NOMA metasurface auxiliary sensing integrated network resource optimization method

Abstract

The invention provides an NOMA-based metasurface auxiliary sensing integrated network resource optimization method. The method comprises the following steps: constructing an RIS auxiliary sensing integrated system robust security model; setting a constraint condition, and optimizing the RIS auxiliary sensing integrated system robust security model based on the constraint condition to obtain a communication sensing integrated system robust resource allocation model; converting a non-convex problem of the robust resource allocation model of the communication perception integrated system into an equivalent convex problem; solving the convex problem to obtain an optimal solution; constructing a network resource optimization scheme based on the optimal solution; by optimizing the beam forming strategy, the robustness and safety of the system can be improved under the condition that the CSI is imperfect, the signal strength of legal users is ensured, and meanwhile signal receiving of eavesdroppers is restrained.

Description

Technical Field

[0001] This invention belongs to the field of communication transmission technology and relates to a method for optimizing network resources in a NOMA-assisted metasurface-assisted sensing network. Background Technology

[0002] With the rapid development of 5G and future 6G communication technologies, the demand for mobile communication network capacity and data rates continues to grow. Especially in high-density user environments, improving wireless resource utilization and signal quality has become a research hotspot. Traditional wireless communication systems rely on direct communication between base stations and users, but with increasing environmental complexity and more limiting factors for signal transmission, traditional methods are struggling to meet the ever-growing demands. Wireless communication security faces severe challenges, with eavesdropping being particularly prominent. In open wireless channels, unauthorized eavesdroppers can steal signal information, threatening the privacy and security of communications. Especially in emerging application scenarios such as the Internet of Things (IoT) and the Internet of Vehicles (IoV), the security of sensitive data transmission is crucial; therefore, improving system security has become an urgent need.

[0003] Reconfigurable Intelligent Surface (RIS) technology offers an emerging solution to the problem of obstructed direct connection between transmitters and receivers. RIS can regulate the propagation direction of wireless signals through programmable reflective elements, improving signal quality and coverage, thereby enhancing communication performance. On the other hand, Non-Orthogonal Multiple Access (NOMA) technology, through power allocation strategies, allows multiple users to share the same frequency band, improving spectrum utilization and system capacity. The combination of RIS and NOMA can further improve communication efficiency while reducing interference between users.

[0004] While RIS and NOMA technologies have made some progress in improving system performance and security, existing research mainly focuses on optimizing RIS-assisted systems and improving the multi-user access efficiency of NOMA. However, ensuring the robust security of RIS-assisted NOMA systems remains a challenge when eavesdroppers are present and Channel State Information (CSI) is imperfect. Traditional beamforming methods typically assume perfect and known CSI, but in real-world environments, CSI errors, time delays, and other factors can severely affect beamforming performance, reducing system reliability and security. Therefore, researching robust and secure beamforming methods for RIS-assisted NOMA systems under imperfect CSI conditions has significant theoretical and practical value. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a NOMA-based metasurface-assisted sensing and communication network optimization method. Considering constraints such as sensing performance threshold constraints, maximum base station transmit power constraints, SIC decoding order constraints, SIC successful execution constraints, minimum data rate constraints for legitimate users, RIS reflector phase shift constraints, and eavesdropper channel uncertainty set constraints, a system model is established for a NOMA-based RIS-assisted sensing and communication system with the optimization objective of maximizing system performance and security. Uncertain constraints are transformed into deterministic constraints through S-processes, and non-convex problems are transformed into convex problems using variable substitution, continuous convex approximation, Schur complement conditions, and semidefinite relaxation. An iterative robust and security-maximizing resource allocation method is proposed, and the optimal resource allocation solution is obtained by combining the semidefinite relaxation method and first-order Taylor expansion.

[0006] To address the problems existing in the prior art, this invention proposes a NOMA-based metasurface-assisted sensing integrated network resource optimization method. This method includes: constructing a robust security model for a RIS-assisted sensing integrated system; setting constraints and optimizing the RIS-assisted sensing integrated system robust security model based on these constraints to obtain a robust resource allocation model for the sensing integrated system; transforming the non-convex problem of the sensing integrated system robust resource allocation model into an equivalent convex problem; solving the convex problem to obtain the optimal solution; and constructing a network resource optimization scheme based on the optimal solution.

[0007] The beneficial effects of this invention are:

[0008] This invention improves system robustness and security under imperfect CSI conditions by optimizing beamforming strategies, ensuring signal strength for legitimate users while suppressing eavesdroppers' signal reception. Combined with NOMA technology, the invention effectively improves spectrum utilization and enhances system capacity in high-density user environments. Furthermore, with the assistance of RIS, the system achieves obstacle-avoidance communication, enhancing its adaptability to environmental changes and demonstrating high practical value and application prospects. Attached Figure Description

[0009] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0010] Figure 1 This is a flowchart of the present invention;

[0011] Figure 2 This is a comparison chart showing the satisfaction probability of the method of this invention with that of different methods. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0014] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0015] A metasurface-assisted synsensory network resource optimization method for NOMA, such as Figure 1 As shown, the method includes: constructing a robust security model for a RIS-assisted sensing integrated system; setting constraints and optimizing the robust security model based on the constraints to obtain a robust resource allocation model for a communication and sensing integrated system; converting the non-convex problem of the robust resource allocation model of the communication and sensing integrated system into an equivalent convex problem; solving the convex problem to obtain the optimal solution; and constructing a network resource optimization scheme based on the optimal solution.

[0016] In this embodiment, a method for optimizing a NOMA-assisted metasurface-based integrated sensing network includes the following steps:

[0017] S1: Construct a robust security model for a NOMA-enabled RIS-assisted sensing integrated system that considers channel uncertainty; this model includes a system equipped with... A dual-function base station with one antenna, one configuration RIS of a passive reflection unit There are one single-antenna NOMA user, multiple point targets, and one single-antenna eavesdropper who may intercept legitimate user information. The direct links from the dual-function base station to legitimate users and the eavesdropper are blocked by obstacles, forcing communication only through a RIS (Remote Information System).

[0018] S2: Considering the constraints of perception performance threshold, base station maximum transmit power, SIC decoding order, SIC successful execution, minimum data rate of legitimate users, RIS reflection unit phase shift, and eavesdropper channel uncertainty set, a robust resource allocation model for the integrated communication and perception system is established based on the bounded channel uncertainty model, with the optimization objective of maximizing the security rate.

[0019] S3: Use the S-process to transform uncertain constraints into deterministic constraints, and use methods such as variable substitution, continuous convex approximation, Schur complement conditions, and semidefinite relaxation to transform non-convex problems into equivalent convex problems.

[0020] S4: Use the CVX toolbox to solve the convex optimization problem and obtain the optimal beamforming vector, RIS phase shift, i.e. the optimal resource allocation scheme.

[0021] Furthermore, the system and security rate are based on Calculate, where, Indicates the first Throughput of each communication user This indicates the throughput of the eavesdropper.

[0022] Furthermore, the system's first The signal received by a legitimate user is:

[0023]

[0024] in, Indicates RIS to legitimate users The channel, This indicates the channel from the base station to the RIS. Indicates a legitimate user The mean is 0 and the variance is Background noise.

[0025] The system's first The signal-to-interference-to-noise ratio (SIR) for each legitimate user is:

[0026]

[0027] Among them, the The throughput of each communication user is based on calculate.

[0028] Furthermore, users in the NOMA system The specific order of decoding interference signals is determined by Provided. To ensure successful SIC execution, authorized users... The data rate for decoding its own signal must meet the following requirements. , .in For RIS to legitimate users The channel vector, This indicates the conjugate transpose. For base station to the first Beamforming vector of signals generated by a legitimate user For users The rate at which the signal is decoded. For users Decoding User The data rate achievable with the signal, For having A single-antenna NOMA user .

[0029] Furthermore, the signal received by the eavesdropper is .in, This represents the channel vector from the RIS to the eavesdropper. The mean at the location of the eavesdropper is 0 and the variance is 0. Additive white Gaussian noise. Therefore, the eavesdropper... The interference-to-noise ratio of the eavesdropping messages for a legitimate user is:

[0030]

[0031] Among them, eavesdroppers target legitimate users The throughput for eavesdropping is based on calculate.

[0032] Furthermore, the target echo signal received by the dual-function base station can be based on... Calculate; where, This represents the steering vector of the transmitting antenna. This indicates the azimuth angle of the target relative to the dual-function base station. This represents the normalized spacing between adjacent antennas. This represents the steering vector of the receiving antenna. This represents the complex amplitude and round-trip path loss, which are proportional to the radar cross-section of the target. This indicates that the mean at the base station receiver is 0 and the variance is... Additive white Gaussian noise.

[0033] Furthermore, the Cramer-Rao boundary of the sensing target relative to the dual-function base station is calculated according to the following formula:

[0034]

[0035] in, To transmit signals The covariance matrix, This indicates that the base station transmits to the user. beamforming vector, Indicates the length of the communication frame. This represents the complex amplitude and round-trip path loss, which are proportional to the radar cross-section of the target. To represent the background noise of the target receiver, The total steering vector of the base station, The direction angle of the target. , Let be the trace of the matrix.

[0036] Furthermore, the bounded CSI error between the dual-function base station and the eavesdropper models the channel uncertainty set as follows: .in, This represents the set of uncertainties in the eavesdropper cascaded channels. This represents the equivalent cascaded channel from the base station to the RIS and then to the eavesdropper. This represents the estimated channel vector. This indicates the corresponding estimation error. Represents the radius of the uncertain set.

[0037] Furthermore, the robust resource allocation model for the integrated communication and sensing system, which aims to maximize the number of legitimate users and the confidentiality rate, is as follows:

[0038]

[0039] in, For the sake of system security and confidentiality, For legitimate users The rate of confidentiality, To estimate the error, Indicates the CRLB threshold. This indicates the maximum transmit power of the base station. Indicates the first Minimum data rate threshold for a legal user; The phase shift angle of RIS. For the set of uncertainties in the eavesdropper's cascaded channels; To perceive performance constraints, To constrain the maximum transmit power of the base station, To enforce the order constraints for SIC, To successfully execute SIC constraints, Service quality constraints for each legitimate user For RIS reflector phase shift constraint, Let be the set of channel uncertainties for eavesdroppers.

[0040] Furthermore, in S3, the non-smooth problem is transformed into a smooth problem using the variable substitution method, specifically as follows:

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] Furthermore, in S3, the original uncertain non-convex optimization problem is transformed into a deterministic convex problem based on methods such as the S-process, the continuous convex approximation method, the Schur complement condition, and the semidefinite relaxation. Specifically:

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] in, , , Will Fractional constraints are decomposed into linear constraints ,in Is with the first Auxiliary variables related to individual user SINR: constraints , Will Fractional constraints are decomposed into linear constraints ,in It is an auxiliary variable related to the eavesdropper's SINR: constraint , In order to be with the first The auxiliary optimization variables related to the user's confidentiality rate transform the objective function into: maximizing ,pass The correlation rate difference can be addressed by using Schur compensation to adjust the constraints. Become ,in ; From base station to RIS and then to the 1st Equivalent channel matrix of each user link, The auxiliary variables for the SIC rate constraint are transformed into linear constraints. , To introduce auxiliary variables; For the first Additive Gaussian white noise at each legitimate user location; For the slack variable of the S process, for An identity matrix of dimension 1 ,definition , The upper limit of the channel estimation error for the eavesdropper; To constrain The auxiliary variable, Taylor expansion is and ; To sense the background noise variance of the target receiver. The length of the communication frame. For Kronecker product, For the first The user decodes the first Auxiliary decoupling variables related to user signals. It is a positive semidefinite constraint that guarantees the performance of perception; Total power constraint for base stations; For multi-user signal power coordination constraints, where For the first Useful signal power of each user ( For users (equivalent concatenated channel matrix). For the first The user was affected by the first Interference power per user. It is a joint constraint for eliminating serial interference in the modified NOMA system; For the first Minimum rate guarantee constraint for a legal NOMA user; It is a robust positive semidefinite constraint against channel uncertainty for eavesdroppers; It is an auxiliary condition that transforms the logarithmic constraint related to the confidentiality rate of legitimate users into a convex constraint. This ensures the energy non-negativity of beamforming and RIS phase; It is a rank-1 constraint, which guarantees the physical realizability of the solution.

[0059] Furthermore, an alternating optimization method is employed to fix the RIS phase shift. Then the base station dual-function beam The optimization subproblem is expressed as:

[0060]

[0061] Further, address the sub-problems constraints middle and There is a coupling problem, in order to decouple Introducing auxiliary variables ,but In order to decouple and Expanding it using a first-order Taylor series, i.e. Among them, superscript Instruction No. Approximate value of the next iteration yes Around the point The first-order Taylor expansion. Therefore, Rewritten as:

[0062]

[0063] Furthermore, decoupling Introducing auxiliary variables ,but Its first-order Taylor expansion is .in, yes Around the point The first-order Taylor expansion is:

[0064]

[0065] Furthermore, the problem It can be rephrased as:

[0066]

[0067] Furthermore, by employing the semidefinite relaxation technique, the problem... It is a convex problem and can be solved directly using the CVX tool.

[0068] Furthermore, fixed base station dual-function beamforming RIS phase shift The optimization subproblem can be expressed as:

[0069]

[0070] Similarly, processing and The problem lies in the coupling of variables. Rephrased as:

[0071]

[0072] Furthermore, by employing semidefinite relaxation techniques and Gaussian randomization methods, we obtained... The optimal solution is the method that maximizes both robustness and confidentiality.

[0073] Figure 1 The present invention provides a RIS-assisted integrated sensing system based on NOMA, as shown in the solution flowchart. Specifically, the system consists of a device equipped with… A dual-function base station with one antenna, one configuration RIS of a passive reflection unit It consists of a single-antenna NOMA user, multiple point targets, and a single-antenna eavesdropper that may intercept legitimate user information. The dual-function base station is... A legitimate user provides communication services while simultaneously detecting the location information of a target. The target is far from the RIS (Radio Router Array), therefore, the RIS is only used to enhance the signal strength of users within the communication coverage area. Channel modeling considers both large-scale and small-scale fading effects; large-scale fading is... .in , Indicates the distance between the transceivers. Is it set to Reference distance, This is the path loss exponent. The settings for the remaining system simulation parameters are shown in Table 1.

[0074] Table 1

[0075]

[0076] Figure 2 Different methods are given. The impact on the system's probability of satisfaction. From Figure 2 It can be seen that the system's probability of satisfying the condition changes with different methods. The increase is due to the increase in [something], because [something] increases. The larger the channel parameter, the worse the eavesdropper's decoding ability, thus reducing their threat and increasing the system's satisfaction probability. Furthermore, the satisfaction probability of the proposed method is significantly higher than that of OMA, because NOMA technology has more efficient resource allocation and power control capabilities than OMA, resulting in a 6.1% higher satisfaction probability. In addition, since the proposed method fully considers the uncertainty of channel parameters during design, its satisfaction probability is 2.2% higher than that of non-robust methods.

[0077] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing resources of a NOMA super-surface-assisted integrated sensing and communication network, characterized in that, The application relates to a network resource optimization method based on a RIS-assisted communication and sensing integrated system. A RIS-assisted communication and sensing integrated system robust security model is constructed; Constraint conditions are set, and the RIS-assisted communication and sensing integrated system robust security model is optimized based on the constraint conditions to obtain a communication and sensing integrated system robust resource allocation model; a non-convex problem of the communication and sensing integrated system robust resource allocation model is converted into an equivalent convex problem; The convex problem is solved to obtain an optimal solution; A network resource optimization scheme is constructed based on the optimal solution.

2. The method of claim 1, wherein the method is characterized by: The RIS-assisted communication and sensing integrated system robust security model comprises a dual-function base station equipped with N fillings, an RIS equipped with M passive reflection units, K single-antenna NOMA users, multiple point targets and a single-antenna eavesdropper.

3. The method of claim 1, wherein the method further comprises: The constraint conditions comprise a sensing performance threshold constraint, a base station maximum transmit power constraint, an SIC decoding order constraint, an SIC successful execution constraint, a legal user minimum data rate constraint, an RIS reflection unit phase shift constraint and an eavesdropper channel uncertainty set constraint.

4. The method of claim 3, wherein, The order of decoding interfering signals in SIC decoding order constraint is , the user decodes its own signal at a data rate satisfying , ; where is the channel vector from the RIS to the legitimate user , denotes the conjugate transpose, is the beamforming vector from the base station to the th legitimate user, is the rate at which the user decodes its own signal, is the achievable data rate when decoding the signal of the user at the user , is a NOMA user with single antennas.

5. The method of claim 3, wherein the method further comprises: The base station maximum transmit power constraint is used to calculate the Cramer-Rao bound of the base station, and the calculation formula is as follows: ； ； wherein is the covariance matrix of the transmitted signal is the beamforming vector transmitted by the base station to the user is the length of the communication frame is the complex amplitude proportional to the radar cross section of the target and the round trip path loss is the background noise representing the target receiver is the total steering vector of the base station is the direction angle of the target is the trace of the matrix.​​ 6. The method of claim 3, wherein the method further comprises: The channel uncertainty set constraint is as follows: ； ； ； ； wherein denotes the set of uncertainties of the eavesdropper cascaded channel, is the equivalent cascaded channel from the base station to the RIS and then to the eavesdropper, denotes the estimated channel vector, denotes the corresponding estimation error, denotes the radius of the set of uncertainties, denotes the RIS with passive reflecting elements, denotes the dual-functional BS with antennas, is the steering vector of the transmitting antenna, is the normalized separation between adjacent antennas; is the azimuth angle of the target, is the steering vector of the receiving antenna; is the total steering vector of the BS; is the complex amplitude proportional to the target radar cross section and the round-trip path loss.

7. The method of claim 1, wherein the method further comprises: The communication and sensing integrated system robust resource allocation model is as follows: ； wherein is the system and secrecy rate, is the secrecy rate for legitimate users , is the estimation error, denotes the CRLB threshold, denotes the maximum transmit power of the base station, denotes the minimum data rate threshold for the th legitimate user; is the phase shift angle of the RIS, is the set of eavesdropper cascaded channel uncertainties; is the sensing performance constraint, is the base station maximum transmit power constraint, is the SIC execution order constraint, is the successful execution of SIC constraint, is the quality of service constraint for each legitimate user, is the RIS reflecting unit phase shift constraint, is the set of eavesdropper channel uncertainties.

8. The method of claim 1, wherein, Converting the non-convex problem of the communication and sensing integrated system robust resource allocation model into an equivalent convex problem comprises: converting a non-smooth problem into a smooth problem by using a variable replacement method; converting an uncertain non-convex optimization problem in the smooth problem into a deterministic convex problem by using an S process, a continuous convex approximation method, a Schur complement condition and a semi-positive relaxation method.

9. The method of claim 8, wherein the method further comprises: The expression of the deterministic convex problem is as follows: ； ； ； ； ； ； ； ； ； ； where, is the auxiliary variable related to the SINR of the th user, is the auxiliary variable related to the SINR of the eavesdropper, is the auxiliary optimization variable related to the secrecy rate of the th user, is the equivalent channel matrix of the link from the base station to the RIS and then to the th user, is the auxiliary variable for the SIC rate constraint, which is converted to a linear constraint, is the auxiliary variable introduced for is the additive white Gaussian noise at the th legitimate user; are slack variables for S processes, is the dimensional identity matrix, is the upper bound of the channel estimation error of the eavesdropper; is the auxiliary variable for the constraint ; is the background noise variance of the cognitive target receiver, is the length of the communication frame, is the Kronecker product, is the auxiliary decoupling variable related to the decoding of the th user signal by the th user, is the semi-positive definite constraint to guarantee the cognitive performance; is the total power constraint of the base station; is the multi-user signal power coordination constraint, is the useful signal power of the th user, is the equivalent cascaded channel matrix of the user , is the interference power of the th user to the th user, is the joint constraint of successive interference cancellation in the NOMA system after transformation; is the minimum rate guarantee constraint of the th legitimate NOMA user; is the robust semi-positive definite constraint for the channel uncertainty of the eavesdropper; is the auxiliary condition to convert the logarithmic constraint related to the secrecy rate of the legitimate user into a convex constraint, is to ensure the energy non-negativity of the beamforming and RIS phase; is the rank 1 constraint.

10. The method of claim 1, wherein, Solving the convex problem comprises solving the convex optimization problem by using a CVX tool box to obtain an optimal beamforming vector and an RIS phase shift.

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