problems (6a) to (6i), the objective function, the constraints (6c), (6d), (6f), (6h), (6i) are all convex, and the constraints (6b) , (6e), (6g) are non-convex, need to deal with these three non-convex constraints, define the function g(x,y)=xy and

where f(x,y)≥g(x,y). Obviously f(x,y) is a convex function, when

When , there is f(x,y)=g(x,y); based on the above analysis, the constraints (6b), (6e), (6g) can be transformed (approximately) into:

为正常数；Among them, π,

is a normal number;

In addition, except for constraint (6c), other constraints are linear or second-order conical form, in order to express constraint (6c) into a second-order conical form, the constraint (6c) is transformed (approximately) are the following three inequalities:

为引入的新变量；in,

is the new variable introduced;

Further, the above formula (10) can be transformed (approximately) into the following second-order cone constraint form:

Where θ _l is the new variable introduced, l={0, 1, 2, ..., L+3}, L is a normal number, the larger the value of L, the higher the approximation accuracy;

Further, the above formula (11) can also be transformed (approximately) into the following second-order cone constraint form:

为引入的新变量，d＝{0，1，2，…，D+3}，D为正常数，D的值越大，近似的精度越高。in,

For the new variable introduced, d={0, 1, 2, ..., D+3}, D is a positive number, the larger the value of D, the higher the approximation accuracy.

In this embodiment, the original optimization problems (5a), (5b), and (5c) are transformed into transformed second-order cone programming problems, namely:

s.t.(6d),(6f),(6h),(6i),(7),(8),(9),(12),(13),(14)(15b)

Specifically, the specific steps for solving the original optimization problem by the quadratic programming-based beamforming method in this embodiment are as follows:

π(0)，

Step S1, initialization iteration times t=0,

π(0),

π(t)求解优化问题，得到解

ζ(t)，β(t)，

Step S2, according to

π(t) solve the optimization problem and get the solution

ζ(t), β(t),

和

Step S3, update

and

Step S4, let t=t+1;

和

收敛，即得到最优解

和

Step S5, repeat steps S2 to S4 until the variable π,

and

Convergence, that is, to get the optimal solution

and

为t＝0时，

和π的初始值；

ζ(t)，β(t)，

分别为第t次迭代时，

ζ、β、

和

的取值。Among them, t is the number of iterations of the algorithm, and t=0 represents the 0th iteration, that is, the initialization stage;

When t=0,

and the initial value of π;

ζ(t), β(t),

are the t-th iteration, respectively,

ζ, β,

and

value of .

The beamforming optimization method based on the second-order cone planning in this embodiment improves the energy efficiency of the heterogeneous cloud wireless access network and suppresses the interference existing in the network by transforming the original optimization problem into a tractable second-order cone planning problem. , Reduce the total power consumption of the network.

Example 2

As shown in FIG. 2, this embodiment provides a heterogeneous cloud wireless access network based on the above beamforming method, the network is composed of a baseband processing unit pool and a plurality of macrocellular networks; each of the macrocellular networks Each includes a macro base station (Macro Base Station, MBS), multiple remote radio frequency modules (Remote Radio Head, RRH), multiple cellular users and multiple RRH users; the baseband processing unit pool and the MBS, RRH The macro base station is used for wide-area coverage of wireless signals, and the RRH is used for coverage of wireless signals in hotspot areas or blind spot areas; MBSs and RRHs provide communication services for the cellular users and RRH users, respectively; The above-mentioned MBSs and RRHs respectively represent multiple MBSs and multiple RRHs.

Specifically, the hotspot area is a busy area of business formed due to uneven distribution of spatial business loads;

The blind spot area is a shadow area caused by obstacles encountered by the radio waves during propagation.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, and substitutions can be made in these embodiments without departing from the principle and spirit of the invention and modifications, the scope of the present invention is defined by the appended claims and their equivalents.

Claims (2)

1. A beamforming method, comprising the following steps: Step A, calculates the data transmission rate of the cellular user; The data transmission rate of the cellular user is calculated by the following formula:

Among them, M={1,2,...,M} represents the set composed of all M macrocellular networks, and N _m ={1,2,...,N _m } represents the set composed of all N _m RRHs in the macrocellular network m Set, J _m ={1,2,...,J _m } denotes the set composed of all J _m RRH users in the macro cellular network m, K _m ={1,2,...,K _m } denotes the macro cellular network m The set of all K _m cellular users,

is the beamforming vector of the MBS in the macrocellular network m to the cellular user k,

is the beamforming vector of the MBS in the macrocellular network m to the cellular user k , where m ≠m, k ≠k;

is the channel vector between the MBS and the cellular user k in the macro cellular network m, and T ₁ is the number of antennas each MBS is equipped with;

is the interference channel vector between RRH n and cellular user k in the macrocellular network m,

is the beamforming vector of RRH n to RRH user j in the macrocellular network m, where n={1, 2, ..., N _m }; T ₂ is the number of antennas equipped with each RRH;

is the interference channel vector between the MBS in the macro cellular network m and the cellular user k in the macro cellular network m, C represents the complex domain, ( ) ^T represents the 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:

in,

is the channel vector between RRH n and RRH user j in the macro cellular network m, n={1, 2,...,N _m };

the interference channel vector between the MBS in the macrocellular network m and the RRH user j in the macrocellular network m; Step C, calculating the total data transmission rate and total power consumption of the heterogeneous cloud wireless access network; The total data transmission rate of RRH users and cellular users in the heterogeneous cloud radio access network is:

The total power consumption of RRH and MBS in the heterogeneous cloud radio access network is:

in,

represents the data transmission rate of RRH user j in the macrocellular network m;

represents the data transmission rate of cellular user k in macrocellular network m;

representation vector

The square of the 2-norm of ,

representation vector

The square of the 2-norm of ; Step D, determine the joint optimization problem of the beamforming vectors of MBS and RRH; The optimization problem is expressed as:

in,

and

are the maximum transmit power thresholds of the RRH and MBS in the macro cellular network m, respectively, and st represents the meaning of the constraints; Step E, solving the joint optimization problem of the beamforming vectors of MBS and RRH, including the following steps: Step E1, by introducing auxiliary variables

α, β, ζ, transform the original optimization problem Equation (5a), Equation (5b), Equation (5c) into the following optimization problem:

s.t.α≥ζβ (6b)

Step E2: Convert the non-convex constraint equations (6b), (6e), and (6g) in the above step E1 into the following convex constraints:

Among them, π,

is a normal number; In step E3, the constraint expression (6c) in the above step E1 is converted into the following three inequalities:

in,

is the new variable introduced; In step E4, the inequality (10) in the above step E3 is transformed into the following second-order cone constraint form:

Among them, θ _l is a new variable introduced, l={0, 1, 2, ..., L+3}, L is a normal number; Transform the inequality (11) in the above step E3 into the following second-order cone constraint form:

in,

is the new variable introduced, d={0, 1, 2, ..., D+3}, D is a normal number; In step E5, the original optimization problem is solved based on the beamforming method with the second-order cone constraint in the above-mentioned step E4, that is, the original optimization problem equations (5a), (5b), and (5c) are transformed into a second-order cone programming problem:

s.t.(6d),(6f),(6h),(6i),(7),(8),(9),(12),(13),(14)(15b). 2. A beamforming method according to claim 1, characterized in that, in step E5, the second-order cone constraint-based beamforming method to solve the original optimization problem specifically comprises the following steps: Step S1, initialization iteration times t=0,

π(0),

Step S2, according to

π(t) solves the transformed optimization problems Eqs. (15a) and (15b), and obtains the solution

ζ(t), β(t),

Step S3, update

and

Step S4, let t=t+1; Step S5, repeat steps S2 to S4 until the variable π,

and

Convergence, that is, to get the optimal solution

and

Among them, t is the number of iterations of the algorithm, and t=0 represents the 0th iteration, that is, the initialization stage;

When π(0) is t=0,

and the initial value of π;

π(t),

ζ(t), β(t),

are the t-th iteration, respectively,

pi,

ζ, β,

and

value of .

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