CN116419245A - An Energy Efficiency Optimization Method for Multi-cell Communication System Based on Intelligent Reflector Assisted Rate Division Multiple Access - Google Patents

Abstract

A multi-cell communication system energy efficiency optimization method based on intelligent reflection surface assisted rate division multiple access combines a rate division multiple access RSMA with an intelligent reflection surface RIS. The RIS realizes intelligent regulation and control of the signal transmission link to enhance the signal transmission quality, thereby improving the signal quality of cell edge users; the RSMA effectively suppresses co-channel interference among users by flexibly controlling a rate segmentation strategy and beamforming, so that an optimization problem aiming at maximizing the energy efficiency of a system is constructed, and a beamforming vector, a rate segmentation multiple access matrix and a phase shift matrix on the RIS side of a base station are jointly optimized; in order to solve the non-convex optimization problem, an iterative solution beam forming, rate segmentation and phase shift optimization sub-problem is provided, wherein continuous convex optimization solution is adopted for the former, and semi-definite programming and penalty function method is adopted for the latter. Compared with the traditional space division multiple access and non-orthogonal frequency division multiple access, the energy efficiency improvement of 28.5% and 10.2% of the energy efficiency of the multi-cell communication system can be realized respectively.

Description

A method for optimizing energy efficiency of multi-cell communication system based on rate division multiple access assisted by intelligent reflector

Technical Field

The present invention belongs to the technical field of mobile communications, and in particular relates to an energy efficiency optimization method for a multi-cell communication system based on rate division multiple access assisted by an intelligent reflector.

Background Art

Compared with 4G/5G communications, 6G will provide greater capacity, lower latency, high reliability, high security and full space coverage. In order to meet the requirements of emerging applications such as augmented reality (AR) and virtual reality (VR) for higher speed and lower latency, the network capacity of 6G wireless communication system is expected to be 100 times that of 5G wireless communication system. At the same time, exploring and breaking through the constraints of many uncontrollable factors in the wireless environment and reconfiguring the wireless transmission environment are the new directions for the development of 6G. In the past decade, in order to meet the various emerging business needs of users, communication scholars and experts have proposed and thoroughly studied various wireless technologies, the most prominent of which include ultra-dense networks (UDN), massive multiple-input multiple-output (MIMO), wireless cellular networks and millimeter wave communications. The rise of these technologies has made a huge leap in spectrum efficiency, thereby meeting the ultra-high capacity requirements required for large-scale communications between a large number of wireless devices. However, the accompanying system energy consumption and hardware costs are key issues faced in practical applications. In order to realize the green and environmentally friendly sixth-generation wireless communication concept, the key is to find environmentally friendly and energy-saving technical support.

Recently, as a key technology in 6G, smart reflective surface (RIS) is considered to be a promising green and cost-effective potential solution. RIS improves the throughput and energy efficiency of wireless networks by reconfiguring the wireless propagation environment. Specifically, RIS is a two-dimensional array composed of a large number of passive reflective elements, each of which can independently adjust the phase shift of the incident signal in real time through the RIS controller.

By dividing users in the power domain, non-orthogonal frequency division multiple access (NOMA) can provide services to multiple users at the same time under the same frequency or time resources. Therefore, the access scheme based on NOMA can achieve higher spectrum efficiency than traditional orthogonal multiple access (OMA). However, when using NOMA, users must decode all interference when receiving messages, which greatly increases the computational complexity required for signal processing. In order to solve this problem, some scholars proposed the concept of rate division multiple access (RSMA) and applied it to wireless communications. Studies have shown that RSMA can achieve better system performance.

Summary of the invention

In view of the problems existing in the above-mentioned background technology, the present invention proposes a multi-cell communication system energy efficiency optimization method based on rate division multiple access assisted by intelligent reflective surface, which combines rate division multiple access RSMA with intelligent reflective surface RIS. RIS realizes intelligent regulation of signal transmission links to enhance signal transmission quality, thereby improving the signal quality of users at the edge of the cell; RSMA effectively suppresses co-channel interference between users by flexibly controlling rate division strategy and beamforming, and thus constructs an optimization problem with the goal of maximizing system energy efficiency, and jointly optimizes the beamforming vector on the base station side, the rate division multiple access matrix, and the phase shift matrix on the RIS side; in order to solve the non-convex optimization problem, an iterative solution of beamforming, rate division and phase shift optimization sub-problems is proposed, and continuous convex optimization is used to solve the former, and semi-definite programming and penalty function method are used to solve the latter.

A method for optimizing energy efficiency of a multi-cell communication system based on rate division multiple access assisted by an intelligent reflector comprises the following steps:

Step S1, establishing a multi-cell system model, determining the base station, the intelligent reflective surface RIS, the channel model between users and the deployment plan of the RIS;

Step S2, constructing a user group according to the actual service needs of the user; determining the public data stream and the private data stream in the rate division multiple access RSMA, and encoding them at the base station side;

Step S3, designing the RIS-RSMA mechanism to improve system performance, constructing an objective function to maximize system energy efficiency; determining the optimization variables as the beamforming vector and rate partitioning matrix on the base station side and the phase shift matrix on the RIS side; forming constraints, including public signal decoding constraints, user minimum rate constraints, RIS phase shift unit mode constraints, base station maximum transmission power constraints, and user rate requirement constraints;

Step S4, converting the above-constructed objective function into a convex optimization problem, decomposing it into beamforming and rate partitioning matrix optimization sub-problems and RIS phase shift optimization sub-problems, the former is solved by continuous convex optimization SCA, and the latter is solved by semidefinite programming and penalty function method;

Step S5, find the optimal solution of the beamforming vector, phase shift matrix and rate splitting matrix through the above steps, bring them into the objective function to obtain the system energy efficiency, give the algorithm flow and analyze the algorithm complexity, and finally compare the system energy efficiency of traditional SDMA, NOMA and this solution through simulation to verify the feasibility of the model and algorithm.

The beneficial effects achieved by the present invention are:

(1) This method explores the energy efficiency gain of the system brought by the combination of smart reflection surface (RIS) and rate division multiple access (RSMA) technology in the scenario of multi-cell edge users. The final analysis results show that compared with traditional space division multiple access (SDMA) and non-orthogonal frequency division multiple access (NOMA), RSMA can achieve 28.5% and 10.2% energy efficiency improvements in the multi-cell communication system, respectively (how this value is obtained, and whether it can be supplemented in the specific implementation method below to reflect it).

(2) Compared with the NOMA-based access scheme, the computational complexity required for signal processing is controlled and reduced, thereby improving system efficiency.

(3) By utilizing the reflection link of RIS, a good quality wireless transmission channel is collaboratively created, thereby improving the user's signal power, meeting the user's higher rate requirements, and reducing the base station's transmission power, thereby improving the system's energy efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing the overall structure of a scene in an embodiment of the present invention.

FIG. 2 is a schematic diagram of the structure of the RSMA working in an embodiment of the present invention.

FIG. 3 is a system simulation diagram in an embodiment of the present invention.

FIG. 4 is a diagram of system performance simulation under different mechanisms in an embodiment of the present invention.

DETAILED DESCRIPTION

The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings.

The present invention takes Figure 1 as the research scenario, which consists of three parts: base station, multiple RIS and multiple users. The energy efficiency of the system is studied, and the analysis and optimization of the total energy efficiency of the system brought by RSMA and RIS are focused.

Specifically, a method for optimizing energy efficiency of a multi-cell communication system based on intelligent reflective surface and rate division multiple access assistance includes the following steps:

Step S1: Establish a multi-cell system model, determine the channel model between the base station, RIS, and users, and the deployment plan of RIS;

Step S2: construct a user group according to the actual service needs of the user; determine the public data flow and private data flow in RSMA, and encode them on the base station side;

Step S3, designing the RIS-RSMA mechanism to improve system performance, constructing an objective function to maximize system energy efficiency; determining the optimization variables as the beamforming vector and rate partitioning matrix on the base station side and the phase shift matrix on the RIS side; forming constraints, mainly including base station transmit power constraints, RIS phase shift unit mode constraints, and user rate demand constraints;

Step S4, proposing a low-complexity algorithm to convert the above-constructed non-convex optimization problem into a convex optimization problem, which is mainly decomposed into beamforming and rate partitioning matrix optimization sub-problems and RIS phase shift optimization sub-problems. The former is solved by continuous convex optimization (SCA), and the latter is solved by semidefinite programming and penalty function method.

Step S5: Find the optimal solution of the beamforming vector, phase shift matrix and rate splitting matrix through the above steps, bring them into the objective function to obtain the system energy efficiency, give the algorithm flow and analyze the algorithm complexity, and finally compare the system energy efficiency of traditional SDMA, NOMA and this solution through simulation to verify the feasibility of the model and algorithm.

表示小区的集合，

表示小区边缘所有用户的集合，

表示RIS的集合。如图2，多个RIS反射基站发出的入射信号，其中每个RIS含有N个反射单元，第r个RIS的反射系数矩阵表示为：First, in step S1, a multi-cell system model is established. As shown in Figure 1, a RIS-assisted multi-cell communication system model is constructed. A 5G base station equipped with N _t antennas serves K single-antenna cell edge users. These users will be subject to co-frequency interference from adjacent cells. Multiple intelligent reflection surfaces RIS equipped with N reflection units act as cooperative nodes to reflect the signals transmitted by the base station. For the convenience of description,

represents a collection of cells,

represents the set of all users at the cell edge,

Represents the set of RIS. As shown in Figure 2, multiple RIS reflect the incident signal sent by the base station, where each RIS contains N reflection units, and the reflection coefficient matrix of the rth RIS is expressed as:

则该组所需的信号表示为

用户k接收到的信号可以表示为：For simplicity, assume that the coefficient β _n of all reflection units is 1, that is, the reflection unit only reflects the signal and does not bring gain. For user k, assume that the group number he belongs to is

The signal required by this group is expressed as

The signal received by user k can be expressed as:

表示第l个小区基站到用户k之间的直连复信道系数矩阵，其中上标H代表H_l,k的共轭转置，x_l表示第l个小区基站发射的信号，

表示第r个RIS与用户k之间的复信道系数矩阵，

表示第l个小区基站到第r个RIS之间的复信道系数矩阵，n_k表示加性高斯白噪声满足

其中

代表噪声方差。可以把直连信道和间接信道相结合为级联信道，

则

表示系统的下行信道链路。注意到，信道系数矩阵是实时变化的。为了方便研究，假设所有的信道系数在一个时间块内保持常数，但在不同的时间块上是相互独立变化的。in

represents the direct complex channel coefficient matrix between the l-th cell base station and user k, where the superscript H represents the conjugate transpose of H _l,k , x _l represents the signal transmitted by the l-th cell base station,

represents the complex channel coefficient matrix between the rth RIS and user k,

represents the complex channel coefficient matrix between the lth cell base station and the rth RIS, and n _k represents the additive white Gaussian noise satisfying

in

represents the noise variance. The direct channel and the indirect channel can be combined into a cascade channel.

but

Represents the downlink channel link of the system. Note that the channel coefficient matrix changes in real time. For the convenience of research, it is assumed that all channel coefficients remain constant within a time block, but change independently in different time blocks.

表示多播组集合，

表示组b中所有用户的集合。同一个组中的用户向基站所请求的信息相同，假设组b中的所有用户请求的信息为W_b。在基站发射端，基于RSMA的设计准则，

组中所需的信息W_b被分为公共部分W_c,b和私有部分W_p,b，其中所有的公共部分{W_c,1,…,W_c,B}使用所有用户共享的码本联合编码为一个所有用户期望的公共数据流s₀，而私有部分{W_p,1,…,W_p,B}被编码为各自对应的用户期望的数据流{s₁,…,s_B}。所有数据流为标准化功率，即

其中上标H代表信号的共轭转置。在基站端将这些数据流以不同功率水平线性叠加，即可产生发射信号矢量x。假定第l个小区为服务小区，该小区的基站发射的信号表示为：Next, in step S2, as shown in FIG2, the cell edge users are divided into B multicast groups according to the different request contents.

Represents a multicast group set,

represents the set of all users in group b. Users in the same group request the same information from the base station. Assume that the information requested by all users in group b is W _b . At the base station transmitter, based on the design principle of RSMA,

The information W _b required in the group is divided into a public part W _c,b and a private part W _p,b , where all public parts {W _c,1 ,…,W _c,B } are jointly encoded into a public data stream s ₀ expected by all users using a codebook shared by all users, while the private parts {W _p,1 ,…,W _p,B } are encoded into data streams {s ₁ ,…,s _B } expected by their respective users. All data streams are normalized in power, i.e.

The superscript H represents the conjugate transpose of the signal. At the base station, these data streams are linearly superimposed at different power levels to generate the transmitted signal vector x. Assuming that the lth cell is the service cell, the signal transmitted by the base station of this cell is expressed as:

表示组b的私有数据流波束赋形矢量，N_t表示基站的发射天线，

表示公共数据流波束赋形矢量，s₀表示公共数据流，s_b表示组b的私有数据流。第l个小区基站的最大发射功率表示为P_l,max，则有：in

represents the private data stream beamforming vector of group b, N _t represents the transmitting antenna of the base station,

represents the public data stream beamforming vector, s ₀ represents the public data stream, and s _b represents the private data stream of group b. The maximum transmit power of the l-th cell base station is denoted as P _l,max , then:

Furthermore, in step S3, a RIS-RSMA mechanism is designed to improve system performance. To achieve this goal, an optimization problem with the goal of maximizing system energy efficiency is modeled. For RSMA technology, first, the public data stream s ₀ is broadcast to all users and decoded by them through serial interference cancellation technology (SIC). Therefore, the public rate that can be achieved by the entire system is limited by the minimum public rate among all users, that is:

中，公共速率只有

是组

中用户所需要的部分，其中|W_p,b|表示信息W的长度。为了方便表示，将

定义为组

解码公共消息的速率。因此需满足

Where _Rc represents the achievable rate of decoding the public data stream _s0 , and _ck is the user public rate. Note that the core of RSMA technology is how to allocate the public rate.

The public rate is only

Yes Group

The part required by the user in the data, where |W _p,b | represents the length of the information W. For the convenience of representation,

Defined as Group

The rate at which public messages are decoded.

中所有用户，组

解码私有数据流s_b的可达速率受限于该组用户的最小私有解码速率，即

其中R_p,b表示用户

解码私有数据的速率。因此，通过以上分析，对于同一组中的所有用户的可达数据速率是相同的，可以定义为该组的组速率。对于组

而言，该组的组速率可以定义为公共速率

和私有速率

(对应组

)之和：The private data stream _sb is sent to the group through multicast

All users, groups

The achievable rate of decoding the private data stream s _b is limited by the minimum private decoding rate of the group of users, that is,

Where R _p,b represents the user

The rate at which private data is decoded. Therefore, through the above analysis, the achievable data rate for all users in the same group is the same, which can be defined as the group rate of the group.

For the group, the group rate can be defined as the public rate

and private rate

(Corresponding group

) and:

代入

中得：To represent the interference between cells,

Substitution

Win:

表示服务小区内其他组的数据流的干扰，

代表第l个基站对于组

对应的波束赋形向量，s_l,b代表第l个基站对于组b对应的信号，n_k表示加性噪声干扰，

表示其他小区基站的干扰，

代表第n个基站到用户k的信道，s_n,b代表第n个基站对于组b对应的信号。为了实现量化效果，假设使用单位带宽。根据香农公式得，用户k接收到的公共数据速率表示为：in,

Indicates the interference of data streams of other groups in the serving cell,

Represents the lth base station for group

The corresponding beamforming vector, s _l,b represents the signal corresponding to group b of the lth base station, _nk represents the additive noise interference,

Indicates the interference from other cell base stations.

represents the channel from the nth base station to user k, and s _n,b represents the signal corresponding to group b from the nth base station. In order to achieve the quantization effect, it is assumed that unit bandwidth is used. According to Shannon's formula, the public data rate received by user k is expressed as:

表示接收公共数据的信噪比，其中分母表示该小区内其它数据流的干扰和其他小区的同频干扰。通过串行干扰消除(SIC)技术去除公共信号干扰后，用户k接收到的私有数据速率表示为：in,

represents the signal-to-noise ratio of receiving public data, where the denominator represents the interference of other data streams in the cell and the co-channel interference of other cells. After removing the public signal interference through the serial interference cancellation (SIC) technology, the private data rate received by user k is expressed as:

表示接收私有信号的信噪比，分母表示去除公共数据流干扰后其它组私有数据流的干扰以及其他小区的同频干扰和噪声干扰。in

It represents the signal-to-noise ratio of the received private signal. The denominator represents the interference of other groups of private data streams after removing the interference of the public data stream and the co-channel interference and noise interference of other cells.

The total power consumption of the considered RIS-assisted RSMA multi-cell system includes the transmission power of the base station, the circuit power consumption of the base station and all users, and the power consumption of all RIS. Therefore, the total power consumption of the system is expressed as:

v_l＝μ_l ^-1，其中μ_l表示第l个小区基站的放大器效率，P_l表示基站的电路消耗功率，P_k表示用户电路消耗功率，P_R表示RIS的每个反射单元所消耗的功率。in

v _l =μ _l ^-1 , where μ _l represents the amplifier efficiency of the base station in the lth cell, P _l represents the circuit power consumption of the base station, P _k represents the circuit power consumption of the user, and _PR represents the power consumed by each reflection unit of the RIS.

(即基站侧的波束赋形向量)、RIS的相移矩阵Φ＝diag(Φ₁,…,Φ_R)、速率分割矩阵

来最大化系统的能效，所建模的优化问题为：Given the system model considered, the goal of the study is to jointly optimize the beamforming vectors of the base stations

(ie, the beamforming vector at the base station side), the phase shift matrix of RIS Φ = diag (Φ ₁ ,…,Φ _R ), the rate partition matrix

To maximize the energy efficiency of the system, the optimization problem modeled is:

代表第r个RIS的第n个RIS单元)，第4个约束表示基站最大发射功率约束，第5个约束表示每个用户分配的速率非负。由于优化目标是一个分式规划问题且优化变量之间是相互耦合的，与此同时，第一、二、三个约束都是是非凸约束，所以是一个非凸优化问题。接下来提出了一种交替优化算法、连续凸优化算法、SDR算法以及惩罚函数法对非凸问题进行凸问题转换。Constraint 1 ensures that all users can decode the common signal. The minimum rate limit for all users is in constraint 2 (this constraint defines that the user rate R _k needs to be greater than or equal to its minimum setting value to avoid the rate being negative). Constraint 3 represents the RIS phase shift limit (

represents the nth RIS unit of the rth RIS), the fourth constraint represents the maximum transmission power constraint of the base station, and the fifth constraint represents the non-negative rate allocated to each user. Since the optimization objective is a fractional programming problem and the optimization variables are mutually coupled, and at the same time, the first, second, and third constraints are all non-convex constraints, it is a non-convex optimization problem. Next, an alternating optimization algorithm, a continuous convex optimization algorithm, an SDR algorithm, and a penalty function method are proposed to convert the non-convex problem into a convex problem.

采用交替优化算法对目标函数进行优化。固定RIS的相移和其他小区上的w_m,m≠l。上述优化问题可以重新表示为：Secondly, in step S4, in order to solve the energy efficiency optimization problem in (P1), a low-complexity iterative algorithm is proposed by decoupling the variables and converting them into two sub-problems of alternating optimization of phase shift vector and beamforming vector. First, the RIS phase shift matrix is given, and the SCA method is used to optimize the beamforming vector and rate splitting matrix; then, according to the obtained variable values, the SDR and penalty function method are used to optimize the RIS phase shift matrix; finally, the alternating optimization algorithm is used until the objective function converges. First, the beamforming vector and rate splitting matrix of the serving cell base station are studied, and then the beamforming vector and rate splitting matrix of the interfering cell are designed. For convenience of representation, let

The alternating optimization algorithm is used to optimize the objective function. The phase shift of RIS and w _m,m≠l on other cells are fixed. The above optimization problem can be reformulated as:

For the optimization problem (P2), the objective function is a fractional programming problem, which is easy to prove to be non-convex. The first and second constraints are non-convex constraints. Next, the SCA method is used to convert the non-convex constraints into convex ones, as follows:

第一个约束可以等效为以下两个约束，其中β_k代表引入的第k个用户对应的松弛变量：First, introduce the slack variable

The first constraint can be equivalent to the following two constraints, where _βk represents the slack variable corresponding to the kth user introduced:

Similarly, the second constraint can be equivalent to the following two constraints, where _δk represents the slack variables corresponding to k users:

因此可以等效为以下两个约束，γ_k是松弛变量，可以视为变量，η_k也是变量：Since the above constraints are fractional constraints, they are still non-convex constraints. We further introduce slack variables

Therefore, it can be equivalent to the following two constraints, γ _k is a slack variable and can be regarded as a variable, and η _k is also a variable:

Similarly, the first two constraints of (P2) are equivalent to the following two constraints

The above constraints are still non-convex problems, and can be approximated by first-order Taylor expansion:

The form of (x) ⁽ⁿ⁾ represents the value of x at the nth iteration. From the above two constraints, it can be seen that the constraint is a first-order linear polynomial about w _l,0 ,w _l,u(k) ,γ _k ,η _k , so the constraint is a convex constraint. Finally, the optimization problem is equivalent to

It can be seen that the optimization goal is a concave-convex fractional programming problem about the objective function, which can be solved by the classic Dinkelbach method. In order to better describe the above algorithm flow, Algorithm 1 summarizes the detailed process of the SCA algorithm.

和速率分割矩阵

去除常数后，原优化问题可以等效为最大和速率问题，即：For the design of RIS reflection phase shift, by giving the base station side precoding matrix

Sum rate partition matrix

After removing the constant, the original optimization problem can be equivalent to the maximum sum rate problem, that is:

将

用

代入可以得到：Considering that the optimization variable Φ is implied in the first and second constraints, and the continuous phase shift constraint in the third constraint, this optimization problem is a non-convex problem and is not easy to solve using convex programming methods. Since there is only one variable to be optimized, the problem can be simplified to a feasibility test problem of finding the phase shift Φ, and the optimal value is obtained by searching for the solution in the feasible domain. The specific process is as follows: Let

Will

use

Substituting in, we get:

上式可以表示为：make

The above formula can be expressed as:

Similarly, we can get

可以得到make

Can get

Therefore, the optimization problem can be equivalent to

This optimization problem cannot be directly converted into a SOCP problem. The SDR technology can be used to approximately solve the problem:

Express the above formula in matrix form

其中t为辅助的复变量满足|t|＝1，那么可以得到：make

Where t is an auxiliary complex variable satisfying |t|＝1, then we can get:

Similarly, we can get

可以得到make

Can get

可以得到make

Can get

Then through transformation we can get:

令

其中满足V＞0且rank(V)＝1，原优化问题等效为：because

make

Where V>0 and rank(V)=1, the original optimization problem is equivalent to:

(P6) Find V

V _n,n ＝1,n＝1,…,RN

V ≥ 0

rank(V)＝1

In order to obtain a better converged solution, the problem (P6) is further transformed into an optimization problem with a clear objective to obtain a generally more efficient phase shift solution.

V _n,n ＝1,n＝1,…,RN

V ≥ 0

rank(V)=1 The rank-one constraint in the optimization problem (P7) can be equivalent to the following anti-convex constraint:

χ(V)-tr(V)＝0

其中v_max表示V的χ(V)对应的单位模特征矢量。根据矩阵的性质可知，χ(V)≤tr(V)恒成立。为了使χ(V)-tr(V)尽可能的大，引入惩罚函数法，优化目标转换为

其中τ≥0表示惩罚因子。最后秩一半定规划的优化问题(P7)进一步转换为如下优化问题，τ代表惩罚因子：Where χ(V) represents the maximum eigenvalue of V, so we can get

Where v _max represents the unit modulus eigenvector corresponding to χ(V) of V. According to the properties of the matrix, χ(V)≤tr(V) always holds. In order to make χ(V)-tr(V) as large as possible, the penalty function method is introduced, and the optimization objective is converted to

Where τ ≥ 0 represents the penalty factor. Finally, the optimization problem of rank-semidefinite programming (P7) is further transformed into the following optimization problem, where τ represents the penalty factor:

V _n,n ＝1,n＝1,…,RN

V ≥ 0

基于以上分析，在第j次迭代中，原优化问题可以近似为以下凸优化问题：Because the trace function is a convex function, it can be seen that the above constraints are all convex constraints, and the objective function is in the form of convex difference. The first-order approximation method is used to solve the problem. In the jth iteration, the first-order low approximation at the point {V ^{j} } can be used to approximate, that is, the objective function is equivalent to

Based on the above analysis, in the jth iteration, the original optimization problem can be approximated as the following convex optimization problem:

V _n,n ＝1,n＝1,…,RN

V ≥ 0

It can be seen that the original problem is transformed into a standard convex optimization problem, which can be solved by the CXV tool. The local optimal solution of the rank-one constraint can be obtained through iterative calculation. For better description, Algorithm 2 lists the algorithm flow of the penalty function method:

Finally, in order to better describe the process of the alternating optimization algorithm used to solve the optimization problem (P1), Algorithm 3 gives the overall process of the algorithm:

Finally, in step S5, Algorithm 3 gives an iterative algorithm for solving the energy efficiency maximization problem in (P1). It can be seen from Algorithm 3 that the complexity of solving problem (P1) is mainly determined by the complexity of (P3) and (P9). Specifically, the complexity of the continuous convex optimization (SCA) algorithm used for the beamforming vector and rate partitioning matrix is:

C ₁ =O(I ₁ (N _t +BN _t +2B+3K) ^3.5 )log(1/∈ ₁ )

Where O is the index of spatial complexity, _Nt represents the number of transmitting antennas, B represents the number of user groups, K represents the number of users, _I1 represents the number of iterations required for the algorithm to converge, ( _Nt + _BNt +2B+3K) represents the total number of variables[14], _{and ∈1} represents the accuracy of the SCA method used to solve problem (P3). The complexity of the semidefinite relaxation algorithm and penalty function method used to solve the RIS phase shift semidefinite programming problem is:

C ₂ =O(I ₂ (R(N+1)) ^3.5 )log(1/∈ ₂ )

Where (N+1) represents the dimension of the semidefinite programming matrix, R represents the number of RIS, and I ₂ represents the number of iterations of Algorithm 2 to converge. In summary, the total complexity of the proposed optimization algorithm is:

C＝O( _Itot ( _C1 + _C2 ))

Where I _tot represents the total number of iterations of the algorithm

In order to illustrate the effectiveness of the method proposed in the present invention, an example is given below. The performance of the proposed algorithm is evaluated by simulation. As shown in Figure 3, the system model studied is mapped to a practical scenario, which has 3 base stations, 6 users randomly located at the edge of the cell, and 3 RIS as cooperative nodes to reflect the signals sent by the base station. The base stations are located at (100, 400), (400, 100) and (400, 700), and the 6 users are randomly located in a circle with a radius of 10m centered at (250, 400). The 6 users are evenly divided into 3 groups, with two users in each group; RIS is used between the base station and the user to provide high quality to the user. Table 1 summarizes the system parameters.

Table 1 Simulation parameters

Assuming that the direct link channel h _d,k obeys Rayleigh fading and the RIS auxiliary channel obeys Rician fading, and assuming that the antenna elements at the base station and RIS are half-wavelength uniform linear arrays, the channels G and H _r,k can be modeled as:

和

表示服从CN(0,1)的非视距分量。Where L ₁ and L _2,k represent the corresponding path losses, σ represents the Ricaian factor, σ is set to 10, a represents the steering vector (a _N and a _M are used in the formula to represent the steering vector, and the subscripts are used to distinguish between different steering vectors), θ, ψ and φ _k represent the direction angles of the base station transmitting antenna, the RIS reflection unit and the user receiving signal, respectively.

and

Represents the non-line-of-sight component that obeys CN(0,1).

FIG4 is a comparison diagram of various mechanism systems under the above simulation conditions. It can be seen that the RIS-RSMA described in this method achieves the best performance efficiency results.

The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed by the present invention should be included in the protection scope recorded in the claims.

Claims (8)

1. An energy efficiency optimization method of a multi-cell communication system based on intelligent reflection surface assisted rate division multiple access is characterized by comprising the following steps: the method comprises the following steps:

step S1, a multi-cell system model is established, and a base station, an intelligent reflection surface RIS, a channel model among users and a deployment scheme of the RIS are determined;

step S2, constructing a user group according to the actual business requirements of the user; determining public data stream and private data stream in the rate division multiple access (RSMA), and encoding at the base station side;

s3, designing a RIS-RSMA mechanism to achieve improvement of system performance, and constructing an objective function to maximize system energy efficiency; the optimization variables are determined as a beam forming vector and a rate dividing matrix at the base station side and a phase shift matrix at the RIS side; forming constraint conditions, including common signal decoding constraint, user minimum rate constraint, RIS phase shift unit mode constraint, base station maximum transmitting power constraint and user rate requirement constraint;

s4, converting the constructed objective function into a convex optimization problem, decomposing the convex optimization problem into a beam forming and rate dividing matrix optimization sub-problem and a RIS phase shift optimization sub-problem, solving the convex optimization problem by adopting a continuous convex optimization SCA method, and solving the convex optimization problem by adopting a semi-definite programming and punishment function method;

and S5, finding the optimal solution of the beam forming vector, the phase shift matrix and the rate dividing matrix through the steps, bringing the optimal solution into an objective function to obtain the system energy efficiency, giving an algorithm flow, analyzing the algorithm complexity, and finally verifying the feasibility of the model and the algorithm through simulation comparison.

2. The method for optimizing energy efficiency of a multi-cell communication system based on intelligent reflection-assisted rate division multiple access according to claim 1, wherein the method comprises the steps of: in step S1, one of the multi-cell system models is provided with N _t The 5G base station of the root antenna serves K single-antenna cell edge users that will suffer co-channel interference from neighboring cells, wherein a plurality of RIS equipped with N reflecting units reflect the signals transmitted by the base station as cooperating nodes.

3. The method for optimizing energy efficiency of a multi-cell communication system based on intelligent reflection-assisted rate division multiple access according to claim 1, wherein the method comprises the steps of: in step S2, the cell edge users are divided into B multicast groups according to the content of the request, the information required in the groups is divided into public parts and private parts, wherein all public parts are jointly encoded into a public data stream desired by all users by using a codebook shared by all users, and the private parts are encoded into data streams desired by the respective corresponding users.

4. The method for optimizing energy efficiency of a multi-cell communication system based on intelligent reflection-assisted rate division multiple access according to claim 1, wherein the method comprises the steps of: in step S2, the base station linearly superimposes the data streams at different power levels to generate a transmission signal.

5. The method for optimizing energy efficiency of a multi-cell communication system based on intelligent reflection-assisted rate division multiple access according to claim 1, wherein the method comprises the steps of: in step S3, the public rate is limited by the minimum public rate among all users, and the achievable rate of decoding the private data stream is limited by the minimum private decoding rate of the group of users.

6. The method for optimizing energy efficiency of a multi-cell communication system based on intelligent reflection-assisted rate division multiple access according to claim 1, wherein the method comprises the steps of: in step S3, for each group, the group rate may be defined as the sum of the public rate and the private rate.

7. The method for optimizing energy efficiency of a multi-cell communication system based on intelligent reflection-assisted rate division multiple access according to claim 1, wherein the method comprises the steps of: in step S3, interference between cells is represented by interference of data streams of other groups in the cells, interference of base stations of other cells, and additive noise interference.

8. The method for optimizing energy efficiency of a multi-cell communication system based on intelligent reflection-assisted rate division multiple access according to claim 1, wherein the method comprises the steps of: in step S4, firstly, a RIS phase shift matrix is given, and a SCA method is adopted to optimize a beam forming vector and a rate dividing matrix; then, optimizing the RIS phase shift matrix by adopting SDR and penalty function methods according to the obtained variable values; and finally, an algorithm is optimized alternately until the objective function converges.

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