CN119449103A

CN119449103A - Information transmission method, device and storage medium

Info

Publication number: CN119449103A
Application number: CN202310961496.8A
Authority: CN
Inventors: 高子路
Original assignee: Datang Mobile Communications Equipment Co Ltd
Current assignee: Datang Mobile Communications Equipment Co Ltd
Priority date: 2023-08-01
Filing date: 2023-08-01
Publication date: 2025-02-14

Abstract

The embodiment of the application provides an information transmission method, device and storage medium, which comprise the steps of obtaining equipment information of an intelligent super-surface RIS, channel state information CSI and transmitting power information of a system, wherein the equipment information comprises the number of reflection units contained in the RIS and reflection coefficients of all reflection units contained in the RIS, determining an optimal transmitting precoding matrix and an optimal RIS reflection coefficient matrix which maximize system throughput based on the equipment information, the CSI and the transmitting power information under the condition that the constraint condition of reflection phase shift of the RIS reflection units, the constraint condition of a function relation between reflection amplitude and reflection phase shift of the RIS reflection units and the constraint condition of transmitting power are met, and adopting the optimal transmitting precoding matrix to transmit information and configuring the optimal RIS reflection coefficient matrix to the RIS for auxiliary relay of information transmission by the RIS.

Description

Information transmission method, device and storage medium

Technical Field

The present application relates to the field of wireless communications technologies, and in particular, to an information transmission method, an information transmission device, and a storage medium.

Background

In a communication scenario based on intelligent super-surface (Reconfigurable Intelligent Surface, RIS), existing beamforming design algorithms all assume that the reflection amplitude of the RIS unit is constant at 1, i.e. assume that the RIS units are all ideal reflection conditions. However, in practical engineering applications, the incident signal cannot realize total reflection on the RIS unit (i.e., the reflection amplitude of the RIS unit is not constant to 1), which results in information transmission of the beamforming parameters obtained by using the existing beamforming design algorithm, and in practical engineering applications, the maximization of the throughput of the system cannot be realized.

Disclosure of Invention

Aiming at the problems existing in the prior art, the embodiment of the application provides an information transmission method, an information transmission device and a storage medium.

In a first aspect, an embodiment of the present application provides an information transmission method, including:

acquiring equipment information of an intelligent super-surface RIS, channel State Information (CSI) and transmitting power information of a system, wherein the equipment information comprises the number of reflection units contained in the RIS and reflection coefficients of all the reflection units contained in the RIS;

Determining an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximize system throughput based on the device information, the CSI, and the transmit power information, under conditions that satisfy a reflective phase shift constraint of the RIS reflection unit, a constraint of a functional relationship between a reflective amplitude of the RIS reflection unit and a reflective phase shift, and a transmit power constraint;

And carrying out information transmission by adopting the optimal transmitting precoding matrix, and configuring the optimal RIS reflection coefficient matrix to the RIS for carrying out auxiliary relay on the information transmission by adopting the optimal RIS reflection coefficient matrix by the RIS.

In some embodiments, the obtaining device information for the intelligent subsurface RIS includes:

transmitting control signaling to the RIS or a control device of the RIS, wherein the control signaling is used for indicating the RIS or the control device to report the device information of the RIS;

And receiving the device information sent by the RIS or the control device.

In some embodiments, the determining an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximize system throughput based on the device information, the CSI, and the transmit power information comprises:

determining an initial RIS reflection coefficient matrix based on the equipment information, and determining an initial transmission precoding matrix based on the initial RIS reflection coefficient matrix, the CSI and the transmission power information;

And determining an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix for maximizing system throughput based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix.

In some embodiments, the determining an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximizes system throughput based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix comprises:

Determining the optimal transmission precoding matrix and the optimal RIS reflection coefficient matrix based on the initial RIS reflection coefficient matrix and the initial transmission precoding matrix and a weighted mean square error function;

The weighted mean square error function is determined based on a signal mean square error function and a weighting coefficient of each receiving end device in the system, and the signal mean square error function is used for representing the mean square error between a transmitting signal detected by the receiving end device and a signal transmitted by the transmitting end device.

In some embodiments, the determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix, and a weighted mean square error function, comprises:

Based on the initial RIS reflection coefficient matrix and the initial transmission precoding matrix, using an alternating optimization algorithm to obtain the optimal transmission precoding matrix and the optimal RIS reflection coefficient matrix through multiple iterations with the value of the weighted mean square error function minimized as a target;

In each iteration, aiming at any target optimization variable of the weighted mean square error function, determining an optimal solution of the target optimization variable in the current iteration based on optimal solutions obtained in the previous iteration of other optimization variables except the target optimization variable.

In some embodiments, the weighted mean square error function is:

Where W represents a transmit precoding matrix, w= [ W ₁,…,w_k…,w_K],w_k ] represents precoding vectors corresponding to a receiving end device K, k=1, 2..and K represents the total number of receiving end devices in the system, Φ represents an RIS reflection coefficient matrix, Representing the reflection coefficient of the reflection unit N, n=1, 2,..n, N representing the number of reflection units comprised by said RIS, u and ρ being the sets of u _k and ρ _k, respectively, u _k representing the auxiliary variables of the receiving end device k, ρ _k representing the weighting parameters of the receiving end device k, MSE _k representing the signal mean square error function of the receiving end device k,The method comprises the steps of representing a transmitting signal detected by a receiving end device k, s _k representing a signal transmitted by the transmitting end device, superscript x representing conjugation, h _d,k representing a direct-view channel parameter from the transmitting end device to the receiving end device k, h _r,k representing a channel parameter from the RIS to the receiving end device k, diag (phi) representing a diagonal matrix with diagonal elements being elements in phi, G representing a channel parameter from the transmitting end device to the RIS, re representing a real part, and sigma ² representing noise power.

In some embodiments, where the target optimization variable is ρ, the determining the optimal solution for the target optimization variable in the current iteration comprises:

The optimal solution for ρ _k in the current iteration is determined based on the following formula:

In the formula, Representing the optimal solution for ρ _k in the current iteration, the MSE _k is determined based on the optimal solution for the optimization variable for the previous iteration of the current iteration.

In some embodiments, where the target optimization variable is u, the determining an optimal solution for the target optimization variable in the current iteration includes:

The optimal solution for u _k in the current iteration is determined based on the following formula:

In the formula, Representing an optimal solution for u _k in the current iteration, the diag (Φ), the w _k, and the w _j are all determined based on an optimal solution for an optimization variable of a previous iteration of the current iteration.

In some embodiments, where the target optimization variable is a transmit precoding matrix W, the determining an optimal solution for the target optimization variable in the current iteration includes:

the optimal solution of the precoding vector w _k corresponding to the receiving end device k in the current iteration is determined based on the following formula:

In the formula, Representing the optimal solution of w _k in the current iteration, the ρ _j, the ρ _k, theAnd said diag (phi) are each determined based on an optimal solution of an optimization variable of a previous iteration of said current iteration,N _t order identity matrix, N _t antenna unit number of the transmitting terminal device, upper label H transpose, lambda based on the setAnd P represents the transmitting power.

In some embodiments, where the target optimization variable is the RIS reflection coefficient matrix Φ, the determining the optimal solution for the target optimization variable in the current iteration includes:

The reflection coefficient of the reflection unit n in the present iteration is determined based on Is the optimal solution of (a):

determining an optimal solution for the reflection phase shift θ _n for reflection element n in the current iteration;

Determining a reflection amplitude beta _n of the reflection unit n in the current iteration based on an optimal solution of the reflection phase shift theta _n of the reflection unit n in the current iteration and a constraint condition of a functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit;

Determining the reflection coefficient of the reflection unit n in the current iteration based on the optimal solution of the reflection phase shift theta _n of the reflection unit n in the current iteration and the determined reflection amplitude beta _n of the reflection unit n in the current iteration Is a solution to the optimization of (3).

In some embodiments, the determining the optimal solution for the reflected phase shift θ _n for the reflective element n in the current iteration includes:

Determining the curve type of a reflection coefficient optimization equivalent function g (theta _n) of the RIS reflection unit in the current iteration;

In the case where the curve of g (θ _n) is a single-peak curve, determining that the optimal solution of the reflection phase shift θ _n of the reflection unit n in the current iteration is-pi or pi;

When the curve of g (θ _n) is any one of the curves of single wave Gu Quxian, left peak right Gu Quxian and left Gu Youfeng, determining a value of θ _n corresponding to a curved trough point of g (θ _n), and determining a value of θ _n corresponding to a curved trough point of g (θ _n) as an optimal solution of the reflection phase shift θ _n of the reflection unit n in the current iteration.

In some embodiments, the determining the curve type of the reflection coefficient optimization equivalence function g (θ _n) of the RIS reflection unit in the current iteration includes:

Determining four judgment points theta _n,1、θ_n,2、θ_n,3 and theta _n,4, wherein theta _n,1＝-π,θ_n,2＝-π+△θ_judge,θ_n,3＝π-△θ_judge,θ_n,4＝π,△θ_judge is a preset angle interval, and delta theta _judge is more than 0;

In the case of g (θ _n,2)＞g(θ_n,1) and g (θ _n,3)＞g(θ_n,4), determining that the curve of g (θ _n) is a single-peak curve;

In the case of g (θ _n,2)＜g(θ_n,1) and g (θ _n,3)＜g(θ_n,4), the curve of g (θ _n) is determined to be a single wave Gu Quxian;

In the case of g (θ _n,2)＞g(θ_n,1) and g (θ _n,3)＜g(θ_n,4), the curve of g (θ _n) is determined to be Gu Quxian on the left and right;

In the case of g (θ _n,2)＜g(θ_n,1) and g (θ _n,3)＞g(θ_n,4), the curve of g (θ _n) is determined to be the left Gu Youfeng curve.

In some embodiments, the expression of g (θ _n) is:

g(θ_n)＝2|X_n|β_ncos(∠X_n-θ_n)+A(n,n)β_n ²+C_n

v_k,j＝h_r,kdiag(Gw_j)

v_k,k＝h_r,kdiag(Gw_k)

Wherein a (n, n), a (n, n ₂)、A(n₁,n₁)、A(n₁,n₂) represent the n-th element of row n in matrix a, the n ₂ -th element of row n ₂ in matrix a, the n ₁ -th element of column n ₁ in matrix a, the n ₁ -th element of column n ₂ in matrix a, b (n), b (n ₁) represent the n-th element of vector b, the n ₁ -th element of vector b, respectively, and the ρ _k, u _k, w _j and w _k are all determined based on the optimal solutions of the optimization variables of the previous iteration of the current iteration.

In some embodiments, the determining the value of θ _n corresponding to the curved trough point of g (θ _n) includes:

Determining a left boundary point and a right boundary point of a search interval based on the curve type of g (theta _n);

And based on the determined left boundary point and right boundary point of the search interval, one-dimensional search is carried out in the search interval, and a theta _n value corresponding to the curve trough point of g (theta _n) is determined.

In some embodiments, the determining the left and right boundary points of the search interval based on the curve type of g (θ _n) comprises:

In the case where the curve of g (θ _n) is a single-valley curve, pi and pi are respectively taken as the left boundary point and the right boundary point of the search interval.

In the case that the curve of g (theta _n) is a left-peak right-valley curve, three search points are determined AndWherein, Δθ _search1 is a preset angular interval, Δθ _search1 >0;

At the position of In the case of (a), willAndRespectively serving as a left boundary point and a right boundary point of the search interval;

At the position of In the case of (2), the current Δθ _search1 is multiplied by m ₁ to obtain a value as updated Δθ _search1, and the current value is calculatedAs updated value of (2)Will be currentAs updated value of (2)And update the saidSubtracting the updated delta theta _search1 as an updated valueBased on the updateAnd said updatedComparison ofAndAt the position ofIn the case of (3), the delta theta _search1 is repeatedly updated,AndUp to the updated courseAndSatisfy the following requirementsWill be updated at the present timeAndRespectively used as a left boundary point and a right boundary point of the search interval, wherein m ₁ is larger than or equal to 1.

In the case where the curve of g (θ _n) is a left Gu Youfeng curve, three search points are determined AndWherein, Δθ _search2 is a preset angular interval, Δθ _search2 >0;

At the position of In the case of (2), the current Δθ _search2 is multiplied by m ₂ to obtain a value as updated Δθ _search2, and the current value is calculatedAs updated value of (2)Will be currentAs updated value of (2)And update the saidAdding the updated value of delta theta _search2 as an updateBased on the updateAnd said updatedComparison ofAndAt the position ofIn the case of (3), the delta theta _search2 is repeatedly updated,AndUp to the updated courseAndSatisfy the following requirementsWill be updated at the present timeAndRespectively used as a left boundary point and a right boundary point of the search interval, wherein m ₂ is larger than or equal to 1.

In some embodiments, the determining the value of θ _n corresponding to the curved trough point of g (θ _n) based on the determined left and right boundary points of the search interval and performing a one-dimensional search in the search interval includes:

And based on the left boundary point and the right boundary point of the determined search interval, performing one-dimensional search in the search interval by using a golden section method, and determining a theta _n value corresponding to the curve trough point of g (theta _n).

In some embodiments, the constraints of the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit include:

wherein β _n represents the reflection amplitude of the reflection unit N, n=1, 2,..and N, N represents the number of reflection units contained in the RIS, θ _n represents the reflection phase shift of the reflection unit N, β _min is not less than 0, And l.gtoreq.0 are constants associated with a particular circuit implementation, beta _min represents the minimum reflection amplitude of the reflecting element,Representing the relative phase shift difference from-pi/2 to beta _min corresponding to the phase shift value, l is used to control the steepness of the curve between the reflected phase shift and the reflected amplitude.

In a second aspect, an embodiment of the present application further provides a transmitting end device, including a memory, a transceiver, and a processor;

The information transmission system comprises a memory for storing a computer program, a transceiver for receiving and transmitting data under the control of the processor, and a processor for reading the computer program in the memory and executing the information transmission method according to the first aspect.

In a third aspect, an embodiment of the present application further provides an information transmission apparatus, including:

the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring equipment information of an intelligent super-surface RIS, channel state information CSI and transmitting power information of a system, wherein the equipment information comprises the number of reflection units contained in the RIS and reflection coefficients of all the reflection units contained in the RIS;

a determining unit configured to determine an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximize a system throughput based on the device information, the CSI, and the transmit power information, in a case where a constraint condition of a reflection phase shift of the RIS reflecting unit, a constraint condition of a functional relation between a reflection amplitude of the RIS reflecting unit and the reflection phase shift, and a transmit power constraint condition are satisfied;

and the transmission unit is used for carrying out information transmission by adopting the optimal transmitting precoding matrix, configuring the optimal RIS reflection coefficient matrix to the RIS and carrying out auxiliary relay on the information transmission by adopting the optimal RIS reflection coefficient matrix by the RIS.

In a fourth aspect, an embodiment of the present application further provides a non-transitory readable storage medium storing a computer program for causing a processor to execute the information transmission method according to the first aspect described above.

In a fifth aspect, an embodiment of the present application further provides a communication device, where a computer program is stored, where the computer program is configured to cause the communication device to execute the information transmission method according to the first aspect.

In a sixth aspect, an embodiment of the present application further provides a processor-readable storage medium storing a computer program for causing a processor to execute the information transmission method according to the first aspect described above.

In a seventh aspect, an embodiment of the present application further provides a chip product, where a computer program is stored, where the computer program is configured to cause the chip product to perform the information transmission method according to the first aspect.

According to the information transmission method, the information transmission device and the storage medium, the optimal transmission precoding matrix and the optimal RIS reflection coefficient matrix which maximize the system throughput are determined based on the condition that the RIS unit is non-ideal reflection, and then information transmission is performed based on the optimal transmission precoding matrix and the optimal RIS reflection coefficient matrix, so that the maximization of the system throughput in practical engineering application can be achieved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following descriptions are some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.

Fig. 1 is a schematic flow chart of an information transmission method according to an embodiment of the present application;

FIG. 2 is an exemplary diagram of RIS reporting device information provided in an embodiment of the present application;

FIG. 3 is an exemplary diagram of a base station configuring an optimal RIS reflection coefficient matrix to a RIS according to an embodiment of the present application;

FIG. 4 is a graph showing an example of a g (θ _n) curve provided by an embodiment of the present application;

FIG. 5 is an exemplary diagram of a RIS-based communication scenario provided by an embodiment of the present application;

FIG. 6 is a graph of iteration count versus convergence provided by an embodiment of the present application;

FIG. 7 is a graph showing comparison of algorithm results under different parameter settings provided by an embodiment of the present application;

Fig. 8 is a schematic structural diagram of a transmitting end device according to an embodiment of the present application;

Fig. 9 is a schematic structural diagram of an information transmission device according to an embodiment of the present application.

Detailed Description

In the embodiment of the application, the term "and/or" describes the association relation of the association objects, which means that three relations can exist, for example, A and/or B, and can mean that A exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.

The term "plurality" in embodiments of the present application means two or more, and other adjectives are similar.

The terms "first," "second," and the like in embodiments of the present application are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or otherwise described herein, and that the "first" and "second" distinguishing between objects generally are not limited in number to the extent that the first object may, for example, be one or more.

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

In the existing beamforming algorithm design, only the situation that the RIS unit is ideal reflection is considered, namely, the reflection amplitude of the RIS unit is assumed to be constant to be 1. In practical engineering, however, the reflection amplitude of the RIS cell is not constant at 1. Specifically, the variation of the RIS unit phase shift affects the current intensity inside the electromagnetic unit, thereby changing the dielectric loss, metal loss and ohmic loss of the electromagnetic unit, and finally changing the reflection amplitude of the electromagnetic unit. This results in existing beamforming algorithms not being suitable for the actual RIS cell model. Therefore, in the design of the beam forming algorithm, the condition that the RIS unit is non-ideal reflection is considered to be more beneficial to the evaluation of the algorithm performance and the development of actual engineering tests.

Fig. 1 is a flow chart of an information transmission method according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:

And 100, acquiring equipment information of the intelligent super-surface RIS, channel State Information (CSI) and transmitting power information of a system, wherein the equipment information comprises the number of reflection units contained in the RIS and reflection coefficients of all the reflection units contained in the RIS.

The method can be applied to the RIS auxiliary communication scene, the execution subject of the method can be any transmitting end Equipment for information transmission in a system, such as network Equipment (e.g. base station), terminal (or User Equipment, UE) or other communication Equipment, etc., the application is not limited to specific Equipment, and the method can be executed by any Equipment as long as the Equipment is used as the transmitting end.

The system of the application refers to a communication system composed of transmitting terminal equipment, RIS and receiving terminal equipment, such as downlink information transmission between a base station and a plurality of terminals, wherein the RIS is used as an auxiliary relay for information transmission, the base station is the transmitting terminal equipment, and the terminal is the receiving terminal equipment.

The embodiment of the application determines the optimal transmitting precoding matrix and the optimal RIS reflection coefficient matrix which maximize the system throughput based on the condition that the RIS unit is non-ideal reflection, and then carries out information transmission based on the optimal transmitting precoding matrix and the optimal RIS reflection coefficient matrix, thereby realizing the maximization of the system throughput (system capacity) in practical engineering application.

Before determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix, the transmitting end device needs to acquire the device Information of the RIS, the channel state Information (CHANNEL STATE Information, CSI) of the system, and the transmit power Information. The device information of the RIS comprises information such as the number of reflection units contained in the RIS, the reflection coefficient of each reflection unit contained in the RIS and the like. The CSI of the system includes channel state information of a link between the transmitting end device and the RIS, channel state information of a link between the RIS and the receiving end device, channel state information of a direct view channel between the transmitting end device and the receiving end device, and the like. The transmit power information includes a transmit power value of the transmitting end device.

The CSI and the transmit power information of the system may be obtained by referring to an existing technical solution, which is not described herein.

And receiving the device information sent by the RIS or the control device.

For example, assuming that the transmitting end device is a base station, fig. 2 is an exemplary diagram of the RIS reporting device information provided by the embodiment of the present application, as shown in fig. 2, the base station may send a control signaling to the RIS or a control device of the RIS (the RIS may be controlled by a third party control device), and instruct the RIS or the control device of the RIS to report the device information of the RIS, thereby obtaining the device information of the RIS.

Step 101, determining an optimal transmission precoding matrix and an optimal RIS reflection coefficient matrix for maximizing the throughput of the system based on the device information, the CSI and the transmission power information, when the constraint condition of the reflection phase shift of the RIS reflection unit, the constraint condition of the functional relation between the reflection amplitude of the RIS reflection unit and the reflection phase shift, and the constraint condition of the transmission power are satisfied.

Specifically, in the case of non-ideal reflection, the reflection amplitude of the RIS unit is not constant 1, but a certain functional relation exists between the reflection amplitude and the reflection phase shift, and the functional relation can be used as one of constraint conditions when determining an optimal transmission precoding matrix and an optimal RIS reflection coefficient matrix so as to embody an actual RIS unit model.

For example, the RIS comprises N reflecting units, and for any one reflecting unit N, the reflecting amplitude of the reflecting unit is not constant to be 1, but is a variable related to the reflecting phase shift of the reflecting unit, so that the non-ideal reflecting condition of the RIS in practical engineering application can be more accurately simulated.

In some embodiments, the reflective phase shift constraint of the RIS reflective elements includes that the range of values of the reflective phase shift of each RIS reflective element is [ -pi, pi ].

In some embodiments, the transmit power constraint includes that the sum of the transmit powers allocated to all receiving end devices is less than or equal to the transmit power of the transmitting end device.

It should be noted that, in the present application, the system throughput may be maximized in various embodiments, for example, the sum of the achievable rates of all receiving end devices in the system may be maximized, or the sum of the system and the rates of all receiving end devices in the system may be maximized.

Step 102, performing information transmission by adopting the optimal transmitting precoding matrix, and configuring the optimal RIS reflection coefficient matrix to the RIS for the RIS to perform auxiliary relay on the information transmission by adopting the optimal RIS reflection coefficient matrix.

Specifically, after determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix, the transmitting end device may use the optimal transmit precoding matrix to perform information transmission, for example, perform precoding on a data stream to be transmitted by using the optimal transmit precoding matrix, determine the transmit power of each antenna, and so on.

And, the transmitting device may configure the optimal RIS reflection coefficient matrix to the RIS, for example, send the optimal RIS reflection coefficient matrix to the RIS or a control device of the RIS. Assuming that the transmitting end device is a base station, fig. 3 is an exemplary diagram of the base station configuring an optimal RIS reflection coefficient matrix to an RIS according to the embodiment of the present application, as shown in fig. 3, the base station may send a control signaling to the RIS or a control device of the RIS (the RIS may be controlled by a third party control device), where the control signaling includes the optimal RIS reflection coefficient matrix, so that the RIS may set the transmission coefficient of each reflection unit in the RIS by using the optimal RIS reflection coefficient matrix, thereby performing auxiliary relay on information transmission, and maximizing the throughput of the system.

According to the information transmission method provided by the embodiment of the application, the optimal transmitting precoding matrix and the optimal RIS reflection coefficient matrix which maximize the system throughput are determined based on the condition that the RIS unit is non-ideal reflection, and then the information transmission is carried out based on the optimal transmitting precoding matrix and the optimal RIS reflection coefficient matrix, so that the maximization of the system throughput in practical engineering application can be realized.

Specifically, before determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix that maximize the throughput of the system, the transmitting device may determine an initial RIS reflection coefficient matrix according to the reflection coefficients of the RIS reflection units in the obtained device information of the RIS. For example, an RIS reflection coefficient matrix composed of reflection coefficients of the RIS reflection units in the RIS device information is used as an initial RIS reflection coefficient matrix.

Then, the transmitting end device may determine an initial transmit precoding matrix according to the initial RIS reflection coefficient matrix, the acquired CSI of the system, and the transmit power information. For example, the initial transmit precoding matrix may be designed using a conventional precoding method such as maximum ratio transmission (Maximum Ratio Transmission, MRT), minimum mean square error (Minimum Mean Square Error, MMSE), zero Forcing (ZF) precoding method, etc., and will not be described herein.

After the initial RIS reflection coefficient matrix and the initial transmission precoding matrix are obtained, the optimal transmission precoding matrix and the optimal RIS reflection coefficient matrix which maximize the throughput of the system can be obtained through optimization iteration based on the initial values.

The initial RIS reflection coefficient matrix is determined based on the RIS equipment information, and the initial transmission precoding matrix is reasonably set based on the initial RIS reflection coefficient matrix, the acquired CSI and the transmission power information of the system, so that the optimization iteration can be converged more quickly, and the efficiency of the optimization iteration is improved.

Specifically, the weighted mean square error function is determined based on the signal mean square error function and the weighting coefficient of each receiving end device in the system, for example, the signal mean square error function of each receiving end device in the system can be weighted according to the weighting coefficient of each receiving end device to obtain the weighted mean square error function.

The signal mean square error function of any one receiving end device refers to a function that can be used to characterize the mean square error between a transmitted signal detected by the receiving end device and a signal transmitted by the transmitting end device. It should be understood by those skilled in the art that, as used herein, the transmission signal detected by the receiving end device is not the transmission signal detected by the receiving end device during actual information transmission, and that, as used herein, the signal transmitted by the transmitting end device is not the transmission signal transmitted by the transmitting end device during actual information transmission, and that, as used herein, the transmission signal detected by the receiving end device and the signal transmitted by the transmitting end device refer to the variables representing the corresponding physical meanings during theoretical derivation.

In some embodiments, the weighted mean square error function is:

For example, in determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix, the system throughput maximization problem can be described as the sum maximization problem of the system and the rate of all receiving end devices in the system, namely:

where R _k denotes the system and rate of the receiving end device k.

Since max _W、Φ∑_1≤k≤KR_k is non-convex and the reflected phase shift θ _n and the reflected amplitude β _n are correlated, this problem is difficult to solve with existing convex optimization methods. To solve the difficulties presented by the sigma log () function and the polynomial term in the objective function, the problem can be converted to a weighted mean square error minimization problem. Specifically, a weighting parameter ρ _k may be introduced to the system and rate function of each receiver device, and an auxiliary variable u _k may be introduced to the received signal of each receiver device to convert the system and rate maximization problem to a weighted mean square error minimization problem. By taking the weighted mean square error function as the objective function, the solving difficulty caused by the sigma log () function and the polynomial term in the original system throughput objective function can be reduced, so that the complexity of solving the problem is reduced.

Specifically, when determining the optimal emission precoding matrix and the optimal RIS reflection coefficient matrix, the complexity of solving can be reduced by alternately optimizing the solving algorithm, namely, the multi-variable optimization is converted into single-variable optimization, and when one variable is optimized each time, other variables are used as constants. Typical alternate optimization algorithms are, for example, block-coordinate descent.

In the embodiment of the application, the optimal transmitting precoding matrix and the optimal RIS reflection coefficient matrix can be obtained through multiple iterations. In any iteration, for a certain target optimization variable, the optimal solution of other optimization variables except the target optimization variable in the previous iteration can be used as the value of the other optimization variables when the target optimization variable is optimized in the current iteration, so that the optimal solution of the target optimization variable in the current iteration is obtained.

For example, assuming that the optimization variables are the weighting parameter ρ, the auxiliary variable u, the RIS reflection coefficient matrix Φ and the transmission precoding matrix W, in any one iteration, the optimal solutions of the transmission precoding matrix W, RIS reflection coefficient matrix Φ and the auxiliary variable u obtained in the previous iteration may be input, in optimizing the auxiliary variable u, the optimal solutions of the transmission precoding matrix W, RIS reflection coefficient matrix Φ and the weighting parameter ρ obtained in the previous iteration may be input, in optimizing the RIS reflection coefficient matrix Φ, the optimal solutions of the weighting parameter ρ, the auxiliary variable u and the transmission precoding matrix W obtained in the previous iteration may be input, and in optimizing the transmission precoding matrix W, the optimal solutions of the weighting parameter ρ, the auxiliary variable u and the RIS reflection coefficient matrix Φ obtained in the previous iteration may be input.

After multiple iterations, under the condition that convergence conditions are reached, the optimal solution obtained by the transmitting precoding matrix and the RIS reflection coefficient matrix in the last iteration can be used as an optimal transmitting precoding matrix and an optimal RIS reflection coefficient matrix. The convergence condition may be that the set number of iterations is reached, or that the optimal solution difference between two iterations of the same optimization variable is smaller than a set threshold, and the like, which is not limited herein.

It should be noted that, for the first iteration, when solving a certain target optimization variable, the values of other optimization variables are all initial values of the optimization iteration. For the two optimization variables of the transmission precoding matrix and the RIS reflection coefficient matrix, the initial values of the optimization iteration are the initial transmission precoding matrix and the initial RIS reflection coefficient matrix, and for other optimization variables, the initial values can be given in the conventional beam forming algorithm, for example, the initial values can be given by randomly generated numerical values, or the initial values are all set to be 1, and the like, so that the application is not particularly limited.

By using an alternate optimization algorithm, the optimal transmitting precoding matrix and the optimal RIS reflection coefficient matrix are obtained through multiple iterations with the aim of minimizing the value of the weighted mean square error function, the complexity of solving can be effectively reduced, and the efficiency of optimizing the iterations is remarkably improved.

Specifically, ρ is a set of weighting parameters of all receiving end devices, and for any one of the weighting parameters ρ _k of the receiving end device k, the weighted mean square error function is described aboveIn the case where W, Φ, u are constants, the optimal solution for ρ _k in any one iteration can be determined by an explicit analytical expression. The specific derivation process may be referred to in example 2 below, and will not be described in detail herein.

For example, for the 3 rd iteration,(I.e., the inverse of MSE _k), where the elements in W, Φ, u used in the MSE _k computation are all elements in the optimal solution for W, Φ, u obtained in iteration 2.

The optimal solution of rho _k is directly obtained through the analysis expression, so that the efficiency of optimization iteration can be remarkably improved.

Specifically, u is a set of auxiliary variables of all receiving end devices, and for any one of the auxiliary variables u _k of receiving end device k, the weighted mean square error function is described aboveIn the case where W, Φ, ρ are constant, the optimal solution of u _k in any one iteration can be determined by an explicit analytical expression. The specific derivation process may be referred to in example 2 below, and will not be described in detail herein.

For example, for the 4 th iteration,Wherein the method comprises the steps ofThe elements in W and Φ used in the calculation are elements in the optimal solution of W and Φ obtained in the 3 rd iteration.

The optimal solution of u _k is directly obtained through the analysis expression, so that the efficiency of optimization iteration can be remarkably improved.

Specifically, for the precoding vector w _k corresponding to any one of the receiving end devices k, the weighted mean square error function is described as followsIn the case where Φ, u, ρ are constant, the optimal solution of w _k in any one iteration can be determined by a λ -dependent analytical expression. The specific derivation process may be referred to in example 2 below, and will not be described in detail herein.

For lambda to take value, it can be in the setThe one-dimensional search is performed, and the one-dimensional search method may be, for example, a dichotomy.

The optimal solution of w _k is directly obtained through the analysis expression, so that the efficiency of optimization iteration can be remarkably improved.

Specifically, the reflection coefficient for the reflection unit nAnd beta _n and theta _n satisfy a certain functional relation, so that the optimal solution of theta _n can be determined in each iterationCan reduce the solution to be obtainedThe complexity of the optimal solution of (2) improves the efficiency of optimization iteration.

For example, beta _n and theta _n satisfyThen the optimal solution for θ _n is determined in each iterationThen, the optimal solution of beta _n can be calculated according to the optimal solution of theta _n And then can obtainIs the optimal solution of (a) When the reflection coefficients of all the reflection units are optimized, the optimal RIS reflection coefficient matrix in the current iteration can be obtained

In particular, according to the weighted mean square error function described hereinbeforeAs can be found from theoretical derivation, under the condition that W, u and ρ are constants, it is still difficult to obtain an analytical expression of the optimal solution of θ _n, and the specific derivation process can be seen in example 2 below, which is not described herein.

Therefore, the present embodiment proposes a solution method based on a large number of simulation results, and the simulation finds that the function curve of the reflection coefficient optimization equivalent function g (θ _n) of the RIS reflection unit has four cases of "single wave peak", "single wave valley", "left peak right valley" and "left Gu Youfeng", and the optimal phase shift point only exists at the boundary point of the curve or the position of the curve wave valley point.

In some embodiments, the expression of g (θ _n) is:

v_k,j＝h_r,kdiag(Gw_j)

v_k,k＝h_r,kdiag(Gw_k)

Fig. 4 is an exemplary graph of g (θ _n) curves provided in the embodiment of the present application, as shown in fig. 4, where (a), (b), (c), and (d) in the graph respectively represent four cases of "single peak", "single trough", "left peak-to-right trough", and "left Gu Youfeng", where the optimal phase shift point of the "single peak" curve exists at the boundary point of the curve, and the optimal phase shift points of the three curves of "single trough", "left peak-to-right trough", and "left Gu Youfeng" are located at the positions of the trough point of the curve.

Therefore, the corresponding optimal phase shift point can be determined by determining the type of the curve of g (θ _n) first, and then according to which of the four cases "single peak", "single trough", "left peak-right trough" and "left Gu Youfeng" the curve of g (θ _n) belongs to.

For example, if the curve of g (θ _n) is a single-peak curve, then it can be determined that the optimal solution for θ _n is-pi or pi.

For example, if the curve of g (θ _n) is any one of the curves of single wave Gu Quxian, left peak right Gu Quxian and left Gu Youfeng, it is necessary to determine the value of θ _n corresponding to the trough point of the curve, and then use the value of θ _n corresponding to the trough point as the optimal solution of θ _n. The θ _n value corresponding to the trough point of the curve can be determined in a variety of different ways, which is not specifically limited herein.

By determining the function curve of the reflection coefficient optimization equivalent function g (theta _n) of the RIS reflection unit and then determining the corresponding optimal phase shift point according to the curve of g (theta _n), the optimal solution of theta _n is obtained, the complexity of solving the optimal solution of theta _n can be greatly simplified, and the efficiency of optimization iteration is remarkably improved.

Specifically, in the case where the curve of g (θ _n) is any one of the curves of the single wave Gu Quxian, the left peak and right Gu Quxian and the left Gu Youfeng, the position of the trough point of the curve of g (θ _n) can be determined by a one-dimensional search method, and a rough search interval can be determined before the one-dimensional search is performed, so as to improve the search efficiency. The method of the one-dimensional search is not limited, and some existing one-dimensional search algorithms can be used, for example.

In some embodiments, a one-dimensional search of the locations of curved wave valleys may be performed using the golden section method. The golden section method comprises the following specific processes:

(1) Setting a probe point Wherein, θ _left and θ _right are the left boundary point and the right boundary point of the golden section search interval, respectively.

(2) When θ _right-θ_left > ε, the following operations are performed until the angle difference between θ _right and θ _left satisfies the precision ε (where ε may be set as needed):

if it is Then(Will be currentΘ _left as an update),(Will be currentAs an update), (Update based on update θ _left update)。

If it isThen(Will be currentΘ _right as an update),(Will be currentAs an update),0.382 X (θ _right-θ_left) (update based on updated θ _right))。

When the angle difference between θ _right and θ _left satisfies the precision ε, the intermediate point between θ _left and θ _right is taken as the position of the searched curve trough, namely

Specifically, as shown in fig. 4, for the single wave Gu Quxian, since the phase shift point with the smallest value of the g (θ _n) function exists at the trough, the phase shift points-pi and pi can be used as the left boundary point and the right boundary point of the search interval, that is, one-dimensional search is performed in the whole value range of the phase shift point, so that the accuracy of the search result is ensured.

Specifically, as shown in fig. 4, for the left peak right Gu Quxian, the position of the curve trough point exists at the trough on the right side, so the left boundary point and the right boundary point of the search interval can be determined stepwise from the right side of the curve, and the efficiency of determining the boundary point of the search interval can be improved.

For example, three search points may be determined firstAndWherein, Δθ _search1 is a preset angular interval, Δθ _search1 >0.

Then, compareAndIf it isThen indicate thatAndA trough point is arranged between the two, and at this time, the two can be connected with each otherAndRespectively as a left boundary point and a right boundary point of the search interval. If it isThen indicate thatAndThe curve between the two curves is monotonically increasing, and at the moment, delta theta _search1 can be updated, AndThe above comparison is then repeatedAndUntil after a certain update the process of (a) is satisfiedThen it is possible toAndRespectively as a left boundary point and a right boundary point of the search interval.

Wherein, for updates of Δθ _search1, m ₁ may be greater than or equal to 1 (e.g., m ₁ may be set to 2) to further improve the efficiency of determining boundary points of the search interval. The value of m ₁ can be flexibly set according to the requirement, and the method is not particularly limited.

Specifically, as shown in fig. 4, for the left Gu Youfeng curve, the position of the curve trough point exists at the trough of the left side, so that the left and right boundary points of the search interval can be determined stepwise from the left side of the curve, and thus the efficiency of determining the boundary points of the search interval can be improved.

For example, three search points may be determined firstAndWherein, Δθ _search2 is a preset angular interval, Δθ _search2 >0.

Then, compareAndIf it isThen indicate thatAndA trough point is arranged between the two, and at this time, the two can be connected with each otherAndRespectively as a left boundary point and a right boundary point of the search interval. If it isThen indicate thatAndThe curves between the two curves are monotonically decreasing, and at the moment, delta theta _search2 can be updated, AndThe above comparison is then repeatedAndUntil after a certain update the process of (a) is satisfiedThen it is possible toAndRespectively as a left boundary point and a right boundary point of the search interval.

Wherein, for updates of Δθ _search2, m ₂ may be greater than or equal to 1 (e.g., m ₂ may be set to 2) to further improve the efficiency of determining boundary points of the search interval. The value of m ₂ can be flexibly set according to the requirement, and the method is not particularly limited.

The method provided by each of the above embodiments of the present application is illustrated below by way of example of a specific application scenario.

Fig. 5 is an exemplary diagram of an RIS-based communication scenario provided in an embodiment of the present application, where in the exemplary scenario shown in fig. 5, a base station and a terminal perform information transmission through the RIS, and a multi-user Multiple-Input Single-Output (MISO) system is shown in the figure, where an RIS including N reflection units is deployed in the system to assist downlink communication transmission from the base station (including N _t antenna units) to K Single-antenna users (i.e., terminals, UEs). Both example 1 and example 2 below are illustrated taking the scenario shown in fig. 5 as an example.

Example 1 general implementation of information transmission method example.

1. The RIS reports the device information.

Step 1-1: the base station sends control signaling to instruct the RIS or the third party control device to report device information to the base station, as shown in fig. 2.

Step 1-2 RIS or third party control device reports the device information to the base station after receiving the control signaling of the base station, as shown in FIG. 2. The device information includes the number N of reflection units included in the RIS and the setting of the reflection coefficients of the N reflection units at that time.

2. And (5) designing a beam forming algorithm.

Step 2-1 the system and rate maximization problem is converted into a weighted mean square error minimization problem by introducing a weighting parameter ρ _k for the system and rate (sum-rate) function of each UE and an auxiliary variable u _k for the received signal of each UE. (see example 2 in particular)

Step 2-2, initializing an RIS reflection coefficient matrix phi and a transmitting precoding matrix W.

The reflection coefficient at the time of RIS reporting is taken as an initial RIS reflection coefficient, and an initial transmission precoding matrix W is determined according to the initial RIS reflection coefficient, the known system CSI and the required transmission power P.

Step 2-3, iteration optimization weighting parameter ρ _k, auxiliary variable u _k, RIS reflection coefficient matrix phi and emission precoding matrix W, stopping iteration when the system and the speed reach convergence. (for the optimization scheme of each parameter, see Step 2-3-1 to Step 2-3-4, the optimization order of each parameter is not strictly limited and may be arbitrary)

Step 2-3-1 the weighting parameters p _k for each UE are optimized. (see example 2 in particular)

The transmit precoding matrix W, RIS reflection coefficient matrix Φ and the auxiliary variable u _k for each UE are input.

Output of optimal value of weighting parameter for each UE

The optimization scheme is that the partial derivative of the objective function with respect to ρ _k is calculated as 0 according to the first-order optimality condition to obtain the optimal value

Step 2-3-2. Optimize the auxiliary variables u _k for each UE. (see example 2 in particular)

The transmit precoding matrix W, RIS reflection coefficient matrix Φ and the weighting parameters per UE ρ _k are input.

Output of optimal value of auxiliary variable per UE

The optimization scheme is that the partial derivative of the objective function with respect to u _k is calculated to be 0 according to the first-order optimality condition to obtain the optimal value

Step 2-3-3. The transmit precoding matrix W is optimized. (see example 2 in particular)

The weighting parameters ρ _k of each UE, the auxiliary variables u _k of each UE and the RIS reflection coefficient matrix Φ are inputted.

And outputting an optimal value W ^opt of the transmission precoding matrix.

Optimization scheme using Lagrangian multiplier to construct Lagrangian function for precoding weight w _k for kth UE, and then taking partial derivative of Lagrangian function for w _k as 0 to obtain optimal valueWhen the optimal value of each UE precoding weight is obtained, the optimal transmitting precoding matrix is obtained

Step 2-3-4, optimizing the RIS reflection coefficient matrix phi. (see example 2 in particular)

The weighting parameter ρ _k of each UE, the auxiliary variable u _k of each UE and the transmit precoding matrix W are inputted.

And outputting an optimal value phi ^opt of the RIS reflection coefficient matrix.

Setting the trial point of the phase shift of the nth reflecting unit, determining the approximate interval of the optimal phase shift value by comparing the value relation of the objective function values of the trial point, and searching in one dimension by golden section method to find the optimal phase shift value of the nth reflecting unitAnd calculate the optimal reflection coefficient of the nth unitBy using the method, the optimal phase shift value of each unit is iteratively optimized, the corresponding optimal reflection coefficient is calculated, and when the system and the speed reach convergence, the iteration is stopped. The optimal value of the RIS reflection coefficient matrix at this time is

3. And transmitting the configuration of the precoding matrix and the RIS reflection coefficient matrix.

Step 3-1, W ^opt obtained by final iteration is used as a transmitting precoding matrix of the base station.

Step 3-2 the base station sends control signaling to the RIS or third party control device to adjust the reflection coefficient of the RIS as shown in fig. 3. Specifically, the final iteration reflection coefficient matrix Φ ^opt is used as the setting of the RIS reflection coefficient.

Step 3-3, information transmission is carried out.

Table 1 below is a summary of the implementation of example 1.

Table 1 summary of the implementation of example 1

Example 2 specific implementation of beamforming algorithm design.

1. And (5) a system model.

The transmitted signal x of the base station can be expressed as:

x=WS (1)

The data signals transmitted by the base station to K UEs may be represented as s= [ S ₁,…,s_k…,s_K]^T∈C^K×1. W is the transmit precoding matrix of the base station, denoted as

The transmission signal y _k of the base station received by the kth UE may be expressed as:

y_k＝(h_d,k+h_r,kdiag(Φ)G)x+n_k (2)

Wherein h _d,k is the direct view channel from the base station to the kth UE, H _r,k is the RIS to kth UE channel, h _r,k∈C^1×N, G is the base station to RIS channel,N _k is Gaussian white noise and n _k～CN(0,σ²), Φ is the reflection coefficient matrix of all reflection units of RIS, which isDimension is Nx1, whereRepresenting the transformation of the reflection coefficient matrix of the RIS into a diagonal matrix, the diag (Φ) is then represented as:

Wherein, theta _n represents the reflection phase shift of the nth reflection unit, and the value is [ -pi, pi ]. Beta _n represents the reflection amplitude of the nth reflection unit, and the value is [0,1]. According to the existing actual RIS unit model, the reflection amplitude beta _n and the reflection phase shift theta _n of the nth RIS unit are set to meet the following relation:

Wherein, beta _min is more than or equal to 0, And l.gtoreq.0 are constants associated with a particular circuit implementation. Beta _min is the minimum reflection amplitude of the reflection unit,Is the relative phase shift difference (radian value) from-pi/2 to beta _min corresponding to the phase shift value, l controls the steepness of the phase shift-reflection amplitude curve.

Substituting equation (1) into equation (2), the transmission signal of the base station received by the kth UE may be expressed again as:

the system and rate R _k for the kth user can be expressed as:

Where σ ² denotes the noise power.

2. Description of the problem.

The aim of the algorithm is to maximize the sum of the system and the rate of all users in the system by designing the transmitting end precoding matrix W and the RIS reflection coefficient matrix phi. The problem can thus be described as:

max_W、Φ∑_1≤k≤KR_k(7a)

subject toθ_n∈[-π,π](7b)

Wherein P is the transmitting power of the base station.

Since the objective function (7 a) is non-convex and the reflected phase shift θ _n and the reflected amplitude β _n are correlated, it is difficult to solve this problem with existing convex optimization methods. In order to solve the difficulties presented by the sigma log () function and the polynomial term in the objective function (7 a), the system and rate maximization problem of the objective function (7 a) can be converted into a weighted mean square error minimization problem. The mean square error function MSE _k for the kth UE is first defined as:

Wherein, U _k C is an auxiliary variable, "×" represents taking the conjugate.

Expanding the formula (8) to obtain

By introducing the weighting parameter ρ _k, the objective function (7 a) can be equivalently converted into the following form:

Where u and ρ are the sets of parameters u _k and ρ _k, respectively.

Since the objective function (10) does not contain a polynomial term similar to that in equation (6) and its functional form is relatively simple, the objective function can be optimized using a conventional block coordinate descent method.

Finally, the optimization problem (7) can be re-expressed as:

subject to(7b)-(7d)(11b)

3. detailed description of beamforming algorithms.

① The weighting parameters ρ _k (Step 2-3-1 corresponding embodiment) for each UE are optimized.

In the case of the input transmit precoding matrix W, RIS reflection coefficient matrix Φ and the auxiliary variable u _k, the optimization problem with respect to the weighting parameter ρ _k of the kth user can be expressed as

min_ρkρ_kMSE_k-log₂ρ_k(12)

Since (12) is an unconstrained convex problem, its optimal solution for ρ _k is, according to the first order optimality condition:

② The auxiliary variable u _k (Step 2-3-2 corresponding embodiment) for each UE is optimized.

In the case of the input transmit precoding matrix W, RIS reflection coefficient matrix Φ and the weighting parameter ρ _k, the optimization problem for the auxiliary variable u _k of the kth user can be expressed as:

min_ukρ_kMSE_k(14)

Similarly, since (14) is an unconstrained convex optimization problem, its optimal solution for u _k is, according to the first order optimality condition:

③ The transmit precoding matrix W is optimized (Step 2-3-3 corresponds to the embodiment).

In the case of the input weighting parameters ρ _k, the auxiliary variables u _k and the RIS reflection coefficient matrix Φ, the optimization problem with respect to the transmit precoding matrix W can be expressed as

Omitting items in MSE _k that are not related to w _k, the optimization problem can be re-expressed as:

Order the The objective function (17 a) can be re-expressed as:

the objective function (18) is expanded and the order of the accumulated sums is adjusted, which can then be expressed as:

Since both the objective function (19) and constraint (17 b) are convex, the solution can be performed using the Lagrangian multiplier method. First, introducing a multiplier λ for the power constraint (17 b) can result in a lagrangian function:

Let the first order partial derivative of the lagrangian function (20) with respect to w _k equal to 0, resulting in an optimal solution with respect to w _k of:

To meet the power constraint, λ may be at the set Obtained by performing a one-dimensional search (for example, by using a dichotomy).

After optimizing the precoding weights of all the UE, obtaining the optimal transmitting precoding matrix

④ The RIS reflectance matrix Φ (Step 2-3-4 corresponding embodiment) is optimized.

In the case of inputting the precoding matrix W, the weighting parameters ρ _k and the auxiliary variables u _k, the optimization problem with respect to the RIS reflection coefficient matrix Φ can be expressed as:

subject to(7b)、(7c)(22b)

In order to represent the objective function (22 a) as a functional form with respect to the RIS reflection coefficient matrix Φ, (22 a) can be represented as:

Wherein, v_k,j＝h_r,kdiag(Gw_j),v_k,k＝h_r,kdiag(Gw_k)。

Then, the formula (23) is expanded, and the term irrelevant to phi is discarded, so that the following can be obtained:

Order the Then (24) can be reduced to:

Since the constraint 7 (c) on reflection coefficients is non-convex and it is difficult to jointly optimize the reflection coefficients of all cells to find an optimal solution for the objective function (25) under the constraint 7 (c), the optimization problem for the RIS reflection coefficient matrix Φ can be split into sub-problems optimized for each cell reflection coefficient. First, the resolution of equation (25) is required:

equation (26) is then expressed as an objective function for the nth RIS cell. Wherein, Can be expressed as:

In (27), since a=a ^H, a (n ₂,n)＝A(n,n₂)^*).

Thus, the objective function for the reflection coefficient of the nth RIS cell can be expressed as:

Order the And is also provided with The optimization problem for the reflection coefficient of the nth RIS cell can be finally expressed as:

subject to(7b)、(7c) (29b)

Wherein,

g(θ_n)＝2|X_n|β_ncos(∠X_n-θ_n)+A(n,n)β_n ²+C_n (30)

However, it is difficult to derive an optimal solution for the reflection coefficient of the nth RIS cell by further deriving the objective function (30). Finally, through a large number of simulations, the function curve of the objective function (30) has four conditions of single wave peak, single wave valley, left peak, right valley and left Gu Youfeng (see fig. 4), and the optimal phase shift point only exists at the position of the boundary point or the wave valley point. Therefore, a one-dimensional searching method can be adopted to search the optimal reflection phase shift point according to the characteristics of the objective function curve, so as to find the optimal reflection coefficient.

Summary of one-dimensional search method:

Step A, firstly determining four judgment points theta _n,1、θ_n,2、θ_n,3 and theta _n,4, wherein theta _n,1＝-π,θ_n,2＝-π+△θ_judge,θ_n,3＝π-△θ_judge,θ_n,4＝π,△θ_judge is a preset angle interval, and delta theta _judge is more than 0.

Step B, determining the graph of the objective function curve of the nth reflecting unit according to the magnitude relation of the objective function corresponding to the judging point.

If g (θ _n,2)＞g(θ_n,1) and g (θ _n,3)＞g(θ_n,4), this indicates that the objective function curve is "single peak" at this time.

If g (θ _n,2)＜g(θ_n,1) and g (θ _n,3)＜g(θ_n,4), this indicates that the objective function curve is "single trough" at this time.

If g (θ _n,2)＞g(θ_n,1) and g (θ _n,3)＜g(θ_n,4), this indicates that the objective function curve is "left peak right valley" at this time.

If g (θ _n,2)＜g(θ_n,1) and g (θ _n,3)＞g(θ_n,4), this indicates that the objective function curve is "left Gu Youfeng" at this time.

Step C, after the graph of the objective function curve is determined, determining the position of the optimal phase shift point according to the condition of the graph.

For the "single-peak" case, the phase shift point at which the objective function (29 a) is smallest is the position of the boundary point, i.eOr pi.

For the case of "single trough", since the phase shift point at which the objective function (29 a) is smallest must exist at the trough, the phase shift points-pi and pi can be directly used as the left boundary point and the right boundary point of the search interval, and then the golden section method is adopted to perform one-dimensional search of the optimal phase shift point.

In the case of "left peak right valley", since the phase shift point at which the objective function (29 a) is smallest must exist at the right valley, it is necessary to determine the approximate section of the right valley (also the search section of the golden section method) first, and then perform one-dimensional search for the optimum phase shift point using the golden section method.

The specific method comprises the following steps:

Step C-1 determining three search points AndWherein, Δθ _search1 is a preset angular interval, Δθ _search1 >0.

Step C-2 whenAt the time of this, deltaθ _search1＝2*△θ_search1,

Until it meetsThe boundary point of the golden section search interval can be determined asAnd

Step C-3 byAndThe left boundary point and the right boundary point of the search interval are respectively used as the golden section method, and then the golden section method is used for carrying out one-dimensional search of the optimal phase shift point.

In the case of "left Gu Youfeng", since the minimum phase shift point of the objective function (29 a) must exist at the trough on the left side, it is necessary to determine the approximate section of the trough on the left side (also the search section of the golden section method) first, and then perform one-dimensional search of the optimum phase shift point using the golden section method.

The specific method comprises the following steps:

Step C-1 determining three search points Wherein, Δθ _search2 is a preset angular interval, Δθ _search2 >0.

Step C-2 whenAt the time of this, deltaθ _search2＝2*△θ_search2, Until it meetsThe boundary point of the golden section search interval can be determined asAnd

Finally, the reflection amplitude of the nth unit can be calculated through a formula (4)Thereby obtaining the reflection coefficient of the nth unit as

When the reflection coefficients of all the reflection units are optimized in an iterative manner, an optimal RIS reflection coefficient matrix can be obtained as

Example 3 verification of beamforming algorithm.

1. And (5) verifying algorithm convergence.

Fig. 6 is a graph of the relationship between the number of iterations and the convergence provided by the embodiment of the present application, where, as shown in fig. 6, the horizontal axis represents the number of iterations, and the vertical axis represents the average system and rate. Taking the setting of the transmitting power p=5dbw and the ris unit number n=64 as an example to verify the convergence of the beamforming algorithm proposed by the present application, it can be seen from the figure that the beamforming algorithm can reach convergence after about 60 iterations.

2. And (5) performance verification.

Fig. 7 is a graph comparing algorithm results under different parameter settings provided in the embodiment of the present application, where, as shown in fig. 7, the horizontal axis represents the transmit power, and the vertical axis represents the average system and rate. The validity of the proposed beamforming algorithm is verified by taking the example of setting the RIS element number n=64. The specific comparison scheme is as follows:

Line 1 (Proposed, β _min =1) the design of the beamforming algorithm is performed according to the scheme Proposed by the present application, wherein β _min =1 in the RIS cell model is set, i.e. the metal loss and ohmic loss inside the cell are 0, and the incident signal can realize total reflection on the cell.

Line 2 (Proposed, β _min =0.2) the design of the beamforming algorithm with the scheme Proposed in this patent, where the minimum reflection amplitude β _min =0.2 of the RIS cell model is set.

Line 3 (Random, < beta _min > = 0.2) the reflection phase shift of the RIS is randomly generated in the [ -pi, pi ] interval and the reflection coefficient of the RIS is calculated by equation (4) (β _min = 0.2 is set in the model), while the precoding matrix at the transmitting end is designed by the proposed scheme (see Step 2-3-3).

Line 4 (Ideal Assumption) assuming that the unit model of RIS is ideal (i.e., β _min =1) in beamforming algorithm optimization, but the actual model of RIS unit model is set to β _min =0.2 at the end of the calculation of system and rate.

As can be seen by looking at the comparison of the results in fig. 7:

(1) Assuming the RIS element as an ideal reflection element in an actual algorithm design, the effect of the algorithm design is affected (see "line 4"). While taking the actual model into account would be more beneficial for performance improvement (see "line 2").

(2) By reasonably designing the reflection coefficient of the RIS, the system and the speed can be improved maximally (see comparison of line 2 and line 3), and the beamforming algorithm design scheme provided by the application has a remarkable effect on the performance improvement of the system and the speed (see line 2).

(3) Losses generated inside the RIS cell affect the magnitude of the minimum reflection amplitude beta _min and thus the final performance. When the metal loss, ohmic loss, etc. inside the RIS cell are small, its minimum reflection amplitude β _min is large, thus having a significant effect on the system and rate improvement (see comparison of "line 1" and "line 2").

The method and the device provided by the embodiments of the present application are based on the same application conception, and because the principles of solving the problems by the method and the device are similar, the implementation of the device and the method can be referred to each other, and the repetition is not repeated.

Fig. 8 is a schematic structural diagram of a transmitting end device according to an embodiment of the present application, as shown in fig. 8, where the transmitting end device includes a memory 820, a transceiver 810, and a processor 800, where the processor 800 and the memory 820 may also be physically separated.

A memory 820 for storing a computer program, and a transceiver 810 for transceiving data under the control of the processor 800.

In particular, the transceiver 810 is used to receive and transmit data under the control of the processor 800.

Wherein in fig. 8, a bus architecture may comprise any number of interconnected buses and bridges, and in particular, one or more processors represented by processor 800 and various circuits of memory represented by memory 820, linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., all as are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. The transceiver 810 may be a number of elements, i.e., including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium, including wireless channels, wired channels, optical cables, etc.

The processor 800 is responsible for managing the bus architecture and general processing, and the memory 820 may store data used by the processor 800 in performing operations.

The processor 800 may be a central processing unit (Central Processing Unit, CPU), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA), or complex Programmable logic device (Complex Programmable Logic Device, CPLD), or the processor may employ a multi-core architecture.

Processor 800 executes any of the methods provided by the embodiments of the present application by invoking a computer program stored in memory 820, for example, to obtain device information of an intelligent super-surface RIS, channel state information CSI and transmit power information of a system, where the device information includes the number of reflection units included in the RIS and reflection coefficients of all reflection units included in the RIS, and to determine an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximize system throughput based on the device information, the CSI and the transmit power information, and to configure the optimal RIS reflection coefficient matrix to the RIS, where the constraint of a reflection phase shift of the RIS reflection unit, a constraint of a functional relationship between a reflection amplitude and a reflection phase shift of the RIS reflection unit, and a transmit power constraint are satisfied, and to use the optimal RIS reflection coefficient matrix to relay the information transmission in an auxiliary manner.

And receiving the device information sent by the RIS or the control device.

In some embodiments, the weighted mean square error function is:

In the formula, Representing the optimal solution of w _k in the current iteration, the ρ _j, the ρ _k, theAnd the diag (phi) are determined based on the optimal solution of the optimization variable of the previous iteration of the current iteration, I _Nt represents an N _t -order identity matrix, N _t represents the number of antenna units of the transmitting end device, the superscript H represents transposition, and lambda is based on the sumAnd P represents the transmitting power.

In some embodiments, the expression of g (θ _n) is:

g(θ_n)＝2|X_n|β_ncos(∠X_n-θ_n)+A(n,n)β_n ²+C_n

v_k,j＝h_r,kdiag(Gw_j)

v_k,k＝h_r,kdiag(Gw_k)

It should be noted that, the transmitting end device provided in the embodiment of the present application can implement all the method steps implemented in the method embodiment and achieve the same technical effects, and the same parts and beneficial effects as those of the method embodiment in the embodiment are not described in detail herein.

Fig. 9 is a schematic structural diagram of an information transmission device according to an embodiment of the present application, as shown in fig. 9, the device includes:

An obtaining unit 900, configured to obtain device information of an intelligent super-surface RIS, channel state information CSI and transmit power information of a system, where the device information includes the number of reflection units included in the RIS and reflection coefficients of all reflection units included in the RIS;

a determining unit 910, configured to determine an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximize a system throughput based on the device information, the CSI, and the transmit power information, in a case where a constraint condition of a reflection phase shift of the RIS reflecting unit, a constraint condition of a functional relation between a reflection amplitude of the RIS reflecting unit and the reflection phase shift, and a transmit power constraint condition are satisfied;

and the transmission unit 920 is configured to perform information transmission by using the optimal transmit precoding matrix, and configure the optimal RIS reflection coefficient matrix to the RIS, and perform auxiliary relay on the information transmission by using the optimal RIS reflection coefficient matrix by using the RIS.

And receiving the device information sent by the RIS or the control device.

In some embodiments, the weighted mean square error function is:

In some embodiments, the expression of g (θ _n) is:

g(θ_n)＝2|X_n|β_ncos(∠X_n-θ_n)+A(n,n)β_n ²+C_n

v_k,j＝h_r,kdiag(Gw_j)

v_k,k＝h_r,kdiag(Gw_k)

It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.

It should be noted that, the above device provided in the embodiment of the present application can implement all the method steps implemented in the method embodiment and achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those in the method embodiment in this embodiment are omitted.

In another aspect, embodiments of the present application also provide a non-transitory readable storage medium storing a computer program for causing a processor to execute the information transmission method provided in the above embodiments.

It should be noted that, the non-transitory readable storage medium provided by the embodiment of the present application can implement all the method steps implemented by the embodiment of the method and achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the embodiment of the method in the embodiment are omitted herein.

The non-transitory readable storage medium may be any available medium or data storage device that can be accessed by a computer, including, but not limited to, magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CD, DVD, BD, HVD, etc.), and semiconductor storage (e.g., ROM, EPROM, EEPROM, nonvolatile storage (NAND FLASH), solid State Disk (SSD)), etc.

The technical scheme provided by the embodiment of the application can be suitable for various systems, in particular to a 5G system. For example, applicable systems may be global system for mobile communications (global system of mobile communication, GSM), code division multiple access (code division multiple access, CDMA), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA) universal packet Radio service (GENERAL PACKET Radio service, GPRS), long term evolution (long term evolution, LTE), LTE frequency division duplex (frequency division duplex, FDD), LTE time division duplex (time division duplex, TDD), long term evolution-advanced (long term evolution advanced, LTE-a), universal mobile system (universal mobile telecommunication system, UMTS), worldwide interoperability for microwave access (worldwide interoperability for microwave access, wiMAX), 5G New air interface (New Radio, NR) systems, and the like. Terminal devices and network devices are included in these various systems. Core network parts such as evolved packet system (Evloved PACKET SYSTEM, EPS), 5G system (5 GS), etc. may also be included in the system.

The terminal according to the embodiment of the application can be a device for providing voice and/or data connectivity for a user, a handheld device with a wireless connection function, or other processing devices connected to a wireless modem, etc. The names of terminals may also be different in different systems, for example in a 5G system, a terminal may be referred to as User Equipment (UE). The wireless terminal device may communicate with one or more Core Networks (CNs) via a radio access Network (Radio Access Network, RAN), which may be mobile terminal devices such as mobile phones (or "cellular" phones) and computers with mobile terminal devices, e.g., portable, pocket, hand-held, computer-built-in or vehicle-mounted mobile devices that exchange voice and/or data with the radio access Network. Such as Personal communication services (Personal Communication Service, PCS) phones, cordless phones, session initiation protocol (Session Initiated Protocol, SIP) phones, wireless local loop (Wireless Local Loop, WLL) stations, personal digital assistants (Personal DIGITAL ASSISTANT, PDA) and the like. The wireless terminal device may also be referred to as a system, subscriber unit (subscriber unit), subscriber station (subscriber station), mobile station (mobile station), remote station (remote station), access point (access point), remote terminal device (remote terminal), access terminal device (ACCESS TERMINAL), user terminal device (user terminal), user agent (user agent), user equipment (user device), and embodiments of the present application are not limited.

The network device according to the embodiment of the present application may be a base station, where the base station may include a plurality of cells for providing services for the terminal. A base station may also be called an access point or may be a device in an access network that communicates over the air-interface, through one or more sectors, with wireless terminal devices, or other names, depending on the particular application. The network device may be configured to exchange received air frames with internet protocol (Internet Protocol, IP) packets as a router between the wireless terminal device and the rest of the access network, which may include an Internet Protocol (IP) communication network. The network device may also coordinate attribute management for the air interface. For example, the network device according to the embodiment of the present application may be a network device (Base Transceiver Station, BTS) in a global system for mobile communications (Global System for Mobile communications, GSM) or code division multiple access (Code Division Multiple Access, CDMA), a network device (NodeB) in a wideband code division multiple access (Wide-band Code Division Multiple Access, WCDMA), an evolved network device (evolutional Node B, eNB or e-NodeB) in a long term evolution (long term evolution, LTE) system, a 5G base station (gNB) in a 5G network architecture (next generation system), a home evolved base station (Home evolved Node B, heNB), a relay node (relay node), a home base station (femto), a pico base station (pico), etc., which are not limited in the embodiment of the present application. In some network structures, the network devices may include centralized unit (centralized unit, CU) nodes and Distributed Unit (DU) nodes, which may also be geographically separated.

Multiple-input Multiple-output (Multi Input Multi Output, MIMO) transmissions may each be made between the network device and the terminal using one or more antennas, and the MIMO transmissions may be Single User MIMO (SU-MIMO) or Multiple User MIMO (MU-MIMO). The MIMO transmission may be 2D-MIMO, 3D-MIMO, FD-MIMO, or massive-MIMO, or may be diversity transmission, precoding transmission, beamforming transmission, or the like, depending on the form and number of the root antenna combinations.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. An information transmission method, comprising:

Acquire device information of the intelligent metasurface RIS, channel state information CSI of the system, and transmit power information; wherein the device information includes the number of reflection units included in the RIS, and the reflection coefficients of all reflection units included in the RIS;

Under the condition that a reflection phase shift constraint of the RIS reflection unit, a constraint of a functional relationship between a reflection amplitude and a reflection phase shift of the RIS reflection unit, and a transmission power constraint are satisfied, determining an optimal transmission precoding matrix and an optimal RIS reflection coefficient matrix that maximize the system throughput based on the device information, the CSI, and the transmission power information;

The optimal transmit precoding matrix is used for information transmission, and the optimal RIS reflection coefficient matrix is configured to the RIS, so that the RIS uses the optimal RIS reflection coefficient matrix to perform auxiliary relaying for the information transmission.

2. The information transmission method according to claim 1, characterized in that the step of obtaining the device information of the intelligent super surface RIS comprises:

Sending a control signaling to the RIS or a control device of the RIS, where the control signaling is used to instruct the RIS or the control device to report device information of the RIS;

The device information sent by the RIS or the control device is received.

3. The information transmission method according to claim 1 or 2, characterized in that the determining, based on the device information, the CSI and the transmit power information, an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximize the system throughput comprises:

Determine an initial RIS reflection coefficient matrix based on the device information, and determine an initial transmit precoding matrix based on the initial RIS reflection coefficient matrix, the CSI and the transmit power information;

Based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix, an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximize system throughput are determined.

4. The information transmission method according to claim 3, characterized in that the determining, based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix, an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximize the system throughput comprises:

Determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix, and a weighted mean square error function;

The weighted mean square error function is determined based on the signal mean square error function and weighting coefficient of each receiving device in the system, and the signal mean square error function is used to characterize the mean square error between the transmitted signal detected by the receiving device and the signal transmitted by the transmitting device.

5. The information transmission method according to claim 4, characterized in that the determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix, and a weighted mean square error function, comprises:

Based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix, an alternating optimization algorithm is used to minimize the value of the weighted mean square error function, and the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix are obtained through multiple iterations;

In each iteration, for any target optimization variable of the weighted mean square error function, based on the optimal solutions of other optimization variables except the target optimization variable obtained in the previous iteration, the optimal solution of the target optimization variable in the current iteration is determined.

6. The information transmission method according to claim 5, characterized in that the weighted mean square error function is:

Where W represents the transmit precoding matrix, W = [w ₁ ,…,w _k …,w _K ], w _k represents the precoding vector corresponding to the receiving device k, k = 1, 2, …, K, K represents the total number of receiving devices in the system; Φ represents the RIS reflection coefficient matrix, represents the reflection coefficient of reflection unit n, n=1, 2, ..., N, N represents the number of reflection units included in the RIS; u and ρ are sets of _uk and ρ _k respectively, _uk represents the auxiliary variable of receiving device k, ρ _k represents the weighting parameter of receiving device k; MSE _k represents the mean square error function of the signal of receiving device k, represents the transmitted signal detected by the receiving device k, s _k represents the signal transmitted by the transmitting device, the superscript * represents conjugate, h _d,k represents the line-of-sight channel parameters from the transmitting device to the receiving device k, _hr,k represents the channel parameters from the RIS to the receiving device k, diag(Φ) represents a diagonal matrix whose diagonal elements are the elements in Φ, G represents the channel parameters from the transmitting device to the RIS, Re represents the real part, and σ ² represents the noise power.

7. The information transmission method according to claim 6, characterized in that, when the target optimization variable is ρ, determining the optimal solution of the target optimization variable in the current iteration comprises:

Based on the following formula, the optimal solution of ρ _k in the current iteration is determined:

In the formula, represents the optimal solution of ρ _k in the current iteration, and the MSE _k is determined based on the optimal solution of the optimization variable in the previous iteration of the current iteration.

8. The information transmission method according to claim 6, wherein, when the target optimization variable is u, determining the optimal solution of the target optimization variable in the current iteration comprises:

Based on the following formula, the optimal solution of u _k in the current iteration is determined:

In the formula, represents the optimal solution of _uk in the current iteration, and diag(Φ), _wk and _wj are all determined based on the optimal solution of the optimization variables in the previous iteration of the current iteration.

9. The information transmission method according to claim 6, wherein, when the target optimization variable is a transmit precoding matrix W, determining the optimal solution of the target optimization variable in the current iteration comprises:

Based on the following formula, the optimal solution of the precoding vector _wk corresponding to the receiving device k in the current iteration is determined:

In the formula, represents the optimal solution of w _k in the current iteration, the ρ _j , the ρ _k , the and diag(Φ) are determined based on the optimal solution of the optimization variable of the previous iteration of the current iteration, I _Nt represents the unit matrix of _Nt order, _Nt represents the number of antenna units of the transmitting end device, and the superscript H represents the transposition; λ is based on the set It is obtained by a one-dimensional search in , where P represents the transmission power.

10. The information transmission method according to claim 6, wherein, when the target optimization variable is a RIS reflection coefficient matrix Φ, determining the optimal solution of the target optimization variable in the current iteration comprises:

The reflection coefficient of reflection unit n in the current iteration is determined based on the following method: The optimal solution is:

Determine the optimal solution of the reflection phase shift θ _n of the reflection unit n in the current iteration;

Determine the reflection amplitude β n of the reflection unit n in the current iteration based on the optimal solution of the reflection phase shift θ _n of the reflection unit n in the current iteration and the constraint condition of the functional relationship between the reflection amplitude and the reflection phase shift of the _RIS reflection unit;

Based on the optimal solution of the reflection phase shift θ _n of the reflection unit n in the current iteration and the determined reflection amplitude β _n of the reflection unit n in the current iteration, the reflection coefficient of the reflection unit n in the current iteration is determined. The optimal solution of .

11. The information transmission method according to claim 10, characterized in that the determining the optimal solution of the reflection phase shift θ _n of the reflection unit n in the current iteration comprises:

Determine the curve type of the reflection coefficient optimization equivalent function g(θ _n ) of the RIS reflection unit in the current iteration;

In the case where the curve of g(θ _n ) is a single-peak curve, determining that the optimal solution of the reflection phase shift θ _n of the reflection unit n in the current iteration is -π or π;

When the curve of g(θ _n ) is any one of a single trough curve, a left-peak-right trough curve, and a left-trough-right-peak curve, determine the θ _n value corresponding to the trough point of the curve of g(θ _n ), and determine the θ _n value corresponding to the trough point of the curve of g(θ _n ) as the optimal solution of the reflection phase shift θ _n of the reflection unit n in the current iteration.

12. The information transmission method according to claim 11, characterized in that the curve type of determining the reflection coefficient optimization equivalent function g(θ _n ) of the RIS reflection unit in the current iteration comprises:

Determine four judgment points θ _n,1 , θ _n,2 , θ _n,3 and θ _n,4 ; wherein θ _n,1 = -π, θ _n,2 = -π+△θ _judge , θ _n,3 = π-△θ _judge , θ _n,4 = π, △θ _judge is a preset angle interval, △θ _judge >0;

In the case where g(θ _n,2 )>g(θ _n,1 ) and g(θ _n,3 )>g(θ _n,4 ), it is determined that the curve of g(θ _n ) is a single-peak curve;

In the case where g(θ _n,2 )＜g(θ _n,1 ) and g(θ _n,3 )＜g(θ _n,4 ), the curve of g(θ _n ) is determined to be a single trough curve;

In the case of g(θ _n,2 )＞g(θ _n,1 ) and g(θ _n,3 )＜g(θ _n,4 ), the curve of g(θ _n ) is determined to be a curve with a left peak and a right valley;

In the case that g(θ _n,2 )＜g(θ _n,1 ) and g(θ _n,3 )＞g(θ _n,4 ), the curve of g(θ _n ) is determined to be a curve with a left valley and a right peak.

13. The information transmission method according to claim 11, characterized in that the expression of g(θ _n ) is:

g(θ _n )＝2|X _n |β _n cos(∠X _n -θ _n )+A(n,n)β _n ² +C _n

v _k,j ＝ _hr,k diag(Gw _j )

v _k,k ＝h _r,k diag(Gw _k )

In the formula, A(n,n), A(n,n ₂ ), A(n ₁ ,n ₁ ), and A(n ₁ ,n ₂ ) respectively represent the element in the nth row and nth column of matrix A, the element in the nth row _{and nth} column of matrix A, the element in the _nth row and _nth column of matrix A, and the element in the _nth row and _nth column of matrix A; b(n) and b(n ₁ ) respectively represent the nth element in vector b and the _nth element in vector b; and ρ _k , uk _, w _j , and w _k are all determined based on the optimal solution of the optimization variables of the iteration before the current iteration.

14. The information transmission method according to any one of claims 11 to 13, characterized in that the step of determining the θ _n value corresponding to the valley point of the g(θ _n ) curve comprises:

Based on the curve type of g(θ _n ), determining the left boundary point and the right boundary point of the search interval;

Based on the determined left boundary point and right boundary point of the search interval, a one-dimensional search is performed in the search interval to determine the θ _n value corresponding to the valley point of the curve of the g(θ _n ).

15. The information transmission method according to claim 14, characterized in that the determining of the left boundary point and the right boundary point of the search interval based on the curve type of g(θ _n ) comprises:

When the curve of g(θ _n ) is a single-trough curve, -π and π are used as the left boundary point and the right boundary point of the search interval respectively.

16. The information transmission method according to claim 14, characterized in that the determining of the left boundary point and the right boundary point of the search interval based on the curve type of g(θ _n ) comprises:

In the case where the curve of g(θ _n ) is a curve with a left peak and a right valley, three search points are determined: and in, △θ _search1 is the preset angle interval, △θ _search1 >0;

exist In the case of and As the left boundary point and right boundary point of the search interval respectively;

exist In the case of , the value obtained by multiplying the current △θ _search1 by m ₁ is used as the updated △θ _search1 , and the current The value of The current The value of and update the Subtract the updated Δθ _search1 to obtain the updated value Based on the updated and the updated Compare and exist In the case of, repeatedly update △θ _search1 , and process until the updated and satisfy After the update and They are respectively used as the left boundary point and the right boundary point of the search interval; wherein, m ₁ is greater than or equal to 1.

17. The information transmission method according to claim 14, characterized in that the determining of the left boundary point and the right boundary point of the search interval based on the curve type of g(θ _n ) comprises:

In the case where the curve of g(θ _n ) is a curve with a left valley and a right peak, three search points are determined: and in, △θ _search2 is the preset angle interval, △θ _search2 >0;

exist In the case of , the value obtained by multiplying the current △θ _search2 by m ₂ is used as the updated △θ _search2 , and the current The value of The current The value of and update the Add the updated Δθ _search2 to obtain the updated value Based on the updated and the updated Compare and exist In the case of, repeatedly update △θ _search2 , and process until the updated and satisfy After the update and They are respectively used as the left boundary point and the right boundary point of the search interval; wherein, m ₂ is greater than or equal to 1.

18. The information transmission method according to claim 14, characterized in that the step of performing a one-dimensional search in the search interval based on the determined left boundary point and right boundary point of the search interval to determine the θ _n value corresponding to the trough point of the g(θ _n ) curve comprises:

Based on the determined left boundary point and right boundary point of the search interval, a one-dimensional search is performed in the search interval using the golden section method to determine the θ _n value corresponding to the trough point of the g(θ _n ) curve.

19. The information transmission method according to claim 1 or 10, characterized in that the constraint conditions of the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit include:

Wherein, β _n represents the reflection amplitude of reflection unit n, n=1, 2, ..., N, N represents the number of reflection units included in the RIS; θ _n represents the reflection phase shift of reflection unit n; β _min ≥0, and l≥0 are constants related to the specific circuit implementation, β _min represents the minimum reflection amplitude of the reflection unit, It represents the relative phase shift difference from -π/2 to β _min corresponding to the phase shift value. l is used to control the steepness of the relationship curve between the reflection phase shift and the reflection amplitude.

20. A transmitting end device, characterized in that it comprises a memory, a transceiver, and a processor;

A memory for storing a computer program; a transceiver for transmitting and receiving data under the control of the processor; and a processor for reading the computer program in the memory and executing the method according to any one of claims 1 to 19.

21. An information transmission device, comprising:

An acquisition unit, used to acquire device information of the intelligent metasurface RIS, channel state information CSI of the system, and transmit power information; wherein the device information includes the number of reflection units included in the RIS, and the reflection coefficients of all reflection units included in the RIS;

a determining unit, configured to determine, based on the device information, the CSI, and the transmit power information, an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix for maximizing system throughput, while satisfying a reflection phase shift constraint of the RIS reflection unit, a constraint of a functional relationship between a reflection amplitude and a reflection phase shift of the RIS reflection unit, and a transmit power constraint;

A transmission unit is used to use the optimal transmit precoding matrix to transmit information, and configure the optimal RIS reflection coefficient matrix to the RIS, so that the RIS uses the optimal RIS reflection coefficient matrix to assist in relaying the information transmission.

22. A non-transitory readable storage medium, characterized in that the non-transitory readable storage medium stores a computer program, wherein the computer program is used to enable a processor to execute the method according to any one of claims 1 to 19.