Information transmission method and device and storage medium

Abstract

The embodiment of the invention provides an information transmission method and device and a storage medium. The method comprises the following steps: acquiring equipment information of an intelligent metasurface RIS, channel state information CSI of a system and transmitting power information; 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; under the condition that the reflection phase shift constraint condition of the RIS reflection unit, the constraint condition of the function relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit and the transmission power constraint condition are met, determining an optimal transmission precoding matrix and an optimal RIS reflection coefficient matrix which maximize the throughput of the system based on the equipment information, the CSI and the transmission power information; and performing information transmission by adopting the optimal transmitting pre-coding matrix, and configuring the optimal RIS reflection coefficient matrix to the RIS, so that the RIS performs auxiliary relay on information transmission by adopting the optimal RIS reflection coefficient matrix.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to an information transmission method, device and storage medium. Background Art

[0002] In communication scenarios based on Reconfigurable Intelligent Surface (RIS), existing beamforming design algorithms all assume that the reflection amplitude of the RIS unit is constant at 1, that is, they assume that the RIS units are all ideal reflectors. However, in actual engineering applications, the incident signal cannot achieve full reflection on the RIS unit (that is, the reflection amplitude of the RIS unit is not constant at 1), which results in the beamforming parameters obtained by the existing beamforming design algorithm for information transmission, which cannot achieve maximum system throughput in actual engineering applications. Summary of the invention

[0003] In view of the problems existing in the prior art, the embodiments of the present application provide an information transmission method, device and storage medium.

[0004] In a first aspect, an embodiment of the present application provides an information transmission method, including:

[0005] 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;

[0006] 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;

[0007] 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.

[0008] In some embodiments, obtaining device information of the intelligent supersurface RIS includes:

[0009] 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;

[0010] The device information sent by the RIS or the control device is received.

[0011] In some embodiments, determining an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximizes system throughput based on the device information, the CSI, and the transmit power information includes:

[0012] 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;

[0013] 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.

[0014] In some embodiments, determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix that maximizes the system throughput based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix includes:

[0015] 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;

[0016] 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.

[0017] 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:

[0018] 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;

[0019] 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.

[0020] In some embodiments, the weighted mean square error function is:

[0021]

[0022]

[0023] Where W represents the transmit precoding matrix, W = [w1,…,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 u k and ρ k The collection of u k represents the auxiliary variable of the receiving device k, ρ k Represents the weighted parameter of the receiving device k; MSE k represents the mean square error function of the signal of the receiving device k, represents the transmitted signal detected by the receiving device k, s k represents the signal transmitted by the transmitting end device, and the superscript * represents conjugation; h d,k represents the direct line-of-sight channel parameter from the transmitting device to the receiving device k, h r,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, σ 2 Represents the noise power.

[0024] In some embodiments, when the target optimization variable is p, determining the optimal solution of the target optimization variable in the current iteration includes:

[0025] Based on the following formula, determine ρ in the current iteration k The optimal solution is:

[0026]

[0027] In the formula, Indicates that ρ in the current iteration k The optimal solution, the MSE k The optimal solution of the optimization variables of the previous iteration is determined based on the current iteration.

[0028] In some embodiments, when the target optimization variable is u, determining the optimal solution of the target optimization variable in the current iteration includes:

[0029] Based on the following formula, determine u in the current iteration k The optimal solution is:

[0030]

[0031] In the formula, Indicates that u in the current iteration k The optimal solution of diag(Φ), the w k and the w j All are determined based on the optimal solution of the optimization variables in the previous iteration of the current iteration.

[0032] In some embodiments, when the target optimization variable is a transmit precoding matrix W, determining the optimal solution of the target optimization variable in the current iteration includes:

[0033] Based on the following formula, the precoding vector w corresponding to the receiving device k in the current iteration is determined k The optimal solution is:

[0034]

[0035]

[0036] In the formula, Indicates that w in the current iteration k The optimal solution of j , the said k , and diag(Φ) are determined based on the optimal solution of the optimization variable of the previous iteration of the current iteration, N t The identity matrix of order, N t represents the number of antenna units of the transmitting end device, the superscript H represents transposition; λ is based on the set It is obtained by a one-dimensional search in , where P represents the transmission power.

[0037] In some embodiments, 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 includes:

[0038] The reflection coefficient of reflection unit n in the current iteration is determined based on the following method: The optimal solution is:

[0039] Determine the reflection phase shift θ of reflection unit n in the current iteration n The optimal solution of

[0040] Based on the reflection phase shift θ of the reflection unit n in the current iterationn The optimal solution of , and the constraint conditions of the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit, determine the reflection amplitude β of the reflection unit n in the current iteration n ;

[0041] Based on the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution of and the reflection amplitude β of the reflection unit n in the current iteration n , determine the reflection coefficient of reflection unit n in the current iteration The optimal solution of .

[0042] In some embodiments, the determination of the reflection phase shift θ of the reflection unit n in the current iteration is n The optimal solution includes:

[0043] Determine the reflection coefficient optimization equivalent function g(θ n )’s curve type;

[0044] In the g(θ n ) is a single peak curve, determine the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution is -π or π;

[0045] In the g(θ n ) is any one of a single trough curve, a left peak and right trough curve, and a left trough and right peak curve, determine the g(θ n ) corresponds to the trough point of the curve n value, the g(θ n ) corresponds to the trough point of the curve n The value is determined as the reflection phase shift θ of reflection unit n in the current iteration n The optimal solution of .

[0046] In some embodiments, the determination of the reflection coefficient optimization equivalent function g(θ n ) curve types, including:

[0047] Determine four judgment points θ n,1 ,θ n,2 ,θ n,3 and θ n,4 ; where θ n,1 =-π,θ n,2 =-π+△θ judge ,θ n,3 =π-△θ judge ,θ n,4 =π, △θ judge is the preset angle interval, △θ judge >0;

[0048] In g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), determine the g(θ n ) is a single peak curve;

[0049] In g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), determine the g(θ n ) is a single trough curve;

[0050] In g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), determine the g(θ n ) is a curve with a left peak and a right valley;

[0051] In g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), determine the g(θ n ) is a curve with a valley on the left and a peak on the right.

[0052] In some embodiments, the g(θ n ) is:

[0053] g(θ n )=2|X n |β n cos(∠X n -θ n )+A(n,n)β n 2 +C n

[0054]

[0055]

[0056]

[0057]

[0058] v k,j =h r,k diag(Gw j )

[0059] v k,k =h r,k diag(Gw k )

[0060] Where A(n,n), A(n,n2), A(n1,n1), and A(n1,n2) 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, respectively. b(n) and b(n1) represent the nth element in vector b and the n1th element in vector b, respectively. The ρ k 、the u k , the said j and the w k All are determined based on the optimal solution of the optimization variables in the previous iteration of the current iteration.

[0061] In some embodiments, the determining of g(θ n ) corresponds to the trough point of the curve n Values, including:

[0062] Based on the g(θ n ) to determine the left and right boundary points of the search interval;

[0063] 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 g(θ n ) corresponds to the trough point of the curve n value.

[0064] In some embodiments, the g(θ n ) to determine the left and right boundary points of the search interval, including:

[0065] In the g(θ n ) is a single trough curve, -π and π are used as the left and right boundary points of the search interval respectively.

[0066] In some embodiments, the g(θ n ) to determine the left and right boundary points of the search interval, including:

[0067] In the g(θ n ) is a curve with a left peak and a right valley, determine three search points and in, △θ search1 is the preset angle interval, △θsearch1 >0;

[0068] exist In the case of and As the left boundary point and right boundary point of the search interval respectively;

[0069] exist In the case of search1 Multiply the value obtained by m1 as the updated △θ search1 , the current The value of The current The value of and update the Subtract the updated Δθ search1 The obtained value is used as the updated Based on the updated and the updated Compare and exist In the case of 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, m1 is greater than or equal to 1.

[0070] In some embodiments, the g(θ n ) to determine the left and right boundary points of the search interval, including:

[0071] In the g(θ n ) is a curve with a left valley and a right peak, determine three search points and in, △θ search2 is the preset angle interval, △θ search2 >0;

[0072] exist In the case of and As the left boundary point and right boundary point of the search interval respectively;

[0073] exist In the case of search2Multiply the value obtained by m2 as the updated △θ search2 , the current The value of The current The value of and update the Add the updated Δθ search2 The obtained value is used as the updated Based on the updated and the updated Compare and exist In the case of 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, m2 is greater than or equal to 1.

[0074] In some embodiments, 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 g(θ n ) corresponds to the trough point of the curve n Values, including:

[0075] 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 g(θ n ) corresponds to the trough point of the curve n value.

[0076] In some embodiments, the constraint conditions of the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit include:

[0077]

[0078] In the formula, 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 Indicates the minimum reflection amplitude of the reflection unit, Represents from -π / 2 to β minThe relative phase shift difference of the corresponding phase shift value, l, is used to control the steepness of the relationship curve between the reflection phase shift and the reflection amplitude.

[0079] In a second aspect, an embodiment of the present application further provides a transmitting end device, including a memory, a transceiver, and a processor;

[0080] A memory for storing a computer program; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer program in the memory and executing the information transmission method described in the first aspect.

[0081] In a third aspect, an embodiment of the present application further provides an information transmission device, including:

[0082] 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;

[0083] 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;

[0084] A transmission unit is used to adopt the optimal transmit precoding matrix to transmit information, and configure the optimal RIS reflection coefficient matrix to the RIS, so that the RIS adopts the optimal RIS reflection coefficient matrix to assist in relaying the information transmission.

[0085] In a fourth aspect, an embodiment of the present application further provides a non-transitory readable storage medium, wherein the non-transitory readable storage medium stores a computer program, and the computer program is used to enable a processor to execute the information transmission method described in the first aspect as described above.

[0086] In a fifth aspect, an embodiment of the present application further provides a communication device, wherein a computer program is stored in the communication device, and the computer program is used to enable the communication device to execute the information transmission method described in the first aspect as described above.

[0087] In a sixth aspect, an embodiment of the present application further provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the information transmission method described in the first aspect as described above.

[0088] In a seventh aspect, an embodiment of the present application further provides a chip product, wherein a computer program is stored in the chip product, and the computer program is used to enable the chip product to execute the information transmission method described in the first aspect as described above.

[0089] The information transmission method, device and storage medium provided in the embodiments of the present application can maximize the system throughput in actual engineering applications by determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix that maximize the system throughput based on the situation where the RIS unit is a non-ideal reflection, and then transmitting information based on the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the following is a brief introduction to the drawings required for use in the embodiments or the related technical descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0091] Figure 1 A schematic diagram of a flow chart of an information transmission method provided in an embodiment of the present application;

[0092] Figure 2 An example diagram of RIS reporting device information provided in an embodiment of the present application;

[0093] Figure 3 An example diagram of a base station configuring an optimal RIS reflection coefficient matrix to a RIS provided in an embodiment of the present application;

[0094] Figure 4 g(θ provided in the embodiment of the present application n ) curve example diagram;

[0095] Figure 5 An example diagram of a communication scenario based on RIS provided in an embodiment of the present application;

[0096] Figure 6 A graph showing the relationship between the number of iterations and convergence provided in an embodiment of the present application;

[0097] Figure 7 A comparison chart of algorithm results under different parameter settings provided in the embodiments of the present application;

[0098] Figure 8 A schematic diagram of the structure of a transmitting end device provided in an embodiment of the present application;

[0099] Fig. 9 A schematic diagram of the structure of an information transmission device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0100] In the embodiments of the present application, the term "and / or" describes the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0101] In the embodiments of the present application, the term "plurality" refers to two or more than two, and other quantifiers are similar.

[0102] In the embodiments of the present application, the terms "first", "second", etc. are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first" and "second" are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more.

[0103] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0104] In the existing beamforming algorithm design, only the case where the RIS unit is an ideal reflector is considered, that is, it is assumed that the reflection amplitude of the RIS unit is constant at 1. However, in actual engineering, the reflection amplitude of the RIS unit is not constant at 1. Specifically, the change in the phase shift of the RIS unit will affect the current intensity inside the electromagnetic unit, thereby changing the dielectric loss, metal loss and ohmic loss of the electromagnetic unit, and ultimately changing the magnitude of the reflection amplitude of the electromagnetic unit. This results in the existing beamforming algorithm not being applicable to the actual RIS unit model. Therefore, in the design of the beamforming algorithm, considering the case where the RIS unit is a non-ideal reflector is more conducive to the evaluation of the algorithm performance and the implementation of actual engineering tests.

[0105] Figure 1 A flow chart of the information transmission method provided in the embodiment of the present application is as follows: Figure 1 As shown, the method comprises the following steps:

[0106] Step 100, obtaining device information of the intelligent metasurface RIS, channel state information CSI of the system, and transmission 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.

[0107] Specifically, the method can be applied to the scenario of RIS-assisted communication. The execution subject of the method can be any transmitting device that transmits information in the system, for example: network equipment (such as base stations), terminals (or user equipment, User Equipment, UE) or other communication equipment, etc. This application does not limit the specific equipment, as long as it is a transmitting device, the method can be executed.

[0108] The system described in this application refers to a communication system composed of a transmitting device, a RIS and a receiving device, such as downlink information transmission between a base station and multiple terminals, where the RIS serves as an auxiliary relay for information transmission, the base station is the transmitting device, and the terminal is the receiving device.

[0109] The embodiment of the present application determines the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix that maximize the system throughput based on the situation that the RIS unit is a non-ideal reflection, and then performs information transmission based on the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix, which can achieve the maximization of the system throughput (system capacity) in actual engineering applications.

[0110] Before determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix, the transmitting device needs to first obtain the device information of the RIS, the channel state information (CSI) of the system, and the transmit power information. Among them, the device information of the RIS includes the number of reflection units contained in the RIS, the reflection coefficient of each reflection unit contained in the RIS, and other information. The CSI of the system includes the channel state information of the link between the transmitting device and the RIS, the channel state information of the link between the RIS and the receiving device, and the channel state information of the direct line of sight channel between the transmitting device and the receiving device. The transmit power information includes the transmit power value of the transmitting device.

[0111] The acquisition of the system's CSI and transmit power information can be implemented by referring to existing technical solutions, which will not be described in detail here.

[0112] In some embodiments, obtaining device information of the intelligent supersurface RIS includes:

[0113] 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;

[0114] The device information sent by the RIS or the control device is received.

[0115] For example, assuming that the transmitting device is a base station, Figure 2 An example diagram of RIS reporting device information provided in the embodiment of the present application, such as Figure 2 As shown, the base station may send control signaling to the RIS or the control device of the RIS (the RIS may be controlled by a third-party control device), instructing the RIS or the control device of the RIS to report the device information of the RIS, thereby acquiring the device information of the RIS.

[0116] Step 101: Under the condition that the reflection phase shift constraint of the RIS reflection unit, the functional relationship constraint between the reflection amplitude and the reflection phase shift of the RIS reflection unit, and the transmission power constraint are satisfied, an optimal transmission precoding matrix and an optimal RIS reflection coefficient matrix that maximize the system throughput are determined based on the device information, the CSI, and the transmission power information.

[0117] Specifically, under non-ideal reflection conditions, the reflection amplitude of the RIS unit is not constant at 1, but has a certain functional relationship with the reflection phase shift. This functional relationship can be used as one of the constraints when determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix to reflect the actual RIS unit model.

[0118] In some embodiments, the constraint conditions of the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit include:

[0119]

[0120] In the formula, 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 Indicates the minimum reflection amplitude of the reflection unit, Represents from -π / 2 to β min The relative phase shift difference of the corresponding phase shift value, l, is used to control the steepness of the relationship curve between the reflection phase shift and the reflection amplitude.

[0121] For example, RIS includes N reflection units. For any reflection unit n, the reflection amplitude of the reflection unit is not constant at 1, but is a variable related to the reflection phase shift of the reflection unit. This can more accurately simulate the non-ideal reflection situation of RIS in actual engineering applications.

[0122] In some embodiments, the reflection phase shift constraint condition of the RIS reflection unit includes: the reflection phase shift value range of each RIS reflection unit is [-π, π].

[0123] In some embodiments, the transmission power constraint condition includes: the sum of the transmission powers allocated to all receiving-end devices is less than or equal to the transmission power of the transmitting-end device.

[0124] It should be noted that there may be multiple specific forms of maximizing system throughput in the present application, for example, it may be maximizing the sum of the achievable rates of all receiving devices in the system, or it may be maximizing the sum of the system sum rates of all receiving devices in the system.

[0125] Step 102: Use the optimal transmit precoding matrix to perform information transmission, and configure the optimal RIS reflection coefficient matrix to the RIS, so that the RIS uses the optimal RIS reflection coefficient matrix to perform auxiliary relaying for the information transmission.

[0126] Specifically, after the transmitting end device determines the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix, the optimal transmit precoding matrix can be used for information transmission, such as using the optimal transmit precoding matrix to precode the data stream to be sent and determine the transmit power of each antenna.

[0127] Furthermore, the transmitting end device may configure the optimal RIS reflection coefficient matrix to the RIS, for example, by sending 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, Figure 3 This is an example diagram of a base station configuring an optimal RIS reflection coefficient matrix to a RIS according to an embodiment of the present application, such as Figure 3 As shown, the base station can send control signaling to the RIS or the control device of the RIS (the RIS may be controlled by a third-party control device), and the control signaling includes the optimal RIS reflection coefficient matrix, so that the RIS can use the optimal RIS reflection coefficient matrix to set the transmission coefficient of each reflection unit in the RIS, thereby assisting in relaying information transmission and maximizing the system throughput.

[0128] The information transmission method provided in the embodiment of the present application can maximize the system throughput in actual engineering applications by determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix that maximize the system throughput based on the situation where the RIS unit is a non-ideal reflection, and then performing information transmission based on the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix.

[0129] In some embodiments, determining an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximizes system throughput based on the device information, the CSI, and the transmit power information includes:

[0130] 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;

[0131] 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.

[0132] Specifically, before determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix that maximize the system throughput, the transmitting end device may determine the initial RIS reflection coefficient matrix according to the reflection coefficients of each RIS reflection unit in the acquired RIS device information. For example, the RIS reflection coefficient matrix composed of the reflection coefficients of each RIS reflection unit in the RIS device information is used as the initial RIS reflection coefficient matrix.

[0133] Then, the transmitting end device can determine the initial transmit precoding matrix according to the initial RIS reflection coefficient matrix, the acquired system CSI and transmit power information. For example, the initial transmit precoding matrix can be designed by using traditional precoding methods such as Maximum Ratio Transmission (MRT), Minimum Mean Square Error (MMSE), Zero Forcing (ZF) precoding method, etc., which will not be described in detail here.

[0134] After obtaining the initial RIS reflection coefficient matrix and the initial transmit precoding matrix, the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix that maximize the system throughput can be obtained through optimization iteration based on these initial values.

[0135] By determining the initial RIS reflection coefficient matrix based on RIS device information, and reasonably setting the initial transmit precoding matrix based on the initial RIS reflection coefficient matrix, the acquired system CSI and transmit power information, the optimization iteration can converge faster, thereby improving the efficiency of the optimization iteration.

[0136] In some embodiments, determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix that maximizes the system throughput based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix includes:

[0137] 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;

[0138] 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.

[0139] Specifically, 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. For example, the signal mean square error function of each receiving device in the system can be weighted according to the weighting coefficient of each receiving device to obtain the weighted mean square error function.

[0140] The signal mean square error function of any receiving device refers to a function that can be used to characterize the mean square error between the transmitted signal detected by the receiving device and the signal transmitted by the transmitting device. Those skilled in the art should understand that the transmitted signal detected by the receiving device mentioned here is not the transmitted signal detected by the receiving device during actual information transmission, and the signal transmitted by the transmitting device mentioned here is not the signal transmitted by the transmitting device during actual information transmission. The transmitted signal detected by the receiving device and the signal transmitted by the transmitting device mentioned here both refer to variables that express corresponding physical meanings in the theoretical derivation process.

[0141] In some embodiments, the weighted mean square error function is:

[0142]

[0143]

[0144] Where W represents the transmit precoding matrix, W = [w1,…,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 u k and ρ k The collection of u k represents the auxiliary variable of the receiving device k, ρ k Represents the weighted parameter of the receiving device k; MSE k represents the mean square error function of the signal of the receiving device k, represents the transmitted signal detected by the receiving device k, s k represents the signal transmitted by the transmitting end device, and the superscript * represents conjugation; hd,k represents the direct line-of-sight channel parameter from the transmitting device to the receiving device k, h r,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, σ 2 Represents the noise power.

[0145] For example, when determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix, the system throughput maximization problem can be described as the problem of maximizing the sum of the system and rates of all receiving devices in the system, that is:

[0146]

[0147]

[0148] Among them, R k Represents the system and rate of the receiving device k.

[0149] Due to max W、Φ ∑ 1≤k≤K R k is non-convex, and the reflection phase shift is θ n and the reflection amplitude β n are related, so it is difficult to solve this problem with existing convex optimization methods. In order to solve the difficulties caused by the ∑log() function and the fractional terms in the objective function, the problem can be converted into a weighted mean square error minimization problem. Specifically, a weighting parameter ρ can be introduced for the system and rate function of each receiving device. k , and introduce an auxiliary variable u for the received signal of each receiving device k , to convert the system and rate maximization problem into a weighted mean square error minimization problem. By taking the weighted mean square error function as the objective function, the difficulty in solving the original system throughput objective function caused by the ∑log() function and fractional terms can be reduced, thereby reducing the complexity of problem solving.

[0150] 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:

[0151] 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;

[0152] 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.

[0153] Specifically, when determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix, the complexity of the solution can be reduced by using an alternating optimization algorithm, that is, converting multivariable optimization into single variable optimization, and each time a certain variable is optimized, other variables are taken as constants. Typical alternating optimization algorithms include block coordinate descent.

[0154] In the embodiment of the present application, the optimal transmit 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 other than the target optimization variable obtained in the previous iteration can be used as the value of the other optimization variables when optimizing the target optimization variable in this iteration, so as to obtain the optimal solution of the target optimization variable in this iteration.

[0155] For example, assuming that the optimization variables are weighting parameter ρ, auxiliary variable u, RIS reflection coefficient matrix Φ and transmit precoding matrix W, in any iteration, when optimizing the weighting parameter ρ, the optimal solution of transmit precoding matrix W, RIS reflection coefficient matrix Φ and auxiliary variable u obtained in the previous iteration can be input; when optimizing the auxiliary variable u, the optimal solution of transmit precoding matrix W, RIS reflection coefficient matrix Φ and weighting parameter ρ obtained in the previous iteration can be input; when optimizing the RIS reflection coefficient matrix Φ, the optimal solution of weighting parameter ρ, auxiliary variable u and transmit precoding matrix W obtained in the previous iteration can be input; when optimizing the transmit precoding matrix W, the optimal solution of weighting parameter ρ, auxiliary variable u and RIS reflection coefficient matrix Φ obtained in the previous iteration can be input.

[0156] After multiple iterations, when the convergence condition is met, the optimal solution of the transmit precoding matrix and the RIS reflection coefficient matrix obtained in the last iteration can be used as the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix. The convergence condition may be that a set number of iterations is reached, or that the difference in the optimal solution of the same optimization variable between two iterations is less than a set threshold, etc., which are not specifically limited here.

[0157] 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, the transmit precoding matrix and the RIS reflection coefficient matrix, the initial values ​​of the optimization iteration are the initial transmit precoding matrix and the initial RIS reflection coefficient matrix described above; for other optimization variables, the method of assigning initial values ​​in the traditional beamforming algorithm can be adopted, for example, the initial values ​​can be assigned with randomly generated values, or the initial values ​​are all set to all 1, etc., and this application does not make specific restrictions.

[0158] By using the alternating optimization algorithm and taking the minimization of the weighted mean square error function as the goal, the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix are obtained through multiple iterations, which can effectively reduce the complexity of the solution and significantly improve the efficiency of the optimization iteration.

[0159] In some embodiments, when the target optimization variable is p, determining the optimal solution of the target optimization variable in the current iteration includes:

[0160] Based on the following formula, determine ρ in the current iteration k The optimal solution is:

[0161]

[0162] In the formula, Indicates that ρ in the current iteration k The optimal solution, the MSE k The optimal solution of the optimization variables of the previous iteration is determined based on the current iteration.

[0163] Specifically, ρ is the set of weighted parameters of all receiving devices. For any weighted parameter ρ of a receiving device k, k , according to the weighted mean square error function mentioned above When W, Φ, and u are constants, ρ k The optimal solution in any iteration can be determined by a clear analytical expression. The specific derivation process can be found in Example 2 below and will not be repeated here.

[0164] For example, for the 3rd iteration, (i.e. MSE k The inverse of MSE k The elements of W, Φ, and u used in the calculation are all the elements of the optimal solution of W, Φ, and u obtained in the second iteration.

[0165] ρ is directly obtained by analyzing the expression k The optimal solution can significantly improve the efficiency of optimization iteration.

[0166] In some embodiments, when the target optimization variable is u, determining the optimal solution of the target optimization variable in the current iteration includes:

[0167] Based on the following formula, determine u in the current iteration k The optimal solution is:

[0168]

[0169] In the formula, Indicates that u in the current iteration k The optimal solution of diag(Φ), the w k and the w j All are determined based on the optimal solution of the optimization variables in the previous iteration of the current iteration.

[0170] Specifically, u is the set of auxiliary variables of all receiving devices. For any auxiliary variable u of a receiving device k, k , according to the weighted mean square error function mentioned above When W, Φ, and ρ are constants, u k The optimal solution in any iteration can be determined by a clear analytical expression. The specific derivation process can be found in Example 2 below and will not be repeated here.

[0171] For example, for the 4th iteration, in The elements of W and Φ used in the calculation are all the elements of the optimal solution of W and Φ obtained in the third iteration.

[0172] Get u directly by analyzing the expression k The optimal solution can significantly improve the efficiency of optimization iteration.

[0173] In some embodiments, when the target optimization variable is a transmit precoding matrix W, determining the optimal solution of the target optimization variable in the current iteration includes:

[0174] Based on the following formula, the precoding vector w corresponding to the receiving device k in the current iteration is determined k The optimal solution is:

[0175]

[0176]

[0177] In the formula, Indicates that w in the current iteration k The optimal solution of j , the said k , and diag(Φ) are determined based on the optimal solution of the optimization variable of the previous iteration of the current iteration, N t The identity matrix of order, N t represents the number of antenna units of the transmitting end device, the superscript H represents transposition; λ is based on the set It is obtained by a one-dimensional search in , where P represents the transmission power.

[0178] Specifically, for the precoding vector w corresponding to any receiving device k k , according to the weighted mean square error function mentioned above When Φ, u, and ρ are constants, w k The optimal solution in any iteration can be determined by an analytical expression related to λ. The specific derivation process can be found in Example 2 below and will not be repeated here.

[0179] For the value of λ, it can be in the set The one-dimensional search method may be, for example, binary search.

[0180] By analyzing the expression directly, w k The optimal solution can significantly improve the efficiency of optimization iteration.

[0181] In some embodiments, 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 includes:

[0182] The reflection coefficient of reflection unit n in the current iteration is determined based on the following method: The optimal solution is:

[0183] Determine the reflection phase shift θ of reflection unit n in the current iteration n The optimal solution of

[0184] Based on the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution of , and the constraint conditions of the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit, determine the reflection amplitude β of the reflection unit n in the current iteration n ;

[0185] Based on the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution of and the reflection amplitude β of the reflection unit n in the current iteration n , determine the reflection coefficient of reflection unit n in the current iteration The optimal solution of .

[0186] Specifically, for the reflection coefficient of reflection unit n And β n With θ n There is a certain functional relationship between them, so in each iteration, we can determine θ n The optimal solution to obtain The optimal solution of The complexity of the optimal solution can be reduced, and the efficiency of optimization iteration can be improved.

[0187] For example, β n With θ n Satisfaction between Then in each iteration, we determine θ n The optimal solution After that, we can use this θ n The optimal solution of β n The optimal solution Then we can get The optimal solution When the reflection coefficients of all reflection units are optimized, the optimal RIS reflection coefficient matrix in the current iteration can be obtained.

[0188] In some embodiments, the determination of the reflection phase shift θ of the reflection unit n in the current iteration is n The optimal solution includes:

[0189] Determine the reflection coefficient optimization equivalent function g(θ n ) curve type;

[0190] In the g(θ n ) is a single peak curve, determine the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution is -π or π;

[0191] In the g(θ n ) is any one of a single trough curve, a left peak and right trough curve, and a left trough and right peak curve, determine the g(θ n ) corresponds to the trough point of the curve n value, the g(θ n ) of the curve corresponding to the trough point θ n The value is determined as the reflection phase shift θ of reflection unit n in the current iteration n The optimal solution of .

[0192] Specifically, according to the weighted mean square error function mentioned above Through theoretical derivation, it can be found that when W, u, and ρ are constants, it is still difficult to obtain θ nThe analytical expression of the optimal solution of . The specific derivation process can be found in Example 2 below, which will not be repeated here.

[0193] Therefore, this embodiment proposes a solution method based on a large number of simulation results. The simulation finds that the reflection coefficient optimization equivalent function g(θ n ) has four cases: "single peak", "single valley", "left peak and right valley" and "left valley and right peak", and the optimal phase shift point only exists at the boundary point of the curve or the valley point of the curve.

[0194] In some embodiments, the g(θ n ) is:

[0195]

[0196]

[0197]

[0198]

[0199]

[0200] v k,j =h r,k diag(Gw j )

[0201] v k,k =h r,k diag(Gw k )

[0202] Where A(n,n), A(n,n2), A(n1,n1), and A(n1,n2) 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, respectively. b(n) and b(n1) represent the nth element in vector b and the n1th element in vector b, respectively. The ρ k 、the u k , the said j and the w k All are determined based on the optimal solution of the optimization variables in the previous iteration of the current iteration.

[0203] Figure 4 g(θ provided in the embodiment of the present application n ) curve example diagram, such as Figure 4As shown, (a), (b), (c), and (d) in the figure represent four cases: "single peak", "single valley", "left peak and right valley", and "left valley and right peak", respectively. The optimal phase shift point of the "single peak" curve exists at the boundary point of the curve, while the optimal phase shift points of the "single valley", "left peak and right valley" and "left valley and right peak" curves are located at the trough point of the curve.

[0204] Therefore, we can first determine g(θ n ) curve type, and then according to g(θ n ) belongs to one of the four cases of “single peak”, “single valley”, “left peak and right valley” and “left valley and right peak” to determine the corresponding optimal phase shift point.

[0205] For example, if g(θ n ) is a single peak curve, then we can determine θ n The optimal solution is -π or π.

[0206] For example, if g(θ n ) is a single trough curve, a left peak and right trough curve, or a left trough and right peak curve, then it is necessary to first determine the θ corresponding to the trough point of the curve. n value, and then the θ corresponding to the trough point n The value of θ n The optimal solution of the curve. n The value can be determined in many different ways, which are not specifically limited here.

[0207] In some embodiments, the determination of the reflection coefficient optimization equivalent function g(θ n ) curve types, including:

[0208] Determine four judgment points θ n,1 ,θ n,2 ,θ n,3 and θ n,4 ; where θ n,1 =-π,θ n,2 =-π+△θ judge ,θ n,3 =π-△θ judge ,θ n,4 =π, Δθ judge is the preset angle interval, △θ judge >0;

[0209] In g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), determine the g(θ n ) is a single peak curve;

[0210] In g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), determine the g(θ n ) is a single trough curve;

[0211] In g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), determine the g(θ n ) is a curve with a left peak and a right valley;

[0212] In g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), determine the g(θ n ) is a curve with a valley on the left and a peak on the right.

[0213] By determining the reflection coefficient of the RIS reflection unit, the equivalent function g(θ n ) function curve, and then according to g(θ n ) curve to determine the corresponding optimal phase shift point, thereby obtaining θ n The optimal solution of θ can greatly simplify the solution of n The complexity of the optimal solution is reduced, which significantly improves the efficiency of optimization iteration.

[0214] In some embodiments, the determining of g(θ n ) of the curve corresponding to the trough point θ n Values, including:

[0215] Based on the g(θ n ) to determine the left and right boundary points of the search interval;

[0216] 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 g(θ n ) of the curve corresponding to the trough point θ n value.

[0217] Specifically, in g(θ n ) is a single-trough curve, a left-peak-right-trough curve, or a left-trough-right-peak curve, g(θ n) curve valley point position, before performing one-dimensional search, a rough search interval can be determined to improve the efficiency of the search. The present application does not limit the specific method used for one-dimensional search, for example, some existing one-dimensional search algorithms can be used.

[0218] In some embodiments, the golden section method can be used to perform a one-dimensional search for the position of the curve valley point. The specific process of the golden section method is:

[0219] (1) Setting the trial point Among them, θ left and θ right They are the left and right boundary points of the golden section search interval respectively.

[0220] (2) When θ right -θ left >ε, perform the following operations until θ right With θ left The angle difference meets the accuracy ε (where ε can be set as needed):

[0221] if but (Change the current As the updated θ left ), (Change the current As an updated ), (Based on the updated θ left renew ).

[0222] if but (Change the current As the updated θ right ), (Change the current As an updated ), 0.382*(θ right -θ left )(Based on the updated θ right renew ).

[0223] When θ right With θ left When the angle difference meets the accuracy ε, θ left and θ right The middle point of is taken as the searched curve valley point position, that is,

[0224] In some embodiments, the g(θ n ) to determine the left and right boundary points of the search interval, including:

[0225] In the g(θ n ) is a single trough curve, -π and π are used as the left and right boundary points of the search interval respectively.

[0226] Specifically, Figure 4 As shown, for a single valley curve, since g(θ n )The phase shift point with the smallest function value exists at the trough, so the phase shift points -π and π can be used as the left and right boundary points of the search interval, that is, a one-dimensional search is performed within the entire value range of the phase shift point to ensure the accuracy of the search results.

[0227] In some embodiments, the g(θ n ) to determine the left and right boundary points of the search interval, including:

[0228] In the g(θ n ) is a curve with a left peak and a right valley, determine three search points and in, △θ search1 is the preset angle interval, △θ search1 >0;

[0229] exist In the case of and As the left boundary point and right boundary point of the search interval respectively;

[0230] exist In the case of search1 Multiply the value obtained by m1 as the updated △θ search1 , the current The value of The current The value of and update the Subtract the updated Δθ search1 The obtained value is used as the updated Based on the updated and the updated Compare and exist In the case of 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, m1 is greater than or equal to 1.

[0231] Specifically, Figure 4 As shown, for the left-peak-right-valley curve, 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 gradually determined starting from the right side of the curve, thereby improving the efficiency of determining the boundary points of the search interval.

[0232] For example, you can first determine three search points and in, △θ search1 is the preset angle interval, △θ search1 >0.

[0233] Then, compare and if It shows that and There is a valley point between and As the left and right boundary points of the search interval respectively. It shows that and The curve is monotonically increasing, and △θ can be updated at this time. search1 , and Then repeat the above comparison and The process continues until a certain update is satisfied. Then we can use and They are used as the left and right boundary points of the search interval respectively.

[0234] Among them, for △θ search1 m1 can be greater than or equal to 1 (for example, m1 can be set to 2) to further improve the efficiency of determining the boundary point of the search interval. The value of m1 can be flexibly set as needed and is not specifically limited.

[0235] In some embodiments, the g(θ n) to determine the left and right boundary points of the search interval, including:

[0236] In the g(θ n ) is a curve with a left valley and a right peak, determine three search points and in, △θ search2 is the preset angle interval, △θ search2 >0;

[0237] exist In the case of and As the left boundary point and right boundary point of the search interval respectively;

[0238] exist In the case of search2 Multiply the value obtained by m2 as the updated △θ search2 , the current The value of The current The value of and update the Add the updated Δθ search2 The obtained value is used as the updated Based on the updated and the updated Compare and exist In the case of 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, m2 is greater than or equal to 1.

[0239] Specifically, Figure 4 As shown, for the left-valley-right-peak curve, the position of the curve trough point exists at the trough on the left side, so the left and right boundary points of the search interval can be gradually determined starting from the left side of the curve, thereby improving the efficiency of determining the boundary points of the search interval.

[0240] For example, you can first determine three search points and in, △θ search2 is the preset angle interval, △θ search2 >0.

[0241] Then, compare and if It shows that and There is a valley point between and As the left and right boundary points of the search interval respectively. It shows that and The curve is monotonically decreasing, and △θ can be updated at this time. search2 , and Then repeat the above comparison and The process continues until a certain update is satisfied. Then we can use and They are used as the left and right boundary points of the search interval respectively.

[0242] Among them, for △θ search2 m2 can be greater than or equal to 1 (for example, m2 can be set to 2) to further improve the efficiency of determining the boundary point of the search interval. The value of m2 can be flexibly set as needed and is not specifically limited.

[0243] The following describes the methods provided in the above embodiments of the present application by using examples of specific application scenarios.

[0244] Figure 5 The example diagram of the communication scenario based on RIS provided in the embodiment of the present application is as follows: Figure 5 In the example scenario shown, the base station and the terminal transmit information through the RIS. The figure shows a multi-user multiple-input single-output (MISO) system, in which a RIS containing N reflection units is deployed in the system to assist the base station (containing N t The following examples 1 and 2 are based on Figure 5 The following scenario is used as an example for explanation.

[0245] Example 1: Example of the overall implementation process of the information transmission method.

[0246] 1. RIS reports device information.

[0247] Step 1-1: The base station sends control signaling to instruct the RIS or third-party control device to report device information to the base station, such as Figure 2 shown.

[0248] Step 1-2: After receiving the control signaling from the base station, the RIS or third-party control device reports the device information to the base station, such as Figure 2 The device information includes the number N of reflection units included in the RIS and the settings of the reflection coefficients of the N reflection units at this time.

[0249] 2. Beamforming algorithm design.

[0250] Step 2-1: Introduce a weighting parameter ρ into the system sum-rate function of each UE k , and introduce an auxiliary variable u for each UE's received signal k , to convert the system and rate maximization problem into a weighted mean square error minimization problem. (See Example 2 for details)

[0251] Step 2-2: Initialize the RIS reflection coefficient matrix Φ and the transmit precoding matrix W.

[0252] The reflection coefficient reported by RIS is used as the initial RIS reflection coefficient, and the initial transmit precoding matrix W is determined according to the initial RIS reflection coefficient, the known system CSI and the required transmit power P.

[0253] Step 2-3: Iteratively optimize the weighted parameter ρ k , auxiliary variable u k , RIS reflection coefficient matrix Φ and transmit precoding matrix W. When the system and rate reach convergence, stop the iteration. (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 can be arbitrary.)

[0254] Step 2-3-1: Optimize the weighted parameter ρ of each UE k (See Example 2 for details)

[0255] Input: transmit precoding matrix W, RIS reflection coefficient matrix Φ and auxiliary variables u for each UE k .

[0256] Output: The optimal value of each UE weight parameter

[0257] Optimization scheme: According to the first-order optimality condition, the objective function is k Find the partial derivative to be 0 to find the optimal value

[0258] Step 2-3-2: Optimize the auxiliary variable u of each UE k (See Example 2 for details)

[0259] Input: transmit precoding matrix W, RIS reflection coefficient matrix Φ and weighting parameter ρ for each UE k .

[0260] Output: The optimal value of each UE auxiliary variable

[0261] Optimization scheme: According to the first-order optimality condition, the objective function is k Find the partial derivative to be 0 to find the optimal value

[0262] Step 2-3-3: Optimize the transmit precoding matrix W. (See Example 2 for details)

[0263] Input: Weight parameter ρ for each UE k , auxiliary variable u for each UE k and the RIS reflection coefficient matrix Φ.

[0264] Output: The optimal value of the transmit precoding matrix W opt .

[0265] Optimization solution: Use the Lagrange multiplier method to construct the precoding weight w for the kth UE k The Lagrangian function of w k Find the partial derivative to be 0 to find the optimal value When the optimal value of each UE precoding weight is obtained, the optimal transmit precoding matrix is ​​obtained.

[0266] Step 2-3-4: Optimize the RIS reflection coefficient matrix Φ. (See Example 2 for details)

[0267] Input: Weight parameter ρ for each UE k , auxiliary variable u for each UE k And the transmit precoding matrix W.

[0268] Output: The optimal value of the RIS reflection coefficient matrix Φ opt .

[0269] Optimization scheme: Set the trial point of the phase shift of the nth reflection unit, determine the approximate interval of the optimal phase shift value by comparing the numerical relationship of the objective function value of the trial point, and then use the golden section method to perform a one-dimensional search to find the optimal phase shift value of the nth unit. And calculate the optimal reflection coefficient of the nth unit In this way, the optimal phase shift value of each unit is iteratively optimized and the corresponding optimal reflection coefficient is calculated. When the system and rate reach convergence, the iteration is stopped. The optimal value of the RIS reflection coefficient matrix at this time is

[0270]

[0271] 3. Configuration of the transmit precoding matrix and the RIS reflection coefficient matrix.

[0272] Step 3-1: Take W obtained from the final iteration opt As the transmit precoding matrix of the base station.

[0273] Step 3-2: The base station sends control signaling to the RIS or a third-party control device to adjust the reflection coefficient of the RIS, such as Figure 3 Specifically, the final iterative reflection coefficient matrix Φ opt As the setting of RIS reflection coefficient.

[0274] Step 3-3: Transmit information.

[0275] Table 1 below summarizes the implementation process of Example 1.

[0276] Table 1 Summary of the implementation process of Example 1

[0277]

[0278] Example 2: Specific implementation process of beamforming algorithm design.

[0279] 1. System model.

[0280] The base station's transmitted signal x can be expressed as:

[0281] x=WS (1)

[0282] The data signal transmitted by the base station to K UEs can be expressed as S = [s1,…,s k …,s K ] T ∈C K×1 W is the base station's transmit precoding matrix, expressed as

[0283]

[0284] The base station's transmitted signal y received by the kth UE k It can be expressed as:

[0285] y k =(h d,k +h r,k diag(Φ)G)x+nk (2)

[0286] Among them, h d,k is the line-of-sight channel from the base station to the kth UE, h r,k is the channel from RIS to the kth UE, h r,k ∈C 1×N ; G is the channel from the base station to RIS, n k is Gaussian white noise, and n k ~CN(0,σ 2 ), Φ is the reflection coefficient matrix of all reflection units of RIS, which is The dimension is N×1, where It means that the reflection coefficient matrix of RIS is transformed into a diagonal matrix, then diag(Φ) is expressed as:

[0287]

[0288] Among them, θ n Represents the reflection phase shift of the nth reflection unit, and its value is [-π,π]. n Represents the reflection amplitude of the nth reflection unit, and its value is [0,1]. According to the existing actual RIS unit model, the reflection amplitude β of the nth RIS unit is set n and the reflected phase shift θ n Satisfies the following relationship:

[0289]

[0290] Among them, β min ≥0, and l≥0 are constants related to the specific circuit implementation. min is the minimum reflection amplitude of the reflection unit, is from -π / 2 to β min The relative phase shift difference (radian value) of the corresponding phase shift value, l controls the steepness of the phase shift-reflection amplitude curve.

[0291] Substituting formula (1) into formula (2), the transmitted signal of the base station received by the kth UE can be re-expressed as:

[0292]

[0293] Then the system sum rate R of the kth user k It can be expressed as:

[0294]

[0295] In the formula, σ 2 Represents the noise power.

[0296] 2. Description of the problem.

[0297] The goal of this algorithm is to maximize the sum of the system and rate of all users in the system by designing the transmitter precoding matrix W and the RIS reflection coefficient matrix Φ. Therefore, the problem can be described as:

[0298] max W、Φ ∑ 1≤k≤K R k (7a)

[0299] subject toθ n ∈[-π,π](7b)

[0300]

[0301]

[0302] Wherein, P is the transmission power of the base station.

[0303] Since the objective function (7a) is non-convex and the reflection phase shift θ n and the reflection amplitude β n are related, so it is difficult to solve this problem with existing convex optimization methods. In order to solve the difficulties caused by the ∑log() function and the fractional terms in the objective function (7a), the system and rate maximization problem of the objective function (7a) can be converted into a weighted mean square error minimization problem. First, define the mean square error function MSE of the kth UE k for:

[0304]

[0305] in, u k ∈C is an auxiliary variable, and “*” represents conjugation.

[0306] Expanding formula (8) yields

[0307]

[0308] By introducing the weighting parameter ρ k , the objective function (7a) can be equivalently transformed into the following form:

[0309]

[0310] Among them, u and ρ are the parameters u k and ρ k A collection of .

[0311] Since the objective function (10) does not contain fractional terms similar to those in formula (6) and its functional form is relatively simple, the objective function can be optimized using the traditional block coordinate descent method.

[0312] Finally, the optimization problem (7) can be reformulated as:

[0313]

[0314] subject to(7b)-(7d)(11b)

[0315] 3. Detailed description of the beamforming algorithm.

[0316] ①Optimize the weighted parameter ρ of each UE k (Implementation example corresponding to Step 2-3-1).

[0317] At the input transmit precoding matrix W, RIS reflection coefficient matrix Φ and auxiliary variable u k In the case of k The optimization problem can be expressed as

[0318] min ρk ρ k MSE k -log2ρ k (12)

[0319] Since (12) is an unconstrained convex problem, according to the first-order optimality condition, its k The optimal solution is:

[0320]

[0321] ②Optimize the auxiliary variable u of each UE k (Implementation example corresponding to Step 2-3-2).

[0322] The input transmit precoding matrix W, RIS reflection coefficient matrix Φ and weighting parameter ρ k In the case of k-th user's auxiliary variable u k The optimization problem can be expressed as:

[0323] min uk ρ k MSE k (14)

[0324] Similarly, since (14) is an unconstrained convex optimization problem, according to the first-order optimality condition, its k The optimal solution is:

[0325]

[0326] ③ Optimize the transmit precoding matrix W (implementation corresponding to Step 2-3-3).

[0327] In the input weighting parameter ρ k , auxiliary variable u k and the RIS reflection coefficient matrix Φ, the optimization problem of the transmit precoding matrix W can be expressed as

[0328]

[0329]

[0330] Omit MSE k Zhong and w k The optimization problem can be reformulated as follows:

[0331]

[0332]

[0333] make Then the objective function (17a) can be reformulated as:

[0334]

[0335] By expanding the objective function (18) and adjusting the order of the cumulative sum, it can be expressed as:

[0336]

[0337] Since the objective function (19) and the constraint condition (17b) are both convex, the Lagrange multiplier method can be used to solve them. First, a multiplier λ is introduced for the power constraint condition (17b), and a Lagrangian function can be obtained:

[0338]

[0339] Let the Lagrangian function (20) be k The first-order partial derivative of is equal to 0, and we get k The optimal solution is:

[0340]

[0341] To satisfy the power constraint, λ can be in the set It can be obtained by performing a one-dimensional search (for example, using binary search) in .

[0342] After optimizing the precoding weights of all UEs, the optimal transmit precoding matrix is ​​obtained.

[0343]

[0344] ④Optimize the RIS reflection coefficient matrix Φ (implementation method corresponding to Step 2-3-4).

[0345] In the input precoding matrix W, weighting parameter ρ k and auxiliary variable u k In the case of , the optimization problem of the RIS reflection coefficient matrix Φ can be expressed as:

[0346]

[0347] subject to(7b),(7c)(22b)

[0348] In order to express the objective function (22a) as a function of the RIS reflection coefficient matrix Φ, (22a) can be expressed as:

[0349]

[0350] in, v k,j =h r,k diag(Gw j ), v k,k =h r,k diag(Gw k ).

[0351] Then, by expanding formula (23) and discarding the terms irrelevant to Φ, we can obtain:

[0352]

[0353] make Then (24) can be simplified as:

[0354]

[0355] Since the constraint 7(c) for the reflection coefficient is non-convex, and it is difficult to jointly optimize the reflection coefficients of all units to find the optimal solution to the objective function (25) under the constraint 7(c), the optimization problem of the RIS reflection coefficient matrix Φ can be split into sub-problems for optimizing the reflection coefficient of each unit. First, formula (25) needs to be split:

[0356]

[0357] Then, formula (26) is expressed as the objective function of the nth RIS unit. It can be expressed as:

[0358]

[0359] In (27), since A=A H , making A(n2,n)=A(n,n2) * .

[0360] Therefore, the objective function of the reflection coefficient of the nth RIS unit can be expressed as:

[0361]

[0362] make and Then the optimization problem of the reflection coefficient of the nth RIS unit can be finally expressed as:

[0363]

[0364] subject to(7b),(7c) (29b)

[0365] in,

[0366] g(θ n )=2|X n |β n cos(∠X n -θ n )+A(n,n)β n 2 +C n (30)

[0367] However, it is difficult to further deduce the objective function (30) to obtain an optimal solution for the reflection coefficient of the nth RIS unit. Finally, through a large number of simulations, it is found that the function curve of the objective function (30) has four situations: "single peak", "single valley", "left peak and right valley" and "left valley and right peak" (see Figure 4 ), and the optimal phase shift point only exists at the boundary point or the trough point. Therefore, a one-dimensional search method can be used to search for the optimal reflection phase shift point according to the characteristics of the objective function curve, so as to find the optimal reflection coefficient.

[0368] Overview of one-dimensional search method:

[0369] Step A: First determine the four judgment points θ n,1 ,θ n,2 ,θ n,3 and θ n,4 ; where θ n,1 =-π,θ n,2=-π+△θ judge ,θ n,3 =π-△θ judge ,θ n,4 =π, Δθ judge is the preset angle interval, △θ judge >0.

[0370] Step B: Then, according to the magnitude relationship between the judgment points and the objective functions, the objective function curve of the nth reflection unit is determined.

[0371] If g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), it indicates that the objective function curve is a “single peak”.

[0372] If g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), it indicates that the objective function curve is a “single valley”.

[0373] If g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), it means that the objective function curve is a "left peak and right valley" situation.

[0374] If g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), it means that the objective function curve is in the situation of “left valley and right peak”.

[0375] Step C: After determining the graph of the objective function curve, it is necessary to determine the position of the optimal phase shift point based on the graph.

[0376] For the case of a "single peak", the phase shift point that minimizes the objective function (29a) is the position of the boundary point, that is, or π.

[0377] For the case of a "single trough", since the phase shift point that minimizes the objective function (29a) must exist at the trough, the phase shift points -π and π can be directly used as the left and right boundary points of the search interval, and then the golden section method can be used to perform a one-dimensional search for the optimal phase shift point.

[0378] For the "left peak and right valley" case, since the phase shift point that minimizes the objective function (29a) must exist at the valley on the right, it is necessary to first determine the approximate interval of the valley on the right (which is also the search interval of the golden section method), and then use the golden section method to perform a one-dimensional search for the optimal phase shift point.

[0379] The specific method is as follows:

[0380] Step C-1: Determine three search points and in, △θ search1 is the preset angle interval, △θ search1 >0.

[0381] Step C-2: When When △θ search1 =2*△θ search1 ,

[0382] Until satisfied Then the boundary points of the golden section search interval can be determined as and

[0383] Step C-3: and They are respectively used as the left boundary point and the right boundary point of the golden section search interval, and then the golden section method is used to perform a one-dimensional search for the optimal phase shift point.

[0384] For the case of "left valley and right peak", since the phase shift point that minimizes the objective function (29a) must exist at the left valley, it is necessary to first determine the approximate interval of the left valley (which is also the search interval of the golden section method), and then use the golden section method to perform a one-dimensional search for the optimal phase shift point.

[0385] The specific method is as follows:

[0386] Step C-1: Determine three search points in, △θ search2 is the preset angle interval, △θ search2 >0.

[0387] Step C-2: When When △θ search2 =2*△θ search2 , Until satisfied Then the boundary points of the golden section search interval can be determined as and

[0388] Step C-3: and They are respectively used as the left boundary point and the right boundary point of the golden section search interval, and then the golden section method is used to perform a one-dimensional search for the optimal phase shift point.

[0389] Finally, the reflection amplitude of the nth unit can be calculated by formula (4): So the reflection coefficient of the nth unit can be obtained as

[0390] When the reflection coefficients of all reflection units are iteratively optimized, the optimal RIS reflection coefficient matrix can be obtained:

[0391] Example 3: Verification of the beamforming algorithm.

[0392] 1. Verification of algorithm convergence.

[0393] Figure 6 The relationship curve between the number of iterations and convergence provided in the embodiment of the present application is as follows: Figure 6 As shown, the horizontal axis is the number of iterations, and the vertical axis is the average system sum rate. The figure takes the setting of transmit power P = 5dBW and the number of RIS units N = 64 as an example to verify the convergence of the beamforming algorithm proposed in this application. It can be seen from the figure that the beamforming algorithm can reach convergence after about 60 iterations.

[0394] 2. Performance verification.

[0395] Figure 7 This is a comparison chart of algorithm results under different parameter settings provided in the embodiment of the present application, such as Figure 7 As shown, the horizontal axis is the transmit power and the vertical axis is the average system sum rate. The figure takes the setting of RIS unit number N=64 as an example to verify the effectiveness of the proposed beamforming algorithm. The specific comparison scheme is as follows:

[0396] Line 1 (Proposed, β min =1): The beamforming algorithm is designed using the scheme proposed in this application, wherein the β in the RIS unit model is set min =1, that is, the metal loss and ohmic loss inside the unit are 0, and the incident signal can be fully reflected on the unit.

[0397] Line 2 (Proposed, β min=0.2): The beamforming algorithm is designed according to the scheme proposed in this patent, in which the minimum reflection amplitude β of the RIS unit model is set min =0.2.

[0398] Line 3 (Random, β min =0.2): The reflection phase shift of RIS is randomly generated in the interval [-π, π), and the reflection coefficient of RIS is calculated by formula (4) (in this model, β is set to min =0.2), and the precoding matrix at the transmitting end is designed by the proposed scheme (see Step 2-3-3).

[0399] Line 4 (Ideal Assumption): In the optimization of the beamforming algorithm, it is assumed that the unit model of the RIS is ideal (i.e., β min =1), but the RIS unit model is set to β when calculating the system and rate in the end min = 0.2 for the actual model.

[0400] By observation Figure 7 The comparison of the results shows that:

[0401] (1) In actual algorithm design, assuming that the RIS unit is an ideal reflection unit will affect the effect of the algorithm design (see "line 4"). Taking the actual model into account will be more conducive to improving performance (see "line 2").

[0402] (2) By rationally designing the reflection coefficient of RIS, the system performance and rate can be maximized (see the comparison between “Line 2” and “Line 3”), and the beamforming algorithm design proposed in this application has a significant effect on improving the performance of the system and rate (see “Line 2”).

[0403] (3) The loss generated inside the RIS unit will affect the minimum reflection amplitude β min When the metal loss and ohmic loss inside the RIS unit are small, its minimum reflection amplitude β min It is relatively large, which has a significant effect on improving the system and rate (see the comparison between "Line 1" and "Line 2").

[0404] The methods and devices provided in the various embodiments of the present application are based on the same application concept. Since the methods and devices solve problems based on similar principles, the implementation of the devices and methods can refer to each other, and the repeated parts will not be repeated.

[0405] Figure 8 A schematic diagram of the structure of the transmitting end device provided in the embodiment of the present application, such as Figure 8As shown, the transmitting end device includes a memory 820, a transceiver 810 and a processor 800; wherein the processor 800 and the memory 820 may also be arranged physically separately.

[0406] The memory 820 is used to store computer programs; the transceiver 810 is used to send and receive data under the control of the processor 800.

[0407] Specifically, the transceiver 810 is used to receive and send data under the control of the processor 800 .

[0408] Among them, Figure 8 In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 800 and memory represented by memory 820. The bus architecture may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described in this application. The bus interface provides an interface. The transceiver 810 may be a plurality of components, namely, a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium, which may include a wireless channel, a wired channel, an optical cable, and other transmission media.

[0409] The processor 800 is responsible for managing the bus architecture and general processing, and the memory 820 can store data used by the processor 800 when performing operations.

[0410] The processor 800 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or a complex programmable logic device (CPLD). The processor may also adopt a multi-core architecture.

[0411] The processor 800 calls the computer program stored in the memory 820 to execute any of the methods provided in the embodiments of the present application according to the obtained executable instructions, for example: obtaining 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 the reflection phase shift constraint of the RIS reflection unit, the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit, and the transmit power constraint are satisfied, based on the device information, the CSI, and the transmit power information, determine the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix that maximize the system throughput; use the optimal transmit precoding matrix for information transmission, 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.

[0412] In some embodiments, obtaining device information of the intelligent supersurface RIS includes:

[0413] 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;

[0414] The device information sent by the RIS or the control device is received.

[0415] In some embodiments, determining an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximizes system throughput based on the device information, the CSI, and the transmit power information includes:

[0416] 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;

[0417] 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.

[0418] In some embodiments, determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix that maximizes the system throughput based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix includes:

[0419] 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;

[0420] 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.

[0421] 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:

[0422] 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;

[0423] 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.

[0424] In some embodiments, the weighted mean square error function is:

[0425]

[0426]

[0427] Where W represents the transmit precoding matrix, W = [w1,…,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 u k and ρ k The collection of u k represents the auxiliary variable of the receiving device k, ρ k Represents the weighted parameter of the receiving device k; MSE k represents the mean square error function of the signal of the receiving device k, represents the transmitted signal detected by the receiving device k, s k represents the signal transmitted by the transmitting end device, and the superscript * represents conjugation; h d,k represents the direct line-of-sight channel parameter from the transmitting device to the receiving device k, h r,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, σ 2 Represents the noise power.

[0428] In some embodiments, when the target optimization variable is p, determining the optimal solution of the target optimization variable in the current iteration includes:

[0429] Based on the following formula, determine ρ in the current iteration k The optimal solution is:

[0430]

[0431] In the formula, Indicates that ρ in the current iteration k The optimal solution, the MSE k The optimal solution of the optimization variables of the previous iteration is determined based on the current iteration.

[0432] In some embodiments, when the target optimization variable is u, determining the optimal solution of the target optimization variable in the current iteration includes:

[0433] Based on the following formula, determine u in the current iteration k The optimal solution is:

[0434]

[0435] In the formula, Indicates that u in the current iteration k The optimal solution of diag(Φ), the w k and the w j All are determined based on the optimal solution of the optimization variables in the previous iteration of the current iteration.

[0436] In some embodiments, when the target optimization variable is a transmit precoding matrix W, determining the optimal solution of the target optimization variable in the current iteration includes:

[0437] Based on the following formula, the precoding vector w corresponding to the receiving device k in the current iteration is determined k The optimal solution is:

[0438]

[0439]

[0440] In the formula, Indicates that w in the current iteration k The optimal solution of j , the said k , and diag(Φ) are determined based on the optimal solution of the optimization variable of the previous iteration of the current iteration, I Nt N t The identity matrix of order, N t represents the number of antenna units of the transmitting end device, the superscript H represents transposition; λ is based on the set It is obtained by a one-dimensional search in , where P represents the transmission power.

[0441] In some embodiments, 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 includes:

[0442] The reflection coefficient of reflection unit n in the current iteration is determined based on the following method: The optimal solution is:

[0443] Determine the reflection phase shift θ of reflection unit n in the current iteration n The optimal solution of

[0444] Based on the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution of , and the constraint conditions of the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit, determine the reflection amplitude β of the reflection unit n in the current iteration n ;

[0445] Based on the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution of and the reflection amplitude β of the reflection unit n in the current iteration n , determine the reflection coefficient of reflection unit n in the current iteration The optimal solution of .

[0446] In some embodiments, the determination of the reflection phase shift θ of the reflection unit n in the current iteration is n The optimal solution includes:

[0447] Determine the reflection coefficient optimization equivalent function g(θ n ) curve type;

[0448] In the g(θ n) is a single peak curve, determine the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution is -π or π;

[0449] In the g(θ n ) is any one of a single trough curve, a left peak and right trough curve, and a left trough and right peak curve, determine the g(θ n ) of the curve corresponding to the trough point θ n value, the g(θ n ) of the curve corresponding to the trough point θ n The value is determined as the reflection phase shift θ of reflection unit n in the current iteration n The optimal solution of .

[0450] In some embodiments, the determination of the reflection coefficient optimization equivalent function g(θ n ) curve types, including:

[0451] Determine four judgment points θ n,1 ,θ n,2 ,θ n,3 and θ n,4 ; where θ n,1 =-π,θ n,2 =-π+△θ judge ,θ n,3 =π-△θ judge ,θ n,4 =π, Δθ judge is the preset angle interval, △θ judge >0;

[0452] In g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), determine the g(θ n ) is a single peak curve;

[0453] In g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), determine the g(θ n ) is a single trough curve;

[0454] In g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), determine the g(θ n ) is a curve with a left peak and a right valley;

[0455] In g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), determine the g(θ n ) is a curve with a left valley and a right peak.

[0456] In some embodiments, the g(θ n ) is:

[0457] g(θ n )=2|X n |β n cos(∠X n -θ n )+A(n,n)β n 2 +C n

[0458]

[0459]

[0460]

[0461]

[0462] v k,j =h r,k diag(Gw j )

[0463] v k,k =h r,k diag(Gw k )

[0464] Where A(n,n), A(n,n2), A(n1,n1), and A(n1,n2) 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, respectively. b(n) and b(n1) represent the nth element in vector b and the n1th element in vector b, respectively. The ρ k 、the u k , the said j and the w k All are determined based on the optimal solution of the optimization variables in the previous iteration of the current iteration.

[0465] In some embodiments, the determining of g(θ n ) corresponds to the trough point of the curve n Values, including:

[0466] Based on the g(θ n ) to determine the left and right boundary points of the search interval;

[0467] 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 g(θ n ) corresponds to the trough point of the curve n value.

[0468] In some embodiments, the g(θ n ) to determine the left and right boundary points of the search interval, including:

[0469] In the g(θ n ) is a single trough curve, -π and π are used as the left and right boundary points of the search interval respectively.

[0470] In some embodiments, the g(θ n ) to determine the left and right boundary points of the search interval, including:

[0471] In the g(θ n ) is a curve with a left peak and a right valley, determine three search points and in, △θ search1 is the preset angle interval, △θ search1 >0;

[0472] exist In the case of and As the left boundary point and right boundary point of the search interval respectively;

[0473] exist In the case of search1 Multiply the value obtained by m1 as the updated △θ search1 , the current The value of The current The value of and update the Subtract the updated Δθ search1 The obtained value is used as the updated Based on the updated and the updated Compare and exist In the case of 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, m1 is greater than or equal to 1.

[0474] In some embodiments, the g(θ n ) to determine the left and right boundary points of the search interval, including:

[0475] In the g(θ n ) is a curve with a left valley and a right peak, determine three search points and in, △θ search2 is the preset angle interval, △θ search2 >0;

[0476] exist In the case of and As the left boundary point and right boundary point of the search interval respectively;

[0477] exist In the case of search2 Multiply the value obtained by m2 as the updated △θ search2 , the current The value of The current The value of and update the Add the updated Δθ search2 The obtained value is used as the updated Based on the updated and the updated Compare and exist In the case of 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, m2 is greater than or equal to 1.

[0478] In some embodiments, 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 g(θ n ) corresponds to the trough point of the curve n Values, including:

[0479] 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 g(θ n ) corresponds to the trough point of the curve n value.

[0480] In some embodiments, the constraint conditions of the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit include:

[0481]

[0482] In the formula, 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 Indicates the minimum reflection amplitude of the reflection unit, Represents from -π / 2 to β min The relative phase shift difference of the corresponding phase shift value, l, is used to control the steepness of the relationship curve between the reflection phase shift and the reflection amplitude.

[0483] It should be noted here that the above-mentioned transmitting end device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects that are the same as the method embodiment in this embodiment will not be described in detail here.

[0484] Fig. 9 A schematic diagram of the structure of the information transmission device provided in the embodiment of the present application is shown in FIG. Fig. 9 As shown, the device comprises:

[0485] An acquisition unit 900 is 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;

[0486] A determining unit 910 is configured to determine an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximize the system throughput based on the device information, the CSI, and the transmit power information, 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;

[0487] The transmission unit 920 is used to use the optimal transmit precoding matrix to perform information transmission, and configure the optimal RIS reflection coefficient matrix to the RIS, so that the RIS uses the optimal RIS reflection coefficient matrix to perform auxiliary relay for the information transmission.

[0488] In some embodiments, obtaining device information of the intelligent supersurface RIS includes:

[0489] 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;

[0490] The device information sent by the RIS or the control device is received.

[0491] In some embodiments, determining an optimal transmit precoding matrix and an optimal RIS reflection coefficient matrix that maximizes system throughput based on the device information, the CSI, and the transmit power information includes:

[0492] 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;

[0493] 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.

[0494] In some embodiments, determining the optimal transmit precoding matrix and the optimal RIS reflection coefficient matrix that maximizes the system throughput based on the initial RIS reflection coefficient matrix and the initial transmit precoding matrix includes:

[0495] 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;

[0496] 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.

[0497] 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:

[0498] 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;

[0499] 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.

[0500] In some embodiments, the weighted mean square error function is:

[0501]

[0502]

[0503] Where W represents the transmit precoding matrix, W = [w1,…,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 u k and ρ k The collection of u k represents the auxiliary variable of the receiving device k, ρ k Represents the weighted parameter of the receiving device k; MSE k represents the mean square error function of the signal of the receiving device k, represents the transmitted signal detected by the receiving device k, s k represents the signal transmitted by the transmitting end device, and the superscript * represents conjugation; h d,k represents the direct line-of-sight channel parameter 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, σ 2 Represents the noise power.

[0504] In some embodiments, when the target optimization variable is p, determining the optimal solution of the target optimization variable in the current iteration includes:

[0505] Based on the following formula, determine ρ in the current iteration k The optimal solution is:

[0506]

[0507] In the formula, Indicates that ρ in the current iteration k The optimal solution, the MSE k The optimal solution of the optimization variables of the previous iteration is determined based on the current iteration.

[0508] In some embodiments, when the target optimization variable is u, determining the optimal solution of the target optimization variable in the current iteration includes:

[0509] Based on the following formula, determine u in the current iteration k The optimal solution is:

[0510]

[0511] In the formula, Indicates that u in the current iteration k The optimal solution of diag(Φ), the w k and the w j All are determined based on the optimal solution of the optimization variables in the previous iteration of the current iteration.

[0512] In some embodiments, when the target optimization variable is a transmit precoding matrix W, determining the optimal solution of the target optimization variable in the current iteration includes:

[0513] Based on the following formula, the precoding vector w corresponding to the receiving device k in the current iteration is determined k The optimal solution is:

[0514]

[0515]

[0516] In the formula, Indicates that w in the current iterationk The optimal solution of j , the said k , and diag(Φ) are determined based on the optimal solution of the optimization variable of the previous iteration of the current iteration, N t The identity matrix of order, N t represents the number of antenna units of the transmitting end device, the superscript H represents transposition; λ is based on the set It is obtained by a one-dimensional search in , where P represents the transmission power.

[0517] In some embodiments, 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 includes:

[0518] The reflection coefficient of reflection unit n in the current iteration is determined based on the following method: The optimal solution is:

[0519] Determine the reflection phase shift θ of reflection unit n in the current iteration n The optimal solution of

[0520] Based on the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution of , and the constraint conditions of the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit, determine the reflection amplitude β of the reflection unit n in the current iteration n ;

[0521] Based on the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution of and the reflection amplitude β of the reflection unit n in the current iteration n , determine the reflection coefficient of reflection unit n in the current iteration The optimal solution of .

[0522] In some embodiments, the determination of the reflection phase shift θ of the reflection unit n in the current iteration is n The optimal solution includes:

[0523] Determine the reflection coefficient optimization equivalent function g(θ n )’s curve type;

[0524] In the g(θ n ) is a single peak curve, determine the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution is -π or π;

[0525] In the g(θ n) is any one of a single trough curve, a left peak and right trough curve, and a left trough and right peak curve, determine the g(θ n ) corresponds to the trough point of the curve n value, the g(θ n ) corresponds to the trough point of the curve n The value is determined as the reflection phase shift θ of reflection unit n in the current iteration n The optimal solution of .

[0526] In some embodiments, the determination of the reflection coefficient optimization equivalent function g(θ n ) curve types, including:

[0527] Determine four judgment points θ n,1 ,θ n,2 ,θ n,3 and θ n,4 ; where θ n,1 =-π,θ n,2 =-π+△θ judge ,θ n,3 =π-△θ judge ,θ n,4 =π, △θ judge is the preset angle interval, △θ judge >0;

[0528] In g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), determine the g(θ n ) is a single peak curve;

[0529] In g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), determine the g(θ n ) is a single trough curve;

[0530] In g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), determine the g(θ n ) is a curve with a left peak and a right valley;

[0531] In g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), determine the g(θn ) is a curve with a left valley and a right peak.

[0532] In some embodiments, the g(θ n ) is:

[0533] g(θ n )=2|X n |β n cos(∠X n -θ n )+A(n,n)β n 2 +C n

[0534]

[0535]

[0536]

[0537]

[0538] v k,j =h r,k diag(Gw j )

[0539] v k,k =h r,k diag(Gw k )

[0540] Where A(n,n), A(n,n2), A(n1,n1), and A(n1,n2) 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, respectively. b(n) and b(n1) represent the nth element in vector b and the n1th element in vector b, respectively. The ρ k 、the u k , the said j and the w k All are determined based on the optimal solution of the optimization variables in the previous iteration of the current iteration.

[0541] In some embodiments, the determining of g(θ n ) corresponds to the trough point of the curve n Values, including:

[0542] Based on the g(θ n ) to determine the left and right boundary points of the search interval;

[0543] 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 g(θ n ) of the curve corresponding to the trough point θ n value.

[0544] In some embodiments, the g(θ n ) to determine the left and right boundary points of the search interval, including:

[0545] In the g(θ n ) is a single trough curve, -π and π are used as the left and right boundary points of the search interval respectively.

[0546] In some embodiments, the g(θ n ) to determine the left and right boundary points of the search interval, including:

[0547] In the g(θ n ) is a curve with a left peak and a right valley, determine three search points and in, △θ search1 is the preset angle interval, △θ search1 >0;

[0548] exist In the case of and As the left boundary point and right boundary point of the search interval respectively;

[0549] exist In the case of search1 Multiply the value obtained by m1 as the updated △θ search1 , the current The value of The current The value of and update the Subtract the updated Δθ search1 The obtained value is used as the updated Based on the updated and the updated Compare and exist In the case of 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, m1 is greater than or equal to 1.

[0550] In some embodiments, the g(θ n ) to determine the left and right boundary points of the search interval, including:

[0551] In the g(θ n ) is a curve with a left valley and a right peak, determine three search points and in, △θ search2 is the preset angle interval, △θ search2 >0;

[0552] exist In the case of and As the left boundary point and right boundary point of the search interval respectively;

[0553] exist In the case of search2 Multiply the value obtained by m2 as the updated △θ search2 , the current The value of The current The value of and update the Add the updated Δθ search2 The obtained value is used as the updated Based on the updated and the updated Compare and exist In the case of 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, m2 is greater than or equal to 1.

[0554] In some embodiments, 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 g(θ n ) corresponds to the trough point of the curve n Values, including:

[0555] 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 g(θ n ) corresponds to the trough point of the curve n value.

[0556] In some embodiments, the constraint conditions of the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit include:

[0557]

[0558] In the formula, 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 Indicates the minimum reflection amplitude of the reflection unit, Represents from -π / 2 to β min The relative phase shift difference of the corresponding phase shift value, l, is used to control the steepness of the relationship curve between the reflection phase shift and the reflection amplitude.

[0559] It should be noted that the division of units in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0560] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0561] It should be noted here that the above-mentioned device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0562] On the other hand, an embodiment of the present application further provides a non-transitory readable storage medium, wherein the non-transitory readable storage medium stores a computer program, and the computer program is used to enable a processor to execute the information transmission method provided by the above embodiments.

[0563] It should be noted here that the non-transitory readable storage medium provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0564] The non-transitory readable storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

[0565] The technical solution provided in the embodiment of the present application can be applicable to a variety of systems, especially 5G systems. For example, the applicable system can be a global system of mobile communication (GSM) system, a code division multiple access (CDMA) system, a wideband code division multiple access (WCDMA) general packet radio service (GPRS) system, a long term evolution (LTE) system, a LTE frequency division duplex (FDD) system, a LTE time division duplex (TDD) system, an advanced long term evolution (LTE-A) system, a universal mobile telecommunication system (UMTS), a world-wide interoperability for microwave access (WiMAX) system, a 5G new air interface (NR) system, etc. These various systems include terminal equipment and network equipment. The system may also include a core network part, such as an evolved packet system (EPS), a 5G system (5GS), etc.

[0566] The terminal involved in the embodiment of the present application may be a device that provides voice and / or data connectivity to a user, a handheld device with a wireless connection function, or other processing devices connected to a wireless modem. In different systems, the name of the terminal may also be different. For example, in a 5G system, the terminal may be called a user equipment (UE). A wireless terminal device can communicate with one or more core networks (CN) via a radio access network (RAN). The wireless terminal device may be a mobile terminal device, such as a mobile phone (or a "cellular" phone) and a computer with a mobile terminal device. For example, it may be a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device that exchanges language and / or data with a wireless access network. For example, personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs) and other devices. The wireless terminal device may also be referred to as a system, a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, an access point, a remote terminal device, an access terminal device, a user terminal device, a user agent, and a user device, but is not limited in the embodiments of the present application.

[0567] The network device involved in the embodiment of the present application may be a base station, which may include multiple cells providing services for the terminal. Depending on the specific application scenario, the base station may also be called an access point, or may be a device in the access network that communicates with the wireless terminal device through one or more sectors on the air interface, or other names. The network device may be used to interchange received air frames with Internet Protocol (IP) packets, and serve as a router between the wireless terminal device and the rest of the access network, wherein the rest of the access network may include an Internet Protocol (IP) communication network. The network device may also coordinate the attribute management of the air interface. For example, the network device involved in the embodiments of the present application may be a network device (Base Transceiver Station, BTS) in the Global System for Mobile communications (Global System for Mobile communications, GSM) or Code Division Multiple Access (Code Division Multiple Access, CDMA), or a network device (NodeB) in Wide-band Code Division Multiple Access (WCDMA), or an evolutionary network device (evolutional Node B, eNB or e-NodeB) in the long term evolution (long term evolution, LTE) system, a 5G base station (gNB) in the 5G network architecture (next generation system), or a home evolved Node B (Home evolved Node B, HeNB), a relay node, a home base station (femto), a pico base station (pico), etc., which is not limited in the embodiments of the present application. In some network structures, the network device may include a centralized unit (CU) node and a distributed unit (DU) node, and the centralized unit and the distributed unit may also be arranged geographically separately.

[0568] Network devices and terminals can each use one or more antennas for multiple-input multiple-output (MIMO) transmission. MIMO transmission can be single-user MIMO (SU-MIMO) or multi-user MIMO (MU-MIMO). Depending on the form and number of antenna combinations, MIMO transmission can be 2D-MIMO, 3D-MIMO, FD-MIMO or massive-MIMO, or it can be diversity transmission, precoded transmission or beamforming transmission, etc.

[0569] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0570] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer executable instructions. These computer executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0571] These processor executable instructions may also be stored in a processor readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the processor readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0572] These processor-executable instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0573] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An information transmission method, characterized in that: include: 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 obtaining of device information of the intelligent super surface RIS includes: 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 for maximizing 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 for maximizing 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 = [w1,…,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 u k and ρ k The collection of u k represents the auxiliary variable of the receiving device k, ρ k Represents the weighted parameter of the receiving device k; MSE k represents the mean square error function of the signal of the receiving device k, represents the transmitted signal detected by the receiving device k, s k represents the signal transmitted by the transmitting end device, and the superscript * represents conjugation; h d,k represents the direct line-of-sight channel parameter from the transmitting device to the receiving device k, h r,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, 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 includes: Based on the following formula, determine ρ in the current iteration k The optimal solution is: In the formula, Indicates that ρ in the current iteration k The optimal solution, the MSE k The optimal solution of the optimization variables of the previous iteration is determined based on the current iteration.

8. The information transmission method according to claim 6, characterized in that: When the target optimization variable is u, determining the optimal solution of the target optimization variable in the current iteration includes: Based on the following formula, determine u in the current iteration k The optimal solution is: In the formula, Indicates that u in the current iteration k The optimal solution of diag(Φ), the w k and the w j All are 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, characterized in that: In a case where the target optimization variable is a transmit precoding matrix W, determining an optimal solution of the target optimization variable in the current iteration includes: Based on the following formula, the precoding vector w corresponding to the receiving device k in the current iteration is determined k The optimal solution is: In the formula, Indicates that w in the current iteration k The optimal solution of j , the said k , and diag(Φ) are determined based on the optimal solution of the optimization variable of the previous iteration of the current iteration, I Nt N t The identity matrix of order, N t represents the number of antenna units of the transmitting end device, the superscript H represents 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, characterized in that: In the case where the target optimization variable is the RIS reflection coefficient matrix Φ, determining the optimal solution of the target optimization variable in the current iteration includes: The reflection coefficient of reflection unit n in the current iteration is determined based on the following method: The optimal solution is: Determine the reflection phase shift θ of reflection unit n in the current iteration n The optimal solution of Based on the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution of , and the constraint conditions of the functional relationship between the reflection amplitude and the reflection phase shift of the RIS reflection unit, determine the reflection amplitude β of the reflection unit n in the current iteration n ; Based on the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution of and the reflection amplitude β of the reflection unit n in the current iteration n , determine the reflection coefficient of reflection unit n in the current iteration The optimal solution of .

11. The information transmission method according to claim 10, characterized in that: The determination of the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution includes: Determine the reflection coefficient optimization equivalent function g(θ n )’s curve type; In the g(θ n ) is a single peak curve, determine the reflection phase shift θ of the reflection unit n in the current iteration n The optimal solution is -π or π; In the g(θ n ) is any one of a single trough curve, a left peak and right trough curve, and a left trough and right peak curve, determine the g(θ n ) corresponds to the trough point of the curve n value, the g(θ n ) corresponds to the trough point of the curve n The value is determined as the reflection phase shift θ of reflection unit n in the current iteration n The optimal solution of .

12. The information transmission method according to claim 11, characterized in that: The determination of the reflection coefficient optimization equivalent function g(θ n ) curve types, including: Determine four judgment points θ n,1 ,θ n,2 ,θ n,3 and θ n,4 ; where θ n,1 =-π,θ n,2 =-π+△θ judge ,θ n,3 =π-△θ judge ,θ n,4 =π, Δθ judge is the preset angle interval, △θ judge >0; In g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), determine the g(θ n ) is a single peak curve; In g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), determine the g(θ n ) is a single trough curve; In g(θ n,2 )>g(θ n,1 ) and g(θ n,3 )<g(θ n,4 ), determine the g(θ n ) is a curve with a left peak and a right valley; In g(θ n,2 )<g(θ n,1 ) and g(θ n,3 )>g(θ n,4 ), determine the g(θ n ) is a curve with a left valley and a right peak.

13. The information transmission method according to claim 11, characterized in that: The g(θ n ) is: g(θ n )=2|X n |b n cos(∠X n -θ n )+A(n,n)β n 2 +C n v k,j =h r,k diag (See j ) v k,k =h r,k diag (See k ) Where A(n,n), A(n,n2), A(n1,n1), and A(n1,n2) 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, respectively. b(n) and b(n1) represent the nth element in vector b and the n1th element in vector b, respectively. The ρ k 、the u k , the said j and the w k All are determined based on the optimal solution of the optimization variables in the previous iteration of the current iteration.

14. The information transmission method according to any one of claims 11 to 13, characterized in that: The determination of g(θ n ) corresponds to the trough point of the curve n Values, including: Based on the g(θ n ) to determine the left and right boundary points 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 g(θ n ) corresponds to the trough point of the curve n value.

15. The information transmission method according to claim 14, characterized in that: Based on the g(θ n ) to determine the left and right boundary points of the search interval, including: In the g(θ n ) is a single trough curve, -π and π are used as the left and right boundary points of the search interval respectively.

16. The information transmission method according to claim 14, characterized in that: Based on the g(θ n ) to determine the left and right boundary points of the search interval, including: In the g(θ n ) is a curve with a left peak and a right valley, determine three search points 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 search1 Multiply the value obtained by m1 as the updated △θ search1 , the current The value of The current The value of and update the Subtract the updated Δθ search1 The obtained value is used as the updated Based on the updated and the updated Compare and exist In the case of 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, m1 is greater than or equal to 1.

17. The information transmission method according to claim 14, characterized in that: Based on the g(θ n ) to determine the left and right boundary points of the search interval, including: In the g(θ n ) is a curve with a left valley and a right peak, determine three search points and in, △θ search2 is the preset angle interval, △θ search2 >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 search2 Multiply the value obtained by m2 as the updated △θ search2 , the current The value of The current The value of and update the Add the updated Δθ search2 The obtained value is used as the updated Based on the updated and the updated Compare and exist In the case of 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, m2 is greater than or equal to 1.

18. The information transmission method according to claim 14, characterized in that: 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 g(θ n ) corresponds to the trough point of the curve n Values, including: 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 g(θ n ) corresponds to the trough point of the curve n value.

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: In the formula, 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 Indicates the minimum reflection amplitude of the reflection unit, Represents from -π / 2 to β min The relative phase shift difference of the corresponding 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: Including memory, transceiver, processor; Memory for storing computer programs; a transceiver, for transmitting and receiving data under the control of the processor; A processor, configured to read the computer program in the memory and execute the method according to any one of claims 1 to 19.

21. An information transmission device, characterized in that: include: 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 adopt the optimal transmit precoding matrix to transmit information, and configure the optimal RIS reflection coefficient matrix to the RIS, so that the RIS adopts 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, and the computer program is used to enable a processor to execute the method according to any one of claims 1 to 19.

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