CN116419245A - Multi-cell communication system energy efficiency optimization method based on intelligent reflection surface assisted rate division multiple access - Google Patents
Multi-cell communication system energy efficiency optimization method based on intelligent reflection surface assisted rate division multiple access Download PDFInfo
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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
Technical Field
The invention belongs to the technical field of mobile communication, and particularly relates to an energy efficiency optimization method of a multi-cell communication system based on intelligent reflection surface assisted rate division multiple access.
Background
Compared to 4G/5G communication, 6G will provide greater capacity, lower latency, high reliability, high security, and full space coverage, and in order to meet the demands of emerging applications such as Augmented Reality (AR) and Virtual Reality (VR) for higher rate and lower latency, the network capacity of a 6G wireless communication system is expected to be 100 times that of a 5G wireless communication system. Meanwhile, the method searches and breaks through the restriction of a plurality of uncontrollable factors in the wireless environment, and reconfigures the wireless transmission environment, thereby being a new direction of 6G development. In the last decade, in order to meet various emerging business demands of users, communication students and experts have proposed and thoroughly studied various wireless technologies, among which most prominent ones include Ultra Dense Networks (UDNs), large-scale Multiple Input Multiple Output (MIMO), wireless cellular networks, and millimeter wave communications, which have made the rise of spectrum efficiency to realize a great leap, thereby satisfying the ultra high capacity demands required for large-scale communications between a large number of wireless devices, but the concomitant system energy consumption and hardware costs are key problems faced in practical applications. In order to realize the sixth generation of green and environment-friendly wireless communication concept, the key is to search for an environment-friendly and energy-saving technical support.
Recently, as a key technology in 6G, smart reflective surfaces (RIS) are considered as a very promising green and cost-effective potential solution. RIS improves the throughput and energy efficiency of a wireless network by reconfiguring the wireless propagation environment. Specifically, the RIS is a two-dimensional array of a large number of passive reflective elements, each of which can independently adjust the phase shift of the incident signal in real time by the RIS controller.
By dividing the users in the power domain, non-orthogonal frequency division multiple access (NOMA) can serve multiple users simultaneously under the same frequency or time resources, and thus NOMA-based access schemes can achieve higher spectral efficiency than traditional Orthogonal Multiple Access (OMA), however, with NOMA, the users have to decode all interference when receiving the message, which greatly increases the computational complexity required for signal processing. To solve this problem, a learner proposed a concept of a rate division multiple access (RSMA) and applied it to wireless communication, and studies indicate that the RSMA can achieve more excellent system performance.
Disclosure of Invention
Aiming at the problems in the background technology, the invention provides an energy efficiency optimization method of a multi-cell communication system based on intelligent reflection surface assisted rate division multiple access, which combines the 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.
An energy efficiency optimization method of a multi-cell communication system based on intelligent reflection surface assisted rate division multiple access 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 and analyzing the algorithm complexity, and finally comparing the traditional SDMA and NOMA with the system energy efficiency of the scheme through simulation, and verifying the feasibility of the model and the algorithm.
The beneficial effects achieved by the invention are as follows:
(1) In the method, the gain of system energy efficiency brought by combining an intelligent reflection surface (RIS) with a rate division multiple access technology (RSMA) is explored under the multi-cell edge user scene, and finally analysis results show that compared with the traditional Space Division Multiple Access (SDMA) and non-orthogonal frequency division multiple access (NOMA), the RSMA can respectively realize the energy efficiency improvement of 28.5% and 10.2% of the energy efficiency of a multi-cell communication system (how the value is obtained and whether the value can be supplemented in the following specific embodiment or not).
(2) Compared with an access scheme based on NOMA, the method and the device control and reduce the computational complexity required by signal processing, and improve the system efficiency.
(3) By utilizing the RIS reflection link, a wireless transmission channel with good quality is cooperatively created, so that the signal power of a user is improved, the higher rate requirement of the user is met, the transmitting power of a base station is reduced, and the energy efficiency of a system is improved.
Drawings
Fig. 1 is an overall view of a scene in an embodiment of the invention.
Fig. 2 is a schematic diagram of the structure of the operation of the RSMA 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 simulation of system performance under different mechanisms in an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the attached drawings.
The invention takes figure 1 as a research scene which consists of a base station, a plurality of RIS and a plurality of users, the energy efficiency of a system is researched, and the analysis and optimization of the total energy efficiency of the system brought by RSMA and RIS are emphasized.
Specifically, the energy efficiency optimization method of the multi-cell communication system based on the intelligent reflecting surface and the rate division multiple access assistance comprises the following steps:
step S1, a multi-cell system model is established, and a base station, an RIS, a channel model among users and a deployment scheme of the RIS are determined;
s2, constructing a user group according to the actual business requirements of the user; determining public data stream and private data stream in RSMA, and coding at 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, which mainly comprise base station transmitting power constraint, RIS phase shift unit mode constraint and user rate requirement constraint;
step S4, providing a low-complexity algorithm, converting the constructed non-convex optimization problem into a convex optimization problem, mainly decomposing the convex optimization problem into a beam forming and rate division matrix optimization sub-problem and a RIS phase shift optimization sub-problem, solving the former by adopting continuous convex optimization (SCA), and solving the latter 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 and analyzing the algorithm complexity, and finally comparing the traditional SDMA and NOMA with the system energy efficiency of the scheme through simulation, and verifying the feasibility of the model and the algorithm.
First, in step S1, a multi-cell system model is first established, and as shown in fig. 1, a RIS-assisted multi-cell communication system model is established. One is provided with N t The 5G base station of the root antenna serves K cell edge users of the single antenna, which will be subjected to co-channel interference by neighboring cells, wherein a plurality of intelligent reflective surfaces RIS provided with N reflective units serve as cooperating nodes for reflecting signals transmitted by the base station. For convenience of description, the following will be made
Representing a set of cells, +.>
Representing the set of all users at the cell edge, +.>
Representing a collection of RIS. As shown in fig. 2, a plurality of RIS reflect the incident signals from the base stations, where each RIS contains N reflection units, and the reflection coefficient matrix of the r-th RIS is expressed as:
for simplicity, it is assumed that the coefficients beta of all the reflecting units n =1, i.e. the reflecting unit reflects only the signal, without gain. For user k, assume that its group number is
The signal required for the group is expressed as +.>
The signal received by user k can be expressed as:
wherein the method comprises the steps of
Representing a direct-connection complex channel coefficient matrix between the base station of the first cell and the user k, wherein the superscript H represents H l,k Is the conjugate transpose of x l Representing the signal transmitted by the base station of the first cell, etc.>
Representing the complex channel coefficient matrix between the r-th RIS and user k,/and>
representing a complex channel coefficient matrix between the base station of the first cell and the rRIS of the first cell, n k Indicating that the additive white gaussian noise satisfies +.>
Wherein->
Representing the noise variance. The direct channel and the indirect channel can be combined into a cascade channel, < >>
Then->
Representing the downlink channel link of the system. Note that the channel coefficient matrix is changing in real time. For ease of study, it is assumed that all channel coefficients remain constant over one time block, but vary independently of each other over different time blocks.
Next, in step S2, as shown in fig. 2, the cell edge users are divided into B multicast groups according to the content of the request. For convenience of presentationBy using
Representing a multicast group set,/->
Representing the set of all users in group b. The information requested by the users in the same group to the base station is the same, assuming that the information requested by all the users in group b is W b . At the base station transmitting end, based on the design criteria of RSMA,>
information W required in a group b Is divided into a common part W c,b And private part W p,b Wherein all common parts { W } c,1 ,…,W c,B Joint coding of a common data stream s intended by all users using a codebook shared by all users 0 While private part { W p,1 ,…,W p,B Encoded into respective corresponding user desired data streams s 1 ,…,s B }. All data streams are normalized power, i.e. +.>
Wherein the superscript H represents the conjugate transpose of the signal. The data streams are linearly superimposed at the base station end at different power levels to produce a transmit signal vector x. Assuming that the first cell is a serving cell, the signal transmitted by the base station of this cell is expressed as:
wherein the method comprises the steps of
Beamforming vector, N, representing private data stream of group b t Representing the transmitting antenna of the base station,
representing common data stream beamforming vectors, s 0 Representing a common data stream, s b Representing the private data stream of group b. The maximum transmit power of the first cell base station is denoted as P l,max The following steps are:
furthermore, in the step S3, a RIS-RSMA mechanism is designed to achieve the improvement of system performance. To achieve this goal, optimization problems aimed at maximizing system energy efficiency have been modeled. For RSMA technology, first of all for the common data stream s 0 The common rate that the whole system can achieve is limited by the minimum common rate among all users, namely:
wherein R is c Representing a decoded common data stream s 0 The achievable rate of c k For the user common rate. Note that the core for RSMA technology is how to allocate common rates. In the group of
In which the common rate is only +.>
Is a group->
In which |W p,b The i indicates the length of the information W. For convenience of presentation, will->
Defined as group->
Rate of decoding common messages. Therefore, it is necessary to satisfy->
In private data stream s b Sending to group by multicast mode
All users of (group)>
Decoding private data stream s b Is limited by the minimum private decoding rate of the group of users, i.e. +.>
Wherein R is p,b Representing user +.>
The rate at which the private data is decoded. Thus, through the above analysis, the achievable data rates for all users in the same group are the same, which may be defined as the group rate for that group. For group->
In other words, the group rate of the group can be defined as the common rate +.>
And private Rate->
(corresponding group->
) And (2) sum:
to represent the interference between cells, the following will be presented
Substituted into->
Is obtained by:
wherein,,
indicating interference of data streams of other groups within the serving cell,/->
Represents the first base station for group->
Corresponding beamforming vectors, s l,b Represents the signal corresponding to group b by the first base station, n k Representing additive noise disturbance->
Indicating interference of other cell base stations, +.>
Representing the channel from the nth base station to user k, s n,b Representing the signal corresponding to group b for the nth base station. In order to achieve the quantization effect, it is assumed that a unit bandwidth is used. According to shannon's formula, the common data rate received by user k is expressed as:
wherein,,
representing the signal-to-noise ratio of the received common data, wherein the denominator represents the interference of other data streams within the cell and the co-channel interference of other cells. After removing the public signal interference by the Serial Interference Cancellation (SIC) technique, the private data rate received by user k is expressed as:
wherein the method comprises the steps of
Representing the signal-to-noise ratio of the received private signal, and the denominator represents the interference of other groups of private data streams and the co-channel interference and noise interference of other cells after the interference of the public data stream is removed.
The total power consumption of the considered RIS assisted multi-cell system of RSMA includes the transmit power of the base station, the circuit power consumption of the base station and all users, and the power consumption of all RIS. The total power consumption of the system is thus expressed as:
wherein the method comprises the steps of
v l ＝μ l -1 Wherein mu l Representing the amplifier efficiency, P, of the base station of the first cell l Indicating the circuit power consumption of the base station, P k Indicating the power consumption of the user circuit, P R Representing the power consumed by each reflective element of the RIS.
Given the system model under consideration, the goal of the study is to jointly optimize the beamforming vectors of the base station
(i.e., beamforming vector at base station), phase shift matrix Φ=diag (Φ) of RIS 1 ,…,Φ R ) Rate partitioning matrix
To maximize the energy efficiency of the system, the modeled optimization problem is:
Secondly, in the step S4, in order to solve the energy efficiency optimization problem in (P1), two sub-problems of alternating optimization phase shift vector and beam forming vector are converted after decoupling variable, a low-complexity iterative algorithm is provided, firstly, an RIS phase shift matrix is given, and the SCA method is adopted to optimize the beam forming vector and the rate division 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. Firstly, researching a beam forming vector and a rate dividing matrix of a base station of a serving cell, and then designing the beam forming vector and the rate dividing matrix of an interference cell. For the convenience of representation, let
And optimizing the objective function by adopting an alternate optimization algorithm. Fixing the phase shift of RIS and w on other cells m,m≠l . The above optimization problem can be re-expressed as:
the objective function is a split-plan problem for the optimization problem (P2), which easily proves to be non-convex, and non-convex for both the first and second constraints. Then adopting the SCA method to perform convex conversion on the non-convex constraint, specifically comprising the following steps:
first introducing relaxation variables
The first constraint can be equivalent to two constraints, where β k Representing the relaxation variable corresponding to the kth user introduced:
similarly, for the second constraint, the following two constraints, δ, may be equivalent k Representing the relaxation variables corresponding to k users:
since the constraint is a split constraint, it is still a non-convex constraint, further introducing a relaxation variable
Therefore, can be equivalently expressed as two constraints, gamma k Is a relaxation variable, which can be regarded as a variable, η k Also the variables:
the first two constraints of the same theory (P2) are equivalent to the following two constraints
The above constraint is still a non-convex problem and the use of a first order taylor expansion can be approximated as:
wherein, for example (x) (n) The n-th iteration of x is expressed in terms of the value of x, from the two constraints above, it can be seen that the constraint is about w l,0 ,w l,u(k) ,γ k ,η k Is a first order linear polynomial of (c), the constraint is therefore a convex constraint. Finally, the optimization problem is equivalent to
It can be seen that the optimization objective is an meniscus programming problem for the objective function, which can be solved using classical Dinkelbach methods. In order to better describe the algorithm flow above, algorithm 1 summarizes the detailed process of the SCA algorithm.
For RIS reflection phase shift design, through given base station side precoding matrix
And rate splitting matrix->
After the constants are removed, the original optimization problem can be equivalent to the maximum sum rate problem, namely:
considering the implicit optimization variable Φ in the first and second constraints, and the continuous phase shift constraint in the third constraint, the optimization problem is a non-convex problem that is not well solved by convex planning methods. Since the optimization has only one variable, the problem can be simplified into a feasibility check problem of finding the phase shift phi, and the optimal value is obtained by searching the solution in the feasible domain, and the specific process is as follows: order the
Will->
Use->
Substitution can be obtained:
order the
The above formula can be expressed as:
can be obtained in the same way
Order the
Can obtain
The optimization problem can be equivalent to
The optimization problem cannot be directly converted into an SOCP problem, and the problem can be approximately solved by adopting SDR technology:
the above is represented in matrix form
Order the
Where t is the complex variable of the auxiliary satisfying |t|=1, then it can be obtained:
can be obtained in the same way
Order the
Can obtain
Order the
Can obtain
Then, the transformation can be obtained:
due to
Let->
Wherein V > 0 and rank (V) =1 is satisfied, the original optimization problem is equivalent to:
(P6)Find V
V n,n ＝1,n＝1,…,RN
V≥0
rank(V)＝1
to obtain a better convergence solution, the problem (P6) is further translated into an optimization problem with a well-defined objective to obtain a generally more efficient phase shift solution.
V n,n ＝1,n＝1,…,RN
V≥0
The rank-one constraint in the rank (V) =1 optimization problem (P7) can be equivalent to the inverse convex constraint as follows:
χ(V)-tr(V)＝0
wherein χ (V) represents the maximum eigenvalue of V, thus can be obtained
Wherein v is max And X (V) represents the corresponding unit mode characteristic vector of V. According to the matrix property, χ (V) is smaller than tr (V) constantly. In order to make χ (V) -tr (V) as large as possible, a penalty function is introduced, the optimization objective is converted into +.>
Wherein τ.gtoreq.0 represents a penalty factor. The optimization problem (P7) of the last rank half-defined plan is further converted into an optimization problem, τ representing a penalty factor: />
V n,n ＝1,n＝1,…,RN
V≥0
Since the trace function is a convex function, it can be seen that the constraint conditions are convex constraints, the objective function is in the form of convex differences, and a first order approximation method is used to solve the problem. In the jth iteration, the point { V may be used {j} First order low approximation at }, i.e. the objective function is equivalent to
Based on the above analysis, in the jth iteration, the original optimization problem may 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 a standard convex optimization problem, the problem can be solved by a CXV tool, and a locally optimal solution of rank-one constraint can be obtained through iterative calculation. For a better description, algorithm 2 lists the algorithm flow of the penalty function method:
finally, to better describe the process of the alternative optimization algorithm employed to solve the optimization problem (P1), algorithm 3 gives the overall flow of the algorithm:
finally, in said step S5, algorithm 3 gives an iterative algorithm for solving the energy efficiency maximization problem in (P1), and 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 successive convex optimization (SCA) algorithm employed for the beamforming vector and the rate splitting matrix is:
C 1 ＝O(I 1 (N t +BN t +2B+3K) 3.5 )log(1/∈ 1 )
wherein O is the index of spatial complexity, N t Representing the number of transmitting antennas, B representing the number of user groups, K representing the number of users, I 1 Representing the number of iterations required for algorithm convergence, (N) t +BN t +2B+3K represents the total number of variables [14 ]]，∈ 1 The accuracy of the SCA method for solving the problem (P3) is represented; the complexity of the semi-definite relaxation algorithm and penalty function method adopted for solving the RIS phase shift semi-definite programming problem is as follows:
C 2 ＝O(I 2 (R(N+1)) 3.5 )log(1/∈ 2 )
wherein (n+1) represents the dimension of the semi-definite programming matrix, R represents the number of RIS, I 2 Representing the number of iterative convergence of algorithm 2. In summary, the overall complexity of the proposed optimization algorithm is:
C＝O(I tot (C 1 +C 2 ))
wherein I is tot Representing the total number of iterative algorithms
To illustrate the effectiveness of the method of the present invention, an example is given below. The performance of the proposed algorithm is evaluated by simulation. As shown in fig. 3, the system model under study maps a practical scenario with 3 base stations, 6 users randomly located at the cell edge, and 3 RIS as cooperative nodes reflecting the signals from the base stations. Wherein the base station is located at three positions (100, 400), (400, 100) and (400,700), 6 users are randomly located in a circle with a radius of 10m around (250,400), and the 6 users are equally divided into 3 groups of two users each; the 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
Suppose direct link channel h d,k Following rayleigh fading, the RIS-assisted channel follows Rician fading and channels G and H are assumed to be half-wavelength uniform linear arrays of antenna elements at the base station and RIS r,k Can be modeled as:
wherein L is 1 、L 2,k Representing the corresponding path loss, σ represents the rician factor, setting σ=10, a represents the steering vector (where a is used N And a M Representing steering vectors, subscripts to distinguish between different steering vectors), θ, ψ, and φ k Indicating the direction angles of the base station transmitting antenna, the RIS reflecting unit and the user received signal respectively,
and->
Representing non-line-of-sight components subject to CN (0, 1).
FIG. 4 is a comparison of the system of mechanisms under the above simulation conditions, and it can be seen that the RIS-RSMA described in the present method achieves the best performance efficiency results.
The above description is merely of preferred embodiments of the present invention, and the scope of the present invention is not limited to the above embodiments, but all equivalent modifications or variations according to the present disclosure will be within the scope of 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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