A Rate Maximization Method Based on IRS-Assisted Full-Duplex Two-Way Communication

Abstract

A method for maximizing the rate based on IRS-assisted full-duplex two-way communication, belonging to the field of wireless communication technology, comprises the following steps: establishing an IRS-assisted full-duplex two-way communication system model; determining the optimization variables as the transmit-end precoding vector P and the IRS phase shift matrix Θ; alternately optimizing the variables P and Θ. First, fix the phase shift matrix Θ and transform the non-convex fractional problem into a convex function form with respect to the precoding vector P; obtain the optimal precoding vector P. On the basis of obtaining the optimal precoding vector P, optimize the phase shift matrix Θ. By swapping the elements of the IRS phase shift matrix Θ and the channel H, decouple the variable θ related to the IRS reflection coefficient in the phase shift matrix Θ and optimize it. Based on the IRS reflection coefficient constraint condition, obtain the optimal phase shift matrix Θ. Finally, output the optimized variables and the optimized rate value. This method continuously alternately optimizes the optimization variables to maximize the communication rate.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a method for maximizing the rate of IRS-assisted full-duplex two-way communication. Background Art

[0002] With the rapid development of communication technologies, highly available information communication technologies are crucial. Due to its flexibility and low cost, intelligent reflecting surface (IRS) is expected to become an important part of future communication networks, providing communication services for blind users or areas not covered by the network. To solve the problems of blind users or areas not covered by the network, we introduce IRS-assisted wireless communication to provide communication support for users who cannot communicate normally. However, in practical application scenarios, due to the power constraint at the transmitting end and the randomness of IRS phase shifts, the improvement of communication rate is usually very limited. In addition, due to the near-field additive path loss and far-field multiplicative path loss, the reasonable deployment of IRS is also very important. These problems pose a major challenge to achieving the maximization of IRS-assisted wireless communication.

[0003] In previous studies, IRS has been mainly applied as a relay in scenarios between base stations and users. These studies usually only optimize the precoding at the base station side or the IRS phase shift, but this method has some limitations. For example, a beamforming method for an IRS-enhanced full-duplex communication system disclosed in Chinese Patent CN118764059A, a two-way communication method based on IRS and NOMA disclosed in Chinese Patent CN115913838A, and a physical layer security enhancement method based on intelligent reflecting surface and full-duplex communication disclosed in Chinese Patent CN115499827A. First, the improvement effect of these methods on communication rate is limited and cannot fully utilize the advantages of IRS. Second, these methods often adopt a fixed planning method for the allocation of communication resources and cannot achieve optimal resource utilization. In addition, these methods need to be redesigned and adjusted in practical applications to adapt to different communication environments and changing requirements. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technologies, the purpose of the present invention is to provide a method for maximizing the rate of IRS-assisted full-duplex two-way communication.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A method for maximizing the rate of IRS-assisted full-duplex two-way communication, comprising the following steps:

[0007] S1 Establish an IRS-assisted full-duplex two-way communication system model;

[0008] S2 constructs a sum-rate maximization optimization problem based on the IRS-assisted communication system model using the alternating optimization iteration algorithm, and determines that the optimization variables are the transmit precoding vector P and the IRS phase shift matrix Θ;

[0009] S3 alternately optimizes the precoding variable P and the IRS phase shift matrix Θ. First, fix the IRS phase shift matrix Θ, and use quadratic transformation to transform the non-convex fractional problem into a convex function form with respect to the precoding vector P; then introduce auxiliary variables, and under the maximum transmit power constraint, obtain the optimal precoding vector P through Lagrangian dual transformation;

[0010] S4, based on the obtained optimal precoding vector P, optimizes the IRS phase shift matrix Θ. Define a new expression to transform the original problem into an optimization problem with respect to the IRS phase shift matrix Θ. By swapping the elements of the IRS phase shift matrix Θ and the channel H, decouple the variable θ related to the IRS reflection coefficient in the IRS phase shift matrix Θ and optimize it. Based on the IRS reflection coefficient constraint condition, obtain the optimal IRS phase shift matrix Θ through Lagrangian dual transformation. Finally, output the optimized variables and the optimized rate value for the accumulation of the rate, obtain the sum rate and output the line chart.

[0011] In S2, based on the alternating optimization iteration algorithm, the expression of the sum-rate maximization optimization problem based on the IRS-assisted communication system model is constructed according to the Shannon formula

[0012] , where, R sum is the sum rate, K is the number of TRs, k represents the th device, γ k is the device k 's signal-to-interference-plus-noise ratio.

[0013] In S3,

[0014] (a) Initialize the precoding vector P, fix the IRS phase shift matrix Θ and the sum rate , and enter the loop optimization process;

[0015] (b) Fix the IRS phase shift matrix Θ, construct an optimization problem with respect to the precoding vector P, introduce an auxiliary variable β through the quadratic transformation method, and transform the original fractional problem into a convex function problem with respect to P;

[0016] (c) Take the partial derivative of the auxiliary variable β and set its derivative to zero to obtain the optimal solution of the auxiliary variable β;

[0017] (d) Based on the Lagrangian multiplier method, establish an optimization expression for the precoding vector P in combination with the maximum power constraint condition;

[0018] (e) Solve the optimization expression to obtain the optimal solution of the precoding vector P.

[0019] The optimization expression of the precoding vector P is

[0020] , L is the symbol of the Lagrangian function; μ is the Lagrange multiplier for dealing with the constraint conditions; K represents the number of TRs, represents the real part of the complex number; k , j is the number of the TR, where k , j ∈ {1, 2}; , are auxiliary variables for transforming the problem form; is the equivalent channel of the received signal of the k -th TR, H represents the conjugate transpose; , respectively represent the precoding vectors of the k, j -th TR, is the maximum transmit power, is the noise variance.

[0021] In S4,

[0022] (a) Based on the condition that the precoding vector P is fixed, construct an equivalent channel expression related to the IRS phase shift matrix Θ;

[0023] (b) Introduce an auxiliary variable ϖ through the quadratic transformation method to transform the original optimization problem into an optimization problem expression about the IRS phase shift matrix Θ;

[0024] (c) Take the partial derivative of the auxiliary variable ϖ and set its derivative to zero to obtain the optimal solution of the auxiliary variable;

[0025] (d) Decouple the reflection coefficient variable θ in the IRS phase shift matrix Θ, and optimize the value of θ through the Lagrangian dual transformation under the constraint conditions of the IRS reflection coefficient;

[0026] (e) Iteratively execute steps (a) to (d) until the sum rate R sum converges, and output the optimized precoding vector P, IRS phase shift matrix Θ and the optimized rate value.

[0027] In step (a), the equivalent channel expression is simplified to a linear expression through the joint action of the channel matrix H and the phase shift matrix Θ, and the specific form is: , where Qis the function symbol, and Θ is the phase shift matrix of the IRS; r is the IRS number, where r ∈ {1, 2}; is the k th channel from the r th TR to the th IRS, r is the phase shift matrix of the th IRS, r is the k th channel from the

[0028] The expression of the optimization problem regarding the IRS phase shift matrix Θ in step (b) is , f is the function symbol, Θ is the phase shift matrix of the IRS, is the introduced auxiliary variable; , respectively represent the product of the precoding vector of the k , j th TR and the equivalent channel of the r th IRS.

[0029] The method for decoupling the reflection coefficient variable θ described in step (d) includes:

[0030] By swapping the elements of the IRS phase shift matrix Θ and the channel H, decouple the variable θ regarding the IRS reflection coefficient in the IRS phase shift matrix Θ and optimize it. Under the IRS reflection coefficient constraint conditions, obtain the optimal θ value through Lagrangian dual transformation.

[0031] A computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the above-mentioned rate maximization method for IRS-assisted full-duplex two-way communication.

[0032] An electronic device, including a memory, a processor, and a computer program stored on the memory, and when the processor executes the program, it implements the above-mentioned rate maximization method for IRS-assisted full-duplex two-way communication.

[0033] The beneficial effects of the present invention: By applying the alternating optimization algorithm to IRS-assisted full-duplex two-way communication, the drone can learn strategies to adapt to different environments and communication requirements. This method continuously alternates and optimizes the optimization variables to maximize the communication rate. Brief Description of the Drawings

[0034] Figure 1 is the overall flowchart of the present invention.

[0035] Figure 2It is the model diagram of the IRS-assisted full-duplex two-way communication system of the present invention.

[0036] Figure 3 It is the schematic diagram of the performance comparison between the proposed model and the single-block IRS model under different transmission powers. Specific embodiments

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0039] As Figure 1 shown, the present invention provides a method for maximizing the rate based on IRS-assisted full-duplex two-way communication, which includes the following steps:

[0040] S1 Establish an IRS-assisted full-duplex two-way communication system model;

[0041] S2 Based on the alternating optimization iteration algorithm, construct a sum-rate maximization optimization problem based on the IRS-assisted communication system model, and determine that the optimization variables are the transmit-end precoding vector P and the IRS phase shift matrix Θ;

[0042] S3 Alternately optimize the precoding variable P and the IRS phase shift matrix Θ. First, fix the IRS phase shift matrix Θ, and use quadratic variation to transform the non-convex fractional problem into a convex function form with respect to the precoding vector P; then introduce auxiliary variables, and under the maximum transmission power constraint condition, obtain the optimal precoding vector P through Lagrangian dual transformation;

[0043] S4 On the basis of obtaining the optimal precoding vector P, optimize the IRS phase shift matrix Θ, define a new expression to transform the original problem into an optimization problem with respect to the IRS phase shift matrix Θ, decouple the variable θ related to the IRS reflection coefficient in the IRS phase shift matrix Θ through the element exchange between the IRS phase shift matrix Θ and the channel H and optimize it, and based on the IRS reflection coefficient constraint condition, obtain the optimal IRS phase shift matrix Θ through Lagrangian dual transformation, and finally output the optimized variables and the optimized rate value.

[0044] The specific process of the first step is as follows:

[0045] As Figure 2 shown, this application considers an IRS-assisted communication system; assuming that due to the obstruction of buildings or obstacles, it is necessary to deploy an intelligent reflecting surface (IRS) as a relay to assist in maintaining normal communication between two full-duplex devices. Assuming that the positions of the full-duplex devices are fixed, two distributed IRSs are respectively close to the two full-duplex devices.

[0046] In S2, based on the alternating optimization iteration algorithm, according to the Shannon formula, an expression for maximizing the sum rate optimization problem based on the IRS-assisted communication system model is constructed

[0047] .

[0048] In S3, the following steps are taken for processing:

[0049] (a) Initialize the precoding vector P, the fixed IRS phase shift matrix Θ, and the rate , and enter the loop optimization process;

[0050] (b) Fix the IRS phase shift matrix Θ, construct an optimization problem regarding the precoding vector P, introduce an auxiliary variable β through the quadratic transformation method, and transform the original fractional problem into a convex function problem regarding P;

[0051] (c) Take the partial derivative of the auxiliary variable β and set its derivative to zero to obtain the optimal solution of the auxiliary variable β;

[0052] (d) Based on the Lagrange multiplier method, establish an optimization expression for the precoding vector P in combination with the maximum power constraint condition;

[0053] (e) Solve the optimization expression to obtain the optimal solution of the precoding vector P.

[0054] Initialize the precoding vector P, the fixed phase shift matrix Θ, and the sum rate and start the loop. Fix the phase shift matrix Θ, optimize the precoding vector P, establish an expression for the optimization problem regarding P, first use the quadratic transformation method to introduce an auxiliary variable , obtain the transformed expression , and transform the original fractional problem into a convex function form regarding P; next, take the partial derivative of the auxiliary variable to zero to obtain its optimal value; in the third step, according to the Lagrange multiplier method, based on the maximum power constraint, establish the expression ; in the last step, solve to obtain the optimal solution of the precoding vector P.

[0055] In S4,

[0056] In the first step, based on the result of S3, fix the precoding vector P and optimize the IRS phase shift matrix Θ;

[0057] In the second step, define the equivalent channel expression for the IRS phase shift matrix Θ to simplify the original optimization problem;

[0058] Subsequently, using the same quadratic variation method in S3, introduce the auxiliary variable ϖ to obtain the optimization problem expression for the IRS phase shift matrix Θ ;

[0059] In the third step, obtain its optimal value by taking the partial derivative of the auxiliary variable to be 0;

[0060] In the fourth step, by swapping the elements of the IRS phase shift matrix Θ and the channel H, decouple the variable θ related to the IRS reflection coefficient in the IRS phase shift matrix Θ and optimize it. Under the constraint condition of the IRS reflection coefficient, obtain the optimal θ value through Lagrangian dual transformation;

[0061] Repeat the above steps until the rate converges, and then output the optimized precoding vector P, IRS phase shift matrix Θ, and the cumulative sum rate value.

[0062] See Figure 3 , for the performance comparison between the model established in this application and the single-block IRS model under different transmit powers.

[0063] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the above method for maximizing the rate of IRS-assisted full-duplex two-way communication. The readable storage medium may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory, and may further include a read-only memory (ROM). Program code is stored thereon, and when the program code is executed by the processor, the processor executes the steps of the embodiments described in this specification.

[0064] The memory may also include a program / utilities having a set (at least one) of program modules, and such program modules include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0065] The bus may represent one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any bus structure in a variety of bus structures.

[0066] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory. When the processor executes the program, the rate maximization method for IRS-assisted full-duplex two-way communication described above is implemented.

[0067] The electronic device can also communicate with one or more external devices (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device, and / or communicate with any device that enables the electronic device to communicate with one or more other computing devices (such as a router, a modem, etc.). This kind of communication can be carried out through an input / output (I / O) interface. Moreover, the electronic device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0068] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described here can be implemented by software, or can be implemented by the way of software combined with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0069] The embodiments should not be regarded as a limitation to the present invention, but any improvement based on the spirit of the present invention should be within the protection scope of the present invention.

Claims

1. A rate maximization method based on IRS-assisted full-duplex bidirectional communication, characterized in that: It includes the following steps: S1 establishes an IRS-assisted full-duplex two-way communication system model; S2 constructs a sum rate maximization optimization problem based on the IRS-assisted communication system model based on the alternating optimization iterative algorithm, and determines the optimization variables as the transmitter precoding vector P and the IRS phase shift matrix Θ; S3 alternately optimizes the precoding vector P and the IRS phase shift matrix Θ. First, the IRS phase shift matrix Θ is fixed, and the non-convex fractional problem is transformed into a convex function form related to the precoding vector P by using quadratic transformation. Then, auxiliary variables are introduced, and the optimal precoding vector P is obtained through Lagrange dual transformation under the maximum transmission power constraint; S4 optimizes the IRS phase shift matrix Θ on the basis of obtaining the optimal precoding vector P, defines a new expression to transform the original problem into an optimization problem about the IRS phase shift matrix Θ, and decouples the variable θ related to the IRS reflection coefficient in the IRS phase shift matrix Θ by exchanging the elements of the IRS phase shift matrix Θ and the channel H, and optimizes it. Based on the IRS reflection coefficient constraint, the optimal IRS phase shift matrix Θ is obtained through Lagrangian dual transformation, and finally the optimized variables and the optimized rate values ​​are output for the accumulation of the rate, and the sum rate is obtained and a line graph is output. In S3, (a) Initialize the precoding vector P, fix the IRS phase shift matrix Θ and the sum rate , and enter the cycle optimization process; (b) Fix the IRS phase shift matrix Θ, construct an optimization problem about the precoding vector P, introduce the auxiliary variable β through the quadratic transformation method, and transform the original fraction problem into a convex function problem about P; (c) taking a partial derivative of the auxiliary variable β and making its derivative zero, thereby obtaining an optimal solution for the auxiliary variable β; (d) Based on the Lagrange multiplier method and combined with the maximum power constraint, an optimal expression for the precoding vector P is established; (e) Solving the optimization expression to obtain the optimal solution of the precoding vector P.

2. The rate maximization method based on IRS-assisted full-duplex bidirectional communication according to claim 1, characterized in that: In S2, based on the alternating optimization iterative algorithm, the sum rate maximization optimization problem expression based on the IRS auxiliary communication system model is constructed according to the Shannon formula ,in, R sum is the sum rate, K refers to the number of TRs, k Indicates Devices, γ k For equipment k signal-to-interference-noise ratio.

3. The rate maximization method based on IRS-assisted full-duplex bidirectional communication according to claim 1, characterized in that: The optimization expression of the precoding vector P is: , L is the Lagrangian function symbol; μ is the Lagrange multiplier, used to deal with constraints; K represents the number of TRs, represents the real part of a complex number; k , j is the TR number, where k , j ∈ {1, 2}; , is an auxiliary variable used to transform the question form; It is k The equivalent channel of the TR receiving signal is: H represents conjugate transpose; , Respectively represent k, j The precoding vector of TR, is the maximum transmit power, is the noise variance.

4. The rate maximization method based on IRS-assisted full-duplex bidirectional communication according to claim 1, characterized in that: In S4, (a) Based on the condition that the precoding vector P is fixed, the equivalent channel expression related to the IRS phase shift matrix Θ is constructed; (b) The auxiliary variable ϖ is introduced through the secondary transformation method to transform the original optimization problem into the optimization problem expression about the IRS phase shift matrix Θ; (c) taking a partial derivative of the auxiliary variable ϖ and making its derivative zero, thereby obtaining an optimal solution for the auxiliary variable; (d) Decouple the reflection coefficient variable θ from the IRS phase shift matrix Θ and optimize the value of θ through Lagrangian dual transformation under the constraint of the IRS reflection coefficient; (e) Iterate steps (a) to (d) until the sum rate R sum After convergence, the optimized precoding vector P, IRS phase shift matrix Θ and optimized rate value are output.

5. The rate maximization method based on IRS-assisted full-duplex bidirectional communication according to claim 4, characterized in that: The equivalent channel expression in step (a) is simplified into a linear expression by combining the channel matrix H and the phase shift matrix Θ, and the specific form is: ,in Q is the function symbol, Θ is the phase shift matrix of IRS; r is the IRS number, where r ∈ {1, 2}; It is k TR to r IRS channels, It is r The phase shift matrix of an IRS, It is r IRS to k TR channels, Indicates k The precoding vector of TR.

6. The rate maximization method based on IRS-assisted full-duplex bidirectional communication according to claim 4, characterized in that: The optimization problem expression for the IRS phase shift matrix Θ in step (b) is: , f is the function symbol, Θ is the phase shift matrix of IRS, is an auxiliary variable introduced; , Respectively represent k , j The precoding vector of the first TR is r The product of the equivalent channels of IRS, is the auxiliary variable introduced by Lagrangian duality, represents the real part of a complex number, is the noise variance.

7. The rate maximization method based on IRS-assisted full-duplex bidirectional communication according to claim 4, characterized in that: The method of decoupling the reflection coefficient variable θ in step (d) comprises: By exchanging the elements of the IRS phase shift matrix Θ and the channel H, the variable θ related to the IRS reflection coefficient in the IRS phase shift matrix Θ is decoupled and optimized. Under the constraint of the IRS reflection coefficient, the optimal θ value is obtained through Lagrangian dual transformation.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the rate maximization method based on IRS-assisted full-duplex bidirectional communication is implemented.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: When the processor executes the program, the rate maximization method based on IRS-assisted full-duplex bidirectional communication is implemented as described in any one of claims 1-7.

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