STAR-RIS assisted cognitive radio network and rate optimization method

Abstract

The invention discloses an STAR-RIS-assisted cognitive radio network and a rate optimization method, and relates to the technical field of radio network rate optimization, and the method comprises the following steps: obtaining channel state information between nodes of the STAR-RIS-assisted cognitive radio network; the STAR-RIS assists the SBS to transmit a signal to the secondary user, and calculates the transmission rate of the secondary user and the interference suffered by the primary user; constructing a secondary user and rate maximization problem of joint optimization of SBS emission beam forming and STAR-RIS reflection and transmission coefficients; and decomposing the optimization problem into two sub-problems, and iteratively solving the two sub-problems through an alternative optimization algorithm to obtain the optimized secondary user and rate. According to the STAR-RIS assisted cognitive radio network and the rate optimization method provided by the invention, the signal coverage range is expanded through the STAR-RIS-ES assisted scheme, and the spectrum efficiency of secondary users of the cognitive radio network is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of radio network rate optimization, and in particular to a STAR-RIS-assisted cognitive radio network and a rate optimization method. Background Art

[0002] In the upcoming next-generation wireless communication network, at least 50 billion communication devices, including consumer terminals, industrial equipment, and transportation facilities, will generate 1,000 times the data rate demand, which will lead to the increasingly scarce limited spectrum resources. Cognitive radio technology can effectively improve the efficiency of spectrum utilization by allowing secondary users to dynamically share the spectrum resources of primary users, and is considered to be an effective way to solve the shortage of spectrum resources. Specifically, cognitive radio network (CRN) can detect the use of radio spectrum in real time, and through real-time adjustment of communication parameters, secondary users can make full use of idle spectrum resources for communication without interfering with primary users, thereby improving spectrum utilization. However, the transmission process of wireless signals is easily blocked by obstacles, resulting in a decrease in the spectrum efficiency of secondary users in CRN. Therefore, how to further improve the spectrum efficiency of CRN has become an urgent problem to be solved.

[0003] As an emerging technology, the reconfigurable intelligent surface (RIS) can generate a narrow beam focused on the receiver through the collaboration of multiple reflection units, thereby bypassing obstacles on the direct link and improving spectrum efficiency. Specifically, RIS is a planar array composed of a large number of low-cost passive components, each of which can independently adjust the amplitude and phase of the incident electromagnetic wave, thereby reconstructing the wireless electromagnetic environment according to different communication needs. However, the traditional RIS only has the function of signal reflection, so it can only provide 180° half-space signal coverage and cannot provide services to users in the other half of the space. In order to solve this problem, the simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) was proposed. It can achieve 360° full-space signal coverage through the simultaneous transmission and reflection of the incident electromagnetic wave signal by each unit, expanding the coverage range of the traditional RIS. By adjusting the transmission and reflection coefficients of each unit of STAR-RIS, the transmission signal and the reflection signal can be dynamically controlled, thereby providing more flexibility for the allocation of communication network resources.

[0004] The present invention can improve the channel environment of secondary users by combining cognitive radio and STAR-RIS technology, effectively increase the transmission rate of secondary users while suppressing interference to primary users, and realize the improvement of CRN spectrum efficiency. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: how to achieve 360° full-space signal coverage in the existing traditional RIS-assisted cognitive radio network and how to further improve the secondary user spectrum efficiency of the cognitive radio network.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a STAR-RIS-assisted cognitive radio network and rate optimization method, comprising obtaining channel state information between nodes of the STAR-RIS-assisted cognitive radio network. STAR-RIS-assisted SBS transmits signals to secondary users, calculates the transmission rate of the secondary users and the interference suffered by the primary users. Construct a secondary user and rate maximization problem of jointly optimizing SBS transmit beamforming and STAR-RIS reflection and transmission coefficients. Decompose the optimization problem into two sub-problems, and iterate and solve them through an alternating optimization algorithm to obtain the optimized secondary user and rate.

[0008] As a preferred solution of the STAR-RIS-assisted cognitive radio network and rate optimization method described in the present invention, wherein: the acquisition of channel state information between nodes of the STAR-RIS-assisted cognitive radio network includes acquiring channel state information from the SBS to the STAR-RIS node, from the STAR-RIS node to the primary user in the transmission area, from the STAR-RIS node to the secondary user in the transmission area, from the STAR-RIS node to the primary user in the reflection area, and from the STAR-RIS node to the secondary user in the reflection area, and the STAR-RIS-assisted SBS simultaneously transmits superimposed signals to the secondary users in the transmission area and the reflection area, The received signal of the secondary user in the area is expressed as: , in, Indicates sent to The signal of secondary users in the area, , t represents the transmission area, r represents the reflection area, and satisfies , Indicates the signal at SBS The transmit beamforming vector of , G represents the channel state information from SBS to STAR-RIS node, , Indicates the STAR-RIS node to Channel state information of secondary users in the area, , represents the N×M-dimensional complex-valued matrix space, represents the conjugate transposed matrix of matrix X, The reflection and transmission coefficient matrix of STAR-RIS is expressed as: , in, represents a diagonal matrix constructed from the elements in vector x, and They represent the amplitude and phase coefficients of the nth unit of STAR-RIS, respectively, and j is an imaginary unit. When STAR-RIS works under the ES protocol, and satisfy , and , .

[0009] Represents the Gaussian white noise of the secondary user, with zero mean and variance The complex Gaussian distribution of is expressed as: .

[0010] As a preferred solution of the STAR-RIS assisted cognitive radio network and rate optimization method of the present invention, wherein: the STAR-RIS assisted SBS transmits a signal to a secondary user, calculates the transmission rate of the secondary user and the interference suffered by the primary user, including according to the received signal Sure Signal-to-interference-noise ratio of secondary users in the area , expressed as: , in, Indicates modulus, Indicates the reflection or transmission area. If ,but ,like ,but , Indicates the signal at SBS The transmit beamforming vector of The transmission rate of the secondary users in the area is: .

[0011] As a preferred solution of the STAR-RIS assisted cognitive radio network and rate optimization method described in the present invention, wherein: the STAR-RIS assisted SBS transmits a signal to a secondary user, calculates the transmission rate of the secondary user and the interference suffered by the primary user, and further includes The received signal of the primary user in the area is expressed as: , in, Indicates the channel status information of the STAR-RIS to primary user link. Represents the Gaussian white noise of the primary user, which has zero mean and variance The complex Gaussian distribution of is expressed as: , According to the primary user receiving signal, calculate The interference to the primary user in the area is expressed as: , make , represents the reflection and transmission coefficient vector of STAR-RIS, then The transmission rate of secondary users in the area and The interference to the primary user in the area is converted into: , , in, , .

[0012] As a preferred solution of the STAR-RIS-assisted cognitive radio network and rate optimization method of the present invention, wherein: the construction of the secondary user sum rate maximization problem of jointly optimizing SBS transmit beamforming and STAR-RIS reflection and transmission coefficients includes constructing an optimization problem P0 with the goal of maximizing the sum rate of the secondary user, expressed as: , in, Indicates the maximum transmit power of SBS, Indicates that it is located The maximum interference that the primary users in the area can withstand, represents the transmit power constraint at the SBS, represents the interference temperature constraint at the primary user, ,as well as Represents the amplitude and phase constraints for each reflection and transmission element in STAR-RIS.

[0013] As a preferred solution of the STAR-RIS assisted cognitive radio network and rate optimization method of the present invention, wherein: the optimization problem is decomposed into two sub-problems and solved iteratively by an alternating optimization algorithm, including by alternatingly optimizing the SBS transmit beamforming vector and the STAR-RIS transmission and reflection coefficient vectors , decompose the original optimization problem into two sub-problems for iterative solution. Given the STAR-RIS transmission and reflection coefficient vectors Optimize the SBS transmit beamforming vector , construct sub-problem P1, expressed as: , Through the successive convex approximation algorithm and the semi-definite relaxation algorithm, the non-convex subproblem is transformed into a convex problem and solved using standard convex optimization tools. Finally, the optimized SBS transmit beamforming vector is obtained through eigenvalue decomposition. .

[0014] As a preferred solution of the STAR-RIS assisted cognitive radio network and rate optimization method of the present invention, wherein: the optimization problem is decomposed into two sub-problems, and the iterative solution is also provided by an alternating optimization algorithm, and the given SBS transmit beamforming vector , optimize the STAR-RIS transmission and reflection coefficient vector , construct sub-problem P2, expressed as: , Through the successive convex approximation algorithm and the semi-positive relaxation algorithm, the non-convex subproblem is transformed into a convex problem and solved using standard convex optimization tools. Finally, the optimized STAR-RIS transmission and reflection coefficient vectors are obtained through eigenvalue decomposition. , through the iterative algorithm, the sub-problems P1 and P2 are alternately optimized until the objective function converges, and the optimized secondary users and rate are obtained.

[0015] Another object of the present invention is to provide a STAR-RIS-assisted cognitive radio network and rate optimization system, which can expand the signal coverage of the current traditional RIS-assisted cognitive radio network technology and further improve the spectrum efficiency of the cognitive radio network through a STAR-RIS-assisted cognitive radio network scheme (STAR-RIS-ES assisted scheme) working under the ES protocol.

[0016] As a preferred solution of the STAR-RIS-assisted cognitive radio network and rate optimization system described in the present invention, it includes: an information acquisition module, a data calculation module, an optimization target construction module, and a solution module. The information acquisition module is used to obtain the channel state information between each node of the STAR-RIS-assisted cognitive radio network. The data calculation module is used to calculate the transmission rate of the secondary user and the interference suffered by the primary user when the STAR-RIS-assisted SBS transmits a signal to the secondary user. The optimization target construction module is used to construct a secondary user and rate maximization problem of jointly optimizing the SBS transmit beamforming and the STAR-RIS reflection and transmission coefficients. The solution module is used to decompose the optimization problem into two sub-problems, and iteratively solve them through an alternating optimization algorithm to obtain the optimized secondary user and rate.

[0017] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a STAR-RIS-assisted cognitive radio network and a rate optimization method.

[0018] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a STAR-RIS-assisted cognitive radio network and rate optimization method.

[0019] Beneficial effects of the present invention: The STAR-RIS-assisted cognitive radio network and rate optimization method provided by the present invention expand the signal coverage range through the STAR-RIS-ES auxiliary scheme and improve the secondary user spectrum efficiency of the cognitive radio network. The present invention achieves better results in terms of signal coverage range and communication spectrum efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1 An overall flow chart of a STAR-RIS-assisted cognitive radio network and rate optimization method provided for the first embodiment of the present invention.

[0022] Figure 2 A network model diagram of a STAR-RIS-assisted cognitive radio network and rate optimization method provided in the second embodiment of the present invention.

[0023] Figure 3A graph showing the relationship between the sum rate of secondary users in the network and the number of reconfigurable smart surface units when maximizing the sum rate of a STAR-RIS-assisted cognitive radio network and rate optimization method provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0025] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a STAR-RIS assisted cognitive radio network and rate optimization method, including: S1: Obtain channel state information between nodes in a STAR-RIS-assisted cognitive radio network.

[0026] Furthermore, obtaining the channel state information between each node of the STAR-RIS-assisted cognitive radio network includes obtaining the channel state information from the SBS to the STAR-RIS node, the STAR-RIS node to the primary user in the transmission area, the STAR-RIS node to the secondary user in the transmission area, the STAR-RIS node to the primary user in the reflection area, and the STAR-RIS node to the secondary user in the reflection area. The STAR-RIS-assisted SBS simultaneously transmits superimposed signals to the secondary users in the transmission area and the reflection area. The received signal of the secondary user in the area is expressed as: , in, Indicates sent to The signal of secondary users in the area, , t represents the transmission area, r represents the reflection area, and satisfies , Indicates the signal at SBS The transmit beamforming vector of , G represents the channel state information from SBS to STAR-RIS node, , Indicates the STAR-RIS node to Channel state information of secondary users in the area, , represents the N×M-dimensional complex-valued matrix space, represents the conjugate transposed matrix of matrix X, The reflection and transmission coefficient matrix of STAR-RIS is expressed as: , in, represents a diagonal matrix constructed from the elements in vector x, and They represent the amplitude and phase coefficients of the nth unit of STAR-RIS, respectively, and j is an imaginary unit. When STAR-RIS works under the ES protocol, and satisfy , and , ; Represents the Gaussian white noise of the secondary user, with zero mean and variance The complex Gaussian distribution of is expressed as: .

[0027] S2: STAR-RIS assists the SBS in transmitting signals to the secondary user and calculates the transmission rate of the secondary user and the interference suffered by the primary user.

[0028] Furthermore, STAR-RIS assists the SBS in transmitting signals to the secondary user, calculating the transmission rate of the secondary user and the interference to the primary user. The received signal of the primary user in the area is expressed as: , in, Indicates the channel status information of the STAR-RIS to primary user link. Represents the Gaussian white noise of the primary user, which has zero mean and variance The complex Gaussian distribution of is expressed as: , According to the primary user receiving signal, calculate The interference to the primary user in the area is expressed as: , make , represents the reflection and transmission coefficient vector of STAR-RIS, then The transmission rate of secondary users in the area and The interference to the primary user in the area is converted into: , , in, , .

[0029] S3: Construct the sub-user and rate maximization problem of jointly optimizing SBS transmit beamforming and STAR-RIS reflection and transmission coefficients.

[0030] Furthermore, constructing the secondary user sum rate maximization problem of jointly optimizing SBS transmit beamforming and STAR-RIS reflection and transmission coefficients includes constructing the optimization problem P0 with the goal of maximizing the sum rate of the secondary users, which is expressed as: , in, Indicates the maximum transmit power of SBS, Indicates that it is located The maximum interference that the primary users in the area can withstand, represents the transmit power constraint at the SBS, represents the interference temperature constraint at the primary user, ,as well as Represents the amplitude and phase constraints for each reflection and transmission element in STAR-RIS.

[0031] S4: Decompose the optimization problem into two sub-problems and solve them iteratively through the alternating optimization algorithm to obtain the optimized sub-users and rate.

[0032] Furthermore, due to the high coupling of variables in the objective function and constraints, the original optimization problem constructed is difficult to solve directly. The optimization problem is decomposed into two sub-problems and solved iteratively by an alternating optimization algorithm, including by alternating optimization of the SBS transmit beamforming vector and the STAR-RIS transmission and reflection coefficient vectors , decompose the original optimization problem into two sub-problems for iterative solution. Given the STAR-RIS transmission and reflection coefficient vectors Optimize the SBS transmit beamforming vector , construct sub-problem P1, expressed as: , make , and satisfy and ,but The transmission rate of secondary users in the area and The interferences suffered by the primary users in the area are converted into: , in, represents the trace of the matrix X, and Indicated by The constructed SBS transmit beamforming matrix, , .

[0033] The transmit power constraint at the SBS is converted to: , Based on the above transformation, the subproblem P1 is rewritten as the non-convex problem P 1-1 , expressed as: , in, represents the rank of the matrix X.

[0034] Introducing slack variables and , ,make , , then the transmission rate of the secondary user is expressed as: , By introducing constraints on slack variables , , P 1-1 Re-expressed as P 1-2 : , Solving by iteration Update to obtain feasible points , at a feasible point Using the first-order Taylor expansion, we get The linear lower bound of , expressed as: , Using semi-positive definite relaxation method to solve non-convex constraints Relax and solve the problem P iteratively 1-3 Equivalent solution P 1-2 , P 1-3 It is expressed as: , P 1-3 It is a convex semidefinite programming problem. The convex optimization solver CVX is used to solve the problem and the eigenvalue decomposition method is used to recover the rank-one solution, which is expressed as: , in, represents the optimized SBS transmit beamforming vector, express The maximum eigenvalue of It represents the iterative solution of the relaxed problem P 1-3The obtained optimized solution is express The corresponding feature vector.

[0035] It should be noted that the optimization problem is decomposed into two sub-problems and solved iteratively through an alternating optimization algorithm, including the given SBS transmit beamforming vector , optimize the STAR-RIS transmission and reflection coefficient vector , construct sub-problem P2, expressed as: , make , and satisfies and ,but The transmission rate of secondary users in the area and The interference to the primary user in the area is converted into: , in, , , , .

[0036] Based on the converted The transmission rate of the secondary users in the area and the interference suffered by the primary users rewrite the subproblem P2 as P 2-1 , expressed as: , Introducing slack variables and , , P 2-1 Re-expressed as P 2-2 , expressed as: , Solving by iteration Calculate feasible points , at a feasible point Using the first-order Taylor expansion, we get The linear lower bound of , expressed as: , Relax the rank-one constraint using a semidefinite relaxation method , by iteratively solving the problem P 2-3 Equivalent solution P 2-2 , P 2-3 It is expressed as: , Use CVX to solve the semi-positive programming problem, and use the eigenvalue decomposition method to recover the rank-one solution, which is expressed as: , in, represents the optimized STAR-RIS transmission and reflection coefficient vector, express The maximum eigenvalue of It represents the iterative solution of the relaxed problem P 2-3 The optimized solution obtained is express The corresponding feature vector.

[0037] It should also be noted that the optimized secondary users and rates include: Initialize the STAR-RIS transmission and reflection coefficient vectors .

[0038] Initialize the number of iterations , set the iteration precision .

[0039] Based on known , solve problem P1 to obtain .

[0040] Based on known , solve problem P2 to obtain .

[0041] Calculate the objective function value ,make .

[0042] Repeat steps until .

[0043] Get the optimized solution With the optimized network users and speed .

[0044] Example 2, reference Figure 2-Figure 3 , which is an embodiment of the present invention, provides a STAR-RIS-assisted cognitive radio network and rate optimization method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0045] An example of the present invention is realized by MATLAB software simulation, and the network model diagram is as follows: Figure 2In the simulation experiment, the wireless channels are assumed to be independent of each other, and the channel state information remains constant in the coherent time block. Considering that in a two-dimensional Cartesian coordinate system, the positions of SBS, STAR-RIS, secondary user in the reflection area, secondary user in the transmission area, primary user in the reflection area, and primary user in the transmission area are (-25, 25), (0, 0), (-10, 5), (10, 5), (-15, 10), (15, 10), respectively, in meters. The SBS is equipped with 8 antennas. The channel state information between SBS and STAR-RIS and between STAR-RIS and each user obeys the Rice distribution. The noise power , upper limit of transmitter power , interference temperature threshold .

[0046] Figure 3 This is a graph showing the relationship between the sum rate of secondary users in the network and the number of reconfigurable smart surface units when the sum rate is maximized in an example of the present invention.

[0047] First of all, it should be noted that in order to ensure the fairness of the comparison scheme, the traditional passive RIS assisted transmission scheme is to deploy a element-only reflective RIS and one equipped with The RIS of the two elements is only transmitted, and the two are adjacent to each other. Figure 3 It can be seen from the figure that compared with the traditional passive RIS assisted transmission scheme, the STAR-RIS assisted transmission scheme working under the MS (Mode Selection) protocol can effectively increase the number of network users and the rate. This is because, by analogy with Components are used for reflection only and The traditional passive RIS with elements for transmission only, STAR-RIS working under the MS protocol can dynamically adjust the number of reflection-only and transmission-only elements according to the channel state information, thereby improving the network information transmission rate. Compared with the MS protocol, the joint optimization scheme of STAR-RIS working under the ES protocol can further improve the network sub-users and rates, because the MS scheme is a special case of the ES scheme. Specifically, the reflection and transmission amplitude coefficients of STAR-RIS in the MS scheme are either 0 or 1, while the reflection and transmission amplitude coefficients of STAR-RIS in the ES scheme can take continuous values ​​between 0 and 1, with a higher degree of freedom. In addition, it can be seen from the figure that regardless of which scheme, the network sum rate increases with the increase in the number of units. Therefore, the network sub-users and rates can be improved by deploying more low-cost reflection units, which further proves the advantage of integrating RIS into CRN.

[0048] Embodiment 3, an embodiment of the present invention provides a STAR-RIS-assisted cognitive radio network and rate optimization system, including an information acquisition module, a data calculation module, an optimization target construction module, and a solution module.

[0049] The information acquisition module is used to obtain the channel state information between each node of the STAR-RIS-assisted cognitive radio network. The data calculation module is used to calculate the transmission rate of the secondary user and the interference suffered by the primary user when the STAR-RIS-assisted SBS transmits a signal to the secondary user. The optimization target construction module is used to construct the secondary user and rate maximization problem of jointly optimizing the SBS transmit beamforming and the STAR-RIS reflection and transmission coefficients. The solution module is used to decompose the optimization problem into two sub-problems, and iteratively solve them through an alternating optimization algorithm to obtain the optimized secondary user and rate.

[0050] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0051] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0052] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0053] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logical function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0054] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A STAR-RIS-assisted cognitive radio network and rate optimization method, characterized in that: include: Obtain channel state information between nodes in a STAR-RIS-assisted cognitive radio network; STAR-RIS assists the SBS in transmitting signals to secondary users and calculates the transmission rate of the secondary users and the interference to the primary users; Construct the secondary user and rate maximization problem of jointly optimizing SBS transmit beamforming and STAR-RIS reflection and transmission coefficients; The optimization problem is decomposed into two sub-problems, and the sub-problems are solved iteratively through an alternating optimization algorithm to obtain the optimized sub-users and rate.

2. The STAR-RIS-assisted cognitive radio network and rate optimization method as claimed in claim 1, characterized in that: The obtaining of the channel state information between each node of the STAR-RIS assisted cognitive radio network includes obtaining the channel state information from the SBS to the STAR-RIS node, the STAR-RIS node to the primary user in the transmission area, the STAR-RIS node to the secondary user in the transmission area, the STAR-RIS node to the primary user in the reflection area, and the STAR-RIS node to the secondary user in the reflection area. The STAR-RIS assisted SBS transmits superimposed signals to the secondary users in the transmission area and the reflection area at the same time. The received signal of the secondary user in the area is expressed as: ， in, Indicates sent to The signal of secondary users in the area, , t represents the transmission area, r represents the reflection area, and satisfies , Indicates the signal at SBS The transmit beamforming vector of , G represents the channel state information from SBS to STAR-RIS node, , Indicates the STAR-RIS node to Channel state information of secondary users in the area, , represents the N×M-dimensional complex-valued matrix space, represents the conjugate transposed matrix of matrix X, The reflection and transmission coefficient matrix of STAR-RIS is expressed as: , in, represents a diagonal matrix constructed from the elements in vector x, and They represent the amplitude and phase coefficients of the nth unit of STAR-RIS, respectively, and j is an imaginary unit. When STAR-RIS works under the ES protocol, and satisfy , and , ; Represents the Gaussian white noise of the secondary user, with zero mean and variance The complex Gaussian distribution of is expressed as: 。 3. The STAR-RIS-assisted cognitive radio network and rate optimization method as claimed in claim 2, characterized in that: The STAR-RIS assists the SBS to transmit signals to the secondary user, calculates the transmission rate of the secondary user and the interference to the primary user, including Sure Signal-to-interference-noise ratio of secondary users in the area , expressed as: ， in, Indicates modulus, Indicates the reflection or transmission area. If ,but ,like ,but , Indicates the signal at SBS The transmit beamforming vector of The transmission rate of the secondary users in the area is: 。 4. The STAR-RIS-assisted cognitive radio network and rate optimization method as claimed in claim 3, characterized in that: The STAR-RIS assists the SBS to transmit signals to the secondary user, calculates the transmission rate of the secondary user and the interference suffered by the primary user, and also includes The received signal of the primary user in the area is expressed as: ， in, Indicates the channel status information of the STAR-RIS to primary user link. Represents the Gaussian white noise of the primary user, which has zero mean and variance The complex Gaussian distribution of is expressed as: ， According to the primary user receiving signal, calculate The interference to the primary user in the area is expressed as: ， make , represents the reflection and transmission coefficient vector of STAR-RIS, then The transmission rate of secondary users in the area and The interference to the primary user in the area is converted into: ， ， in, , .

5. The STAR-RIS-assisted cognitive radio network and rate optimization method as claimed in claim 4, characterized in that: The construction of the secondary user sum rate maximization problem of jointly optimizing the SBS transmit beamforming and the STAR-RIS reflection and transmission coefficients includes constructing an optimization problem P0 with the goal of maximizing the sum rate of the secondary users, which is expressed as: ， in, Indicates the maximum transmit power of SBS, Indicates that it is located The maximum interference that the primary users in the area can withstand, represents the transmit power constraint at the SBS, represents the interference temperature constraint at the primary user, ,as well as Represents the amplitude and phase constraints for each reflection and transmission element in STAR-RIS.

6. The STAR-RIS-assisted cognitive radio network and rate optimization method as claimed in claim 5, characterized in that: The optimization problem is decomposed into two sub-problems and solved iteratively by an alternating optimization algorithm, including alternatingly optimizing the SBS transmit beamforming vector and the STAR-RIS transmission and reflection coefficient vectors , decompose the original optimization problem into two sub-problems for iterative solution. Given the STAR-RIS transmission and reflection coefficient vectors Optimize the SBS transmit beamforming vector , construct sub-problem P1, expressed as: ， Through the successive convex approximation algorithm and the semi-definite relaxation algorithm, the non-convex subproblem is transformed into a convex problem and solved using standard convex optimization tools. Finally, the optimized SBS transmit beamforming vector is obtained through eigenvalue decomposition. .

7. The STAR-RIS-assisted cognitive radio network and rate optimization method as claimed in claim 6, characterized in that: The optimization problem is decomposed into two sub-problems, and the iterative solution is also provided by an alternating optimization algorithm, including a given SBS transmit beamforming vector , optimize the STAR-RIS transmission and reflection coefficient vector , construct sub-problem P2, expressed as: ， Through the successive convex approximation algorithm and the semi-positive relaxation algorithm, the non-convex subproblem is transformed into a convex problem and solved using standard convex optimization tools. Finally, the optimized STAR-RIS transmission and reflection coefficient vectors are obtained through eigenvalue decomposition. , through the iterative algorithm, the sub-problems P1 and P2 are alternately optimized until the objective function converges, and the optimized secondary users and rate are obtained.

8. A system using the STAR-RIS-assisted cognitive radio network and rate optimization method as claimed in any one of claims 1 to 7, characterized in that: It includes information acquisition module, data calculation module, optimization target construction module and solution module; The information acquisition module is used to obtain channel state information between nodes of the STAR-RIS assisted cognitive radio network; The data calculation module is used to calculate the transmission rate of the secondary user and the interference suffered by the primary user when the STAR-RIS assists the SBS to transmit a signal to the secondary user; The optimization target building module is used to construct a secondary user and rate maximization problem for jointly optimizing SBS transmit beamforming and STAR-RIS reflection and transmission coefficients; The solution module is used to decompose the optimization problem into two sub-problems, and solve them iteratively through an alternating optimization algorithm to obtain the optimized secondary users and rate.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the STAR-RIS-assisted cognitive radio network and rate optimization method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the STAR-RIS-assisted cognitive radio network and rate optimization method according to any one of claims 1 to 7 are implemented.

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