Rate maximization method based on IRS-assisted full-duplex bidirectional communication

Abstract

A rate maximization method based on IRS-assisted full-duplex bidirectional communication belongs to the technical field of wireless communication, and comprises the following steps: establishing an IRS-assisted full-duplex bidirectional communication system model; determining optimization variables as a transmitting end precoding vector P and an IRS phase shift matrix theta; alternately optimizing variables P and theta, firstly fixing a phase shift matrix theta, and converting a non-convexity fraction problem into a convex function form related to a pre-coding vector P; obtaining an optimal precoding vector P; on the basis of obtaining the optimal pre-coding vector P, optimizing the phase shift matrix theta, decoupling a variable theta related to an IRS reflection coefficient in the phase shift matrix theta through element exchange of the IRS phase shift matrix theta and a channel H, optimizing the variable theta, obtaining an optimal phase shift matrix theta based on an IRS reflection coefficient constraint condition, and obtaining the optimal pre-coding vector P; and finally, outputting the optimized variable and the optimized rate value. According to the method, the communication rate is maximized by continuously and alternately optimizing the optimization variables.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a rate maximization method based on IRS-assisted full-duplex bidirectional communication. Background Art

[0002] With the rapid development of communication technology, high-availability information communication technology is essential. Smart reflecting surface (IRS) is expected to become an important part of future communication networks due to its flexibility and low cost, providing communication services for users in blind areas or areas not covered by the network. In order to solve the problem of users in blind areas or areas without network coverage, we introduced IRS-assisted wireless communication to provide communication support for users who cannot communicate normally. However, in actual application scenarios, due to the power constraints of the transmitter and the randomness of the IRS phase shift, the improvement in communication rate is usually very limited. In addition, due to the near-field additive path loss and the far-field multiplicative path loss, the reasonable deployment of IRS is also very important. These issues are a major challenge to maximizing IRS-assisted wireless communication.

[0003] In previous studies, IRS was mainly used as a relay in scenarios between base stations and users. These studies usually only optimize the precoding or IRS phase shift on the base station side, but this method has some limitations. For example, Chinese patent CN118764059A discloses an IRS enhanced beamforming method for full-duplex communication system, Chinese patent CN115913838A discloses a two-way communication method based on IRS and NOMA, and Chinese patent CN115499827A discloses a physical layer security enhancement method based on intelligent reflective surface and full-duplex communication. First, these methods have limited effect on improving communication rates and cannot give full play to the advantages of IRS. Secondly, these methods often use a fixed planning method for the allocation of communication resources, which 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 changes in demand. Summary of the invention

[0004] In view of the deficiencies in the prior art, an object of the present invention is to provide a rate maximization method based on IRS-assisted full-duplex bidirectional communication.

[0005] To achieve the above object, the present invention provides the following technical solutions: A rate maximization method based on IRS-assisted full-duplex bidirectional communication comprises 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 variable 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 changes. Then, auxiliary variables are introduced, and the optimal precoding vector P is obtained by 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 the Lagrangian dual transformation. Finally, the optimized variables and the optimized rate values ​​are output for accumulating the rate, and the sum rate is obtained and a line graph is output.

[0006] 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.

[0007] 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.

[0008] 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.

[0009] 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.

[0010] 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 tok TR channels.

[0011] 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 the IRSs.

[0012] 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.

[0013] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned rate maximization method based on IRS-assisted full-duplex bidirectional communication. An electronic device comprises a memory, a processor and a computer program stored in the memory, wherein when the processor executes the program, the rate maximization method based on IRS-assisted full-duplex bidirectional communication is implemented.

[0014] The beneficial effects of the present invention are as follows: by applying the alternating optimization algorithm to IRS-assisted full-duplex two-way communication, the UAV can learn strategies to adapt to different environments and communication requirements. The method continuously optimizes the optimization variables alternately to maximize the communication rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is the overall flow chart of the present invention.

[0016] Figure 2 This is a model diagram of the IRS-assisted full-duplex two-way communication system of the present invention.

[0017] Figure 3 It is a schematic diagram of the performance comparison between the proposed model and the single-block IRS model at different transmission powers. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0020] like Figure 1 As shown, the present invention provides a rate maximization method based on IRS-assisted full-duplex bidirectional communication, which 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 variable 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 changes. Then, auxiliary variables are introduced, and the optimal precoding vector P is obtained by 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, and defines a new expression to transform the original problem into an optimization problem about the IRS phase shift matrix Θ. 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. 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 optimized rate values ​​are output.

[0021] The specific process of step 1 is: like Figure 2 As shown, the present application considers an IRS-assisted communication system; assuming that due to obstruction by buildings or obstacles, an intelligent reflecting surface (IRS) needs to be deployed as a relay to assist two full-duplex devices in maintaining normal communication. Assuming that the position of the full-duplex device is fixed, two distributed IRSs are respectively close to the two full-duplex devices.

[0022] In S2, based on the alternating optimization iterative algorithm and Shannon's formula, the sum rate maximization optimization problem expression based on the IRS auxiliary communication system model is constructed. .

[0023] In S3, the following steps are performed: (a) Initialize the precoding vector P, fix the IRS phase shift matrix Θ and 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.

[0024] Initialize the precoding vector P and the fixed phase shift matrix Θ, as well as the sum rate The phase shift matrix Θ is fixed, the precoding vector P is optimized, and the optimization problem expression about P is established. First, the auxiliary variable is introduced by using the quadratic change method. , and get the changed expression , the original fraction problem is transformed into a convex function form about P; the next step is to use the auxiliary variable The partial derivative is taken as 0 to obtain its optimal value; the third step is to establish the expression based on the maximum power constraint according to the Lagrange multiplier method: ; The last step is to obtain the optimal solution of the precoding vector P.

[0025] In S4, The first step is to fix the precoding vector P and optimize the IRS phase shift matrix Θ based on the result of S3; The second step is to define the equivalent channel expression for the IRS phase shift matrix Θ To simplify the original optimization problem; Then, using the same quadratic variation method in S3, the auxiliary variable ϖ is introduced to obtain the optimization problem expression for the IRS phase shift matrix Θ: ; The third step is to use auxiliary variables Taking the partial derivative as 0 obtains its optimal value; The fourth step is to decouple the variable θ related to the IRS reflection coefficient in the IRS phase shift matrix θ by exchanging the elements of the channel H and optimize it. Under the constraint of the IRS reflection coefficient, the optimal θ value is obtained through Lagrangian dual transformation. Repeat the above steps until the rate When converged, the optimized precoding vector P, IRS phase shift matrix Θ and accumulated sum rate value are output.

[0026] See also Figure 3 ,The performance of the model established in this application is compared with the ,single-block IRS model at different transmit powers.

[0027] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned rate maximization method based on IRS-assisted full-duplex bidirectional communication is implemented. 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). A program code is stored thereon, and the program code is executed by a processor, so that the processor executes the steps of the embodiment as recorded in this specification.

[0028] The memory may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0029] The bus may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0030] The present invention also provides an electronic device, comprising a memory, a processor and a computer program stored in the memory, wherein when the processor executes the program, the rate maximization method based on IRS-assisted full-duplex bidirectional communication is implemented.

[0031] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device (e.g., routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may be performed through an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, 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 may be used in conjunction 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.

[0032] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which 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 implementation of the present disclosure.

[0033] The embodiments should not be regarded as limiting the present invention, but any improvements made 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 variable 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 the Lagrangian dual transformation. Finally, the optimized variables and the optimized rate values ​​are output for accumulating the rate, and the sum rate is obtained and a line graph is output.

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: 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.

4. The rate maximization method based on IRS-assisted full-duplex bidirectional communication according to claim 3, 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.

5. 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.

6. The rate maximization method based on IRS-assisted full-duplex bidirectional communication according to claim 5, 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.

7. The rate maximization method based on IRS-assisted full-duplex bidirectional communication according to claim 5, 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 the IRSs.

8. The rate maximization method based on IRS-assisted full-duplex bidirectional communication according to claim 5, 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.

9. 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.

10. 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 1-8.

Images