Intelligent reflecting surface assisted radar communication system spectrum compatibility method and device

Abstract

The application discloses a kind of intelligent reflecting surface assisted radar communication system spectrum compatibility method and system, method includes: build a system including radar, communication base station and intelligent reflecting surface, radar, communication base station and intelligent reflecting surface are connected with each other communication;The signal received by radar system receiving end and communication base station is modeled;Radar side mutual information and communication side mutual information are calculated using modeling results, with the weighted sum of radar side mutual information and communication side mutual information maximum as target and set to constraint, to formulate optimization problem;By fixing two of three variables to be solved in turn, the third variable is solved, the optimization problem is converted into three sub-problems, the sub-problems are simplified using MM algorithm, and then ADPM algorithm or EBCD algorithm is used for solving;The application has the advantages that: interference is cooperatively suppressed, spectrum resource allocation is efficient and compatible, optimization algorithm converges quickly, system deployment has low energy consumption and sufficient adaptability.

Description

Technical Field

[0001] This invention relates to the field of spectrum coexistence in radar communication systems, and specifically to a method and apparatus for spectrum compatibility of radar communication systems assisted by intelligent reflectors. Background Technology

[0002] Currently, the rapid development of wireless communication and radar sensing services has led to increasingly fierce competition for spectrum resources. To achieve efficient utilization of scarce spectrum resources, the coexistence technology of radar and communication systems on the same frequency has become a research hotspot in the field of information and communication engineering. For example, Chinese Patent Publication No. CN114660564A discloses a spectrum sharing configuration method for a radar-communication spectrum coexistence system. However, in existing technologies, the coexistence of radar and communication systems still faces the following problems:

[0003] Question 1: Radar and communication systems have poor coexistence performance and are susceptible to various types of interference.

[0004] Traditional technologies fail to effectively regulate the electromagnetic wave propagation environment, cannot specifically suppress clutter or alleviate interference between users, and are also unable to avoid mutual interference between radar and communication signals, leading to conflicts in spectrum resource utilization and making it difficult for the system to coexist stably in complex electromagnetic environments.

[0005] Problem 2: High system energy consumption, insufficient deployment flexibility and adaptability.

[0006] Traditional technologies often rely on active devices to improve system performance, which consume a lot of energy when they are working. Moreover, these devices have many limitations in terms of size and installation conditions, making it difficult to deploy them flexibly in common locations such as building exteriors and street light poles. They also cannot adjust their deployment methods according to complex environments, reducing the system's adaptability in different scenarios.

[0007] Question 3: Low spectrum resource allocation efficiency and poor spectrum compatibility.

[0008] Traditional technologies lack effective spectrum compatibility constraint mechanisms and efficient resource allocation strategies, making it impossible to dynamically and rationally allocate spectrum resources according to the actual needs of the system. They also fail to precisely control the spectrum radiation of radar and communication systems, resulting in low spectrum resource utilization efficiency and prominent mutual interference problems.

[0009] Question 4: The optimization algorithm has slow convergence speed and weak adaptability and robustness.

[0010] Traditional algorithms do not dynamically update and adjust key factors such as penalty parameters, making it difficult to quickly approach the optimal solution during the iteration process. At the same time, the algorithm structure design does not fully consider the changes in interference and parameter fluctuations in complex environments, which makes the algorithm performance susceptible to changes in the environment and unable to complete the optimization task stably and efficiently.

[0011] In summary, existing technologies suffer from several problems, including multiple interferences in complex electromagnetic environments (radar clutter interference, self-interference from multiple users in communication, and mutual interference between signals from two systems), inefficient spectrum resource allocation with poor compatibility, slow convergence and weak robustness of optimization algorithms, and high energy consumption and insufficient adaptability in system deployment. At the same time, they also have prominent pain points in specific scenarios, such as weak main lobe interference suppression, conflict between information transmission security and real-time performance, susceptibility to interference in ranging and velocity measurement accuracy, and poor stability of single intelligent reflector (IRS) links, which limit the overall collaborative performance. Summary of the Invention

[0012] The technical problem to be solved by this invention is that existing technologies suffer from multiple interference superpositions in complex electromagnetic environments, inefficient spectrum resource allocation with poor compatibility, slow convergence and weak robustness of optimization algorithms, and high energy consumption and insufficient adaptability in system deployment.

[0013] This invention solves the above-mentioned technical problems through the following technical means: a spectrum compatibility method for a smart reflector-assisted radar communication system, comprising:

[0014] S1. Build a system that includes a radar, a communication base station, and a smart reflector. The radar, communication base station, and smart reflector are interconnected. Model the signals received by the radar system receiver and the communication base station.

[0015] S2. Calculate the mutual information on the radar side and the mutual information on the communication side using the modeling results. With the goal of maximizing the weighted sum of the mutual information on the radar side and the mutual information on the communication side, set power constraints on the radar side's transmitting antenna, power constraints on the communication system's transmitting antenna, spectrum compatibility constraints on the radar side, power constraints on the communication system's transmitting antenna, and electromagnetic wave phase constraints on the smart reflector. Formulate an optimization problem by taking the diagonal phase shift matrix of the smart reflector, the spatiotemporal waveform corresponding to the pulse transmitted by the radar's transmitting antenna, and the codeword transmitted by the communication user as the three variables to be solved in the optimization problem.

[0016] S3. By fixing two of the three variables to be solved in sequence and solving the third variable, the optimization problem is transformed into three sub-problems. When the spatiotemporal waveform corresponding to the pulse transmitted by the radar transmitting antenna or the codeword transmitted by the communication user is used as the third variable, the corresponding sub-problem is simplified by the MM algorithm and then solved by the ADPM algorithm. When the diagonal phase shift matrix of the intelligent reflector is used as the third variable, the corresponding sub-problem is simplified by the MM algorithm and then solved by the ADPM algorithm or the EBCD algorithm.

[0017] This invention introduces a smart reflector to construct a joint design framework for radar-communication spectrum coexistence, breaking the limitations of single-system optimization and achieving collaborative interference suppression between the two systems, thus solving the problem of multiple interference superposition in complex electromagnetic environments. A spectrum compatibility constraint mechanism is established to ensure efficient spectrum resource allocation. Furthermore, combined with the passive, low-power, and easy-to-deploy characteristics of the smart reflector, resource utilization efficiency and adaptability to complex environments are improved, system deployment energy consumption is reduced, and system adaptability is enhanced. Optimization algorithms such as MM-ADPM and MM-EBCD are designed to overcome the difficulties in solving non-convex problems, improving convergence speed and adaptability.

[0018] Furthermore, the modeling of the radar system receiver is as follows:

[0019]

[0020] Among them, o R B represents the signal received by the radar system receiver, where s represents the spatiotemporal waveform during radar transmission; tR For the equivalent channel from the target to the radar, B tr For the equivalent channel from the target to the smart reflector, B rR If the equivalent channel from the smart reflector to the radar is B, then the overall channel is B. t,R =B tR +B rR Θ H B tr Θ is the diagonal phase shift matrix of the intelligent reflector. H Represents the Hermitian operator. b Rt (·) and b Rr (·) represent the radar's transmit steering vector and receive steering vector, respectively. D Let H represent the D-dimensional identity matrix, where ω0 represents the direction in which the radar detects the target; h1 represents the h1-th scattering point, and H1 represents the total number of scattering points. This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. This represents the clutter echo signal generated when the radar signal reaches the h1th scattering point and The h1-th scattering point is represented; k represents the k-th communication user; K represents the total number of communication users; G b,R Let G be the channel matrix of interference from the communication end to the radar end. b,R =G bR +F rR Θ H F br G bRFor the baseband equivalent channel from the base station to the radar, F br The baseband equivalent channel from the base station to the smart reflector, F rR From smart reflectors to radar baseband equivalent channels; n R This represents the additive noise at the radar end.

[0021] Furthermore, the formula for modeling the signal received by the communication base station is as follows:

[0022]

[0023] Among them, o C,k This represents the signal received by the k-th communication user at the communication terminal and the signal received by the communication base station. The equivalent channel between the base station, the user, and the smart reflector. H bu,k H is the equivalent channel from the base station to the k-th communication user. ru,k This is the equivalent channel from the intelligent transmitter to the k-th communication user; Let be the channel matrix of interference from the base station to the t-th target, and H bu,t H is the equivalent channel from the base station to the t-th target. ru,t Let be the equivalent channel from the intelligent reflector to the t-th target; Represents the equivalent channel between the radar, the user, and the smart reflector. This represents the equivalent channel for radar reaching the k-th user. This represents the equivalent channel from the smart reflector to the k-th user. This represents the equivalent channel of the radar reaching the smart reflector, where m1 represents the m1-th element of the smart reflector, M1 represents the total number of reflecting elements in the smart reflector, and n... X,k Let be the noise at the receiver of the k-th communication user.

[0024] Furthermore, the calculation of mutual information on the radar side and mutual information on the communication side using the modeling results includes:

[0025] The signal-to-interference-plus-noise ratio (SIR) at the radar receiver is given by the following formula.

[0026]

[0027] Among them, R scn ({x k},s,Θ) represents the covariance matrix of interference and noise at the radar receiver, and Let be the covariance matrix of point clutter interference and The variance of the additive noise at the radar end. Let be the covariance matrix of the interference in the communication system and Let w represent the variance of the additive noise at the radar end, and w be the optimal filter vector. Substituting this into the formula for the signal-to-interference-plus-noise ratio (SIR) at the radar receiver, we obtain the simplified form of the SIR at the radar receiver:

[0028]

[0029] Therefore, the mutual information on the radar side is expressed by the following formula.

[0030] MI1({x k},s,Θ)=logdet(I+R scn ({x k},s,Θ) -1 B t,R ss H B t,R H )

[0031] Where I represents the identity matrix;

[0032] The mutual information formula on the communication side is expressed as follows:

[0033]

[0034] Where D represents the D codewords sent by the communication user, R Cin,k Let the covariance matrix of noise and interference at the k-th user receiver be denoted as and This represents the variance of the noise at the k-th user's receiver. Let V be the variance matrix representing the interference at the k-th user receiver.

[0035] Furthermore, the formulation of the optimization problem includes:

[0036]

[0037] st||s|| 2 =P R

[0038] ||x k || 2 =P C k = 1, ..., K

[0039] s H Π m s≤E R,m m=1,...,M

[0040]

[0041] 0≤θ q ≤2π, q=1,…,Q,

[0042] Among them, κ1 and γ k Represent the non-negative weights assigned to radar and communication metrics, respectively. 2 P represents the square of the modulus. R P represents the power threshold of the transmitting antenna on the radar side. C The power threshold of the transmitting antenna of the communication system is represented by m, where m = 1, 2, 3...M, M represents the total number of electromagnetic radiators, h represents the h-th spectrum on the communication side, and H represents the total number of spectrums on the communication side; Π m Λ represents the total spectral energy on the radar side. h E represents the energy of the h-th spectrum on the communication side. R,m This indicates the upper limit of the energy constraint on the radar side. θ represents the upper limit of the energy constraint on the communication side. q Let represent the angle of the q-th passive reflective element of the intelligent reflective surface, where Q indicates that there are a total of Q passive reflective elements.

[0043] Furthermore, S3 includes:

[0044] By fixing variables s and Θ among the three variables to be solved, the variable x is solved. k The optimization problem is transformed into the first subproblem.

[0045]

[0046] Furthermore, S3 also includes:

[0047] By fixing the variable x among the three variables to be solved k And Θ, solve for variable s, and transform the optimization problem into a second subproblem.

[0048]

[0049] Furthermore, S3 also includes:

[0050] By fixing the variable x among the three variables to be solved k Given s, solve for the variable Θ, and transform the optimization problem into a third subproblem.

[0051]

[0052] The present invention also provides a spectrum-compatible device for a radar communication system assisted by a smart reflector, comprising:

[0053] The model building module is used to build a system that includes a radar, a communication base station, and a smart reflector, which are interconnected. It also models the signals received by the radar system receiver and the communication base station.

[0054] The problem formulation module is used to calculate the mutual information of the radar side and the mutual information of the communication side using the modeling results. The objective is to maximize the weighted sum of the mutual information of the radar side and the mutual information of the communication side. Power constraints are set for the radar side transmitting antenna, the communication system transmitting antenna, the radar side spectrum compatibility constraints, the communication system transmitting antenna, and the electromagnetic wave phase constraints of the smart reflector. The diagonal phase shift matrix of the smart reflector, the spatiotemporal waveform corresponding to the pulse transmitted by the radar transmitting antenna, and the codeword transmitted by the communication user are used as the three variables to be solved in the optimization problem.

[0055] The problem-solving module is used to solve the optimization problem by fixing two of the three variables to be solved in sequence and solving the third variable. This transforms the optimization problem into three subproblems. When the spatiotemporal waveform corresponding to the pulse transmitted by the radar's transmitting antenna or the codeword transmitted by the communication user is used as the third variable, the corresponding subproblem is simplified using the MM algorithm and then solved using the ADPM algorithm. When the diagonal phase shift matrix of the intelligent reflector is used as the third variable, the corresponding subproblem is simplified using the MM algorithm and then solved using the ADPM algorithm or the EBCD algorithm.

[0056] Furthermore, the modeling of the radar system receiver is as follows:

[0057]

[0058] Among them, o R B represents the signal received by the radar system receiver, where s represents the spatiotemporal waveform during radar transmission; tR For the equivalent channel from the target to the radar, B tr For the equivalent channel from the target to the smart reflector, B rR If the equivalent channel from the smart reflector to the radar is B, then the overall channel is B. t,R =B tR +B rR Θ H B tr Θ is the diagonal phase shift matrix of the intelligent reflector. H Represents the Hermitian operator. b Rt (·) and b Rr (·) represent the radar's transmit steering vector and receive steering vector, respectively. DLet H represent the D-dimensional identity matrix, where ω0 represents the direction in which the radar detects the target; h1 represents the h1-th scattering point, and H1 represents the total number of scattering points. This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. This represents the clutter echo signal generated when the radar signal reaches the h1th scattering point and The h1-th scattering point is represented; k represents the k-th communication user; K represents the total number of communication users; G b,R Let G be the channel matrix of interference from the communication end to the radar end. b,R =G bR +F rR Θ H F br G bR For the baseband equivalent channel from the base station to the radar, F br The baseband equivalent channel from the base station to the smart reflector, F rR From smart reflectors to radar baseband equivalent channels; n R This represents the additive noise at the radar end.

[0059] Furthermore, the formula for modeling the signal received by the communication base station is as follows:

[0060]

[0061] Among them, o C,k This represents the signal received by the k-th communication user at the communication terminal and the signal received by the communication base station. The equivalent channel between the base station, the user, and the smart reflector. H bu,k H is the equivalent channel from the base station to the k-th communication user. ru,k This is the equivalent channel from the intelligent transmitter to the k-th communication user; Let be the channel matrix of interference from the base station to the t-th target, and H bu,t H is the equivalent channel from the base station to the t-th target. ru,t Let be the equivalent channel from the intelligent reflector to the t-th target; Represents the equivalent channel between the radar, the user, and the smart reflector. This represents the equivalent channel for radar reaching the k-th user. This represents the equivalent channel from the smart reflector to the k-th user. This represents the equivalent channel of the radar reaching the smart reflector, where m1 represents the m1-th element of the smart reflector, M1 represents the total number of reflecting elements in the smart reflector, and n... X,k Let be the noise at the receiver of the k-th communication user.

[0062] Furthermore, the calculation of mutual information on the radar side and mutual information on the communication side using the modeling results includes:

[0063] The signal-to-interference-plus-noise ratio (SIR) at the radar receiver is given by the following formula.

[0064]

[0065] Among them, R scn ({x k},s,Θ) represents the covariance matrix of interference and noise at the radar receiver, and Let be the covariance matrix of point clutter interference and The variance of the additive noise at the radar end. Let be the covariance matrix of the interference in the communication system and Let w represent the variance of the additive noise at the radar end, and w be the optimal filter vector. Substituting this into the formula for the signal-to-interference-plus-noise ratio (SIR) at the radar receiver, we obtain the simplified form of the SIR at the radar receiver:

[0066]

[0067] Therefore, the mutual information on the radar side is expressed by the following formula.

[0068] MI1({x k},s,Θ)=logdet(I+R scn ({x k},s,Θ) -1 B t,R ss H B t,R H )

[0069] Where I represents the identity matrix;

[0070] The mutual information formula on the communication side is expressed as follows:

[0071]

[0072] Where D represents the D codewords sent by the communication user, R Cin,k Let the covariance matrix of noise and interference at the k-th user receiver be denoted as and This represents the variance of the noise at the k-th user's receiver. Let V be the variance matrix representing the interference at the k-th user receiver.

[0073] Furthermore, the formulation of the optimization problem includes:

[0074]

[0075] st||s|| 2 =P R

[0076] ||x k || 2 =P C k = 1, ..., K

[0077] s H Π m s≤E R,m m=1,...,M

[0078]

[0079] 0≤θ q ≤2π, q=1,…,Q,

[0080] Among them, κ1 and γ k Represent the non-negative weights assigned to radar and communication metrics, respectively. 2 P represents the square of the modulus. R P represents the power threshold of the transmitting antenna on the radar side. C The power threshold of the transmitting antenna of the communication system is represented by m, where m = 1, 2, 3...M, M represents the total number of electromagnetic radiators, h represents the h-th spectrum on the communication side, and H represents the total number of spectrums on the communication side; Π m Λ represents the total spectral energy on the radar side. h E represents the energy of the h-th spectrum on the communication side. R,m This indicates the upper limit of the energy constraint on the radar side. θ represents the upper limit of the energy constraint on the communication side. q Let represent the angle of the q-th passive reflective element of the intelligent reflective surface, where Q represents the Q-th passive reflective element.

[0081] Furthermore, the problem-solving module is also used for:

[0082] By fixing variables s and Θ among the three variables to be solved, the variable x is solved. k The optimization problem is transformed into the first subproblem.

[0083]

[0084] Furthermore, the problem-solving module is also used for:

[0085] By fixing the variable x among the three variables to be solved k And Θ, solve for variable s, and transform the optimization problem into a second subproblem.

[0086]

[0087] Furthermore, the problem-solving module is also used for:

[0088] By fixing the variable x among the three variables to be solved k Given s, solve for the variable Θ, and transform the optimization problem into a third subproblem.

[0089]

[0090] The advantages of this invention are:

[0091] (1) This invention introduces a smart reflector to construct a joint design framework for radar-communication spectrum coexistence, breaking the limitations of single-system optimization, achieving collaborative suppression of interference between the two systems, and solving the problem of multiple interference superposition in complex electromagnetic environments. A spectrum compatibility constraint mechanism is established to ensure efficient spectrum resource allocation. Furthermore, combined with the passive, low-power, and easy-to-deploy characteristics of the smart reflector, resource utilization efficiency and adaptability to complex environments are improved, system deployment energy consumption is reduced, and system adaptability is enhanced. Optimization algorithms such as MM-ADPM and MM-EBCD are designed to overcome the difficulties in solving non-convex problems, improving convergence speed and adaptability.

[0092] (2) This invention develops a multivariate joint optimization method that integrates IRS control. By dynamically adjusting the radar transmission signal, communication codebook and IRS phase matrix, the performance requirements of the two systems are balanced. At the same time, a robust framework against channel uncertainty and multiple interferences is constructed. Under the premise of satisfying power and spectrum constraints, the integrated design of "interference suppression-resource efficiency-performance robustness" is realized, providing a feasible technical solution for the coexistence of radar and communication systems in complex electromagnetic environments.

[0093] (3) This invention optimizes the overall performance of the coexistence system by introducing a smart reflector to dynamically regulate the electromagnetic wave propagation environment, effectively improving communication mutual information and radar detection signal-to-noise ratio. Taking into full account the various types of interference and channel state information uncertainties under complex electromagnetic environments, two optimization algorithms and a spectrum compatibility constraint framework are designed to significantly improve the system's performance stability in complex and variable environments. The proposed design method can flexibly adjust the weighting factors of radar and communication performance according to actual application requirements, achieving a precise trade-off between multi-user communication and radar detection functions, fully meeting the core requirements of different application scenarios. Numerical simulation verification shows that the MM-ADPM, MM-EBCD, and multivariate alternating optimization algorithms proposed in this invention have good convergence performance and can reach the optimal solution of the optimization problem in a short time. This invention provides a new approach and technical method for the design and application of smart reflector-assisted radar-communication coexistence systems, and is expected to play an important role in wireless communication and radar detection fields, powerfully promoting technological progress and innovative development in related fields, with broad application prospects.

[0094] (4) This invention optimizes detection accuracy, transmission security and link stability for specific scenarios such as main lobe interference and information security, and ultimately achieves efficient collaboration and robust operation of radar-communication system with IRS assistance.

[0095] (5) Compared with the prior art document (CN114660564A) described in the background art, the system architecture and core components of this application are different. This application introduces an IRS to construct a three-party system, including radar, communication base station, and IRS. The IRS achieves passive interference suppression by regulating the phase of electromagnetic waves, and has the characteristics of low power consumption and easy deployment. In contrast, the prior art document adopts a traditional joint optimization scheme without additional auxiliary equipment. It is based solely on the MIMO architecture of the radar and communication systems and relies on the optimization of the transmitter / receiver parameters to achieve coexistence. The optimization objectives and constraints of this application and the prior art document are different. This application aims to maximize the weighted sum of mutual information between the radar side and the communication side; the constraints cover power constraints, spectrum compatibility constraints, and IRS electromagnetic wave phase constraints, specifically addressing the problem of multiple types of interference superposition. The prior art document aims to maximize the overall mutual information of the system; the constraints include radar-side signal-to-noise ratio, waveform similarity, and transmit power constraints, focusing on robustness under signal direction mismatch. The optimization variables and solution logic of this application differ from those of the prior art. In this application, the optimization variables are the IRS diagonal phase shift matrix, radar spatiotemporal waveform, and communication user codewords. It employs an alternating strategy of "fixing two variables to solve for the third variable." Sub-problems are simplified using the MM algorithm and then solved using the ADPM or EBCD algorithm, overcoming the difficulties of non-convex problems. In contrast, the optimization variables in the prior art are the communication system codebook, radar waveform, and radar receiver filter weight vector. Through alternating iterations, these variables are transformed into three sub-problems, solved using a convex solver, the MVDR algorithm, and the Lagrange extremum method (KKT conditions), respectively, relying on traditional convex optimization tools. These differences lead to performance and applicability differences between this application and the prior art. This application is more suitable for complex electromagnetic environments, effectively suppressing clutter, user self-interference, and system mutual interference. It offers flexible deployment, low energy consumption, and stronger convergence speed and robustness. The prior art is suitable for scenarios with a single type of interference and is sensitive to deployment costs, requiring no additional hardware, but its resistance to multiple interferences is weaker, and its convergence efficiency depends on the performance of the convex optimization tools. Attached Figure Description

[0096] Figure 1 This is a flowchart of a spectrum compatibility method for a radar communication system assisted by an intelligent reflector, as disclosed in an embodiment of the present invention.

[0097] Figure 2 This is a curve showing the change of the objective function value with the number of iterations in a spectrum compatibility method for a radar communication system assisted by an intelligent reflector, as disclosed in an embodiment of the present invention.

[0098] Figure 3 This is a graph showing the change of the objective function value with the spectrum compatibility constraint in a spectrum compatibility method for a radar communication system assisted by an intelligent reflector, as disclosed in an embodiment of the present invention.

[0099] Figure 4The curve showing the change of the objective function value with the number of iterations in a spectrum compatibility method for a radar communication system assisted by an intelligent reflector disclosed in an embodiment of the present invention. Detailed Implementation

[0100] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, 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.

[0101] Example 1

[0102] With the increasing demand for radar and multi-user communication in complex electromagnetic environments, the interference problem between the two systems has become prominent. The radar is interfered with by clutter (echoes from Q scattering points) and communication signals, while communication is affected by self-interference from multiple users and radar spectrum encroachment, limiting the cooperative performance. IRS, with its advantages of being passive, low-power, and easy to deploy (adapted to building exteriors and streetlight poles), focuses echoes on the radar side to suppress clutter and beamforming at the communication end to resolve self-interference, becoming the key to solving the coexistence problem.

[0103] To construct the IRS-assisted coexistence model, the system adopts a multiple-input multiple-output (MIMO) architecture: radar (N R,t hair, N R,r (receiving antenna) far-field detection, base station (N) C,t (Transmitting antenna) serves K antenna users, IRS (N-element ULA linear array, phase shift matrix) Phase [0, 2π] adjustable, single reflection). Existing schemes have the following limitations: single-variable optimization (e.g., fixing Θ and adjusting s or x). k ), must meet the power requirement (radar ||s||) 2 =P R , communication||x k || 2 =P C For problems involving k = 1, ..., K) and spectral constraints, the suboptimal solution method for non-convex problems (ADMM / SDR) converges slowly, and channel state deviations lead to performance degradation.

[0104] This invention focuses on the joint design of IRS-assisted spectrum coexistence, aiming to maximize the weighted sum of radar-communication mutual information, and uses alternating optimization to decompose the non-convex problem: MM framework s / x kA convex surrogate function is used, and ADPM handles the constraints; Θ(|Θ_nn|=1) is solved using MM-ADPM / EBCD. Simulation verification shows that the three-variable optimization objective value (MM-ADPM reaches 2.154 nats) surpasses that of single / bivariable optimization, and its resistance to tightened spectral constraints, increased frequency bands, and enhanced interference is superior to ADMM / SDR. Figure 1 As shown, the present invention provides a spectrum compatibility method for a radar communication system assisted by a smart reflector, comprising the following steps:

[0105] S1. Building the system model

[0106] Consider a smart reflector (IRS)-assisted radar communication coexistence system employing a uniform linear array (ULA). The system includes one equipped with N R,t One transmitting antenna and N R,r A MIMO radar with one receiving antenna, and one with N C,t A communication base station with N transmitting antennas, and K communication base stations each containing N C,rk A multi-antenna communication user with one receiving antenna is simultaneously deployed in an IRS containing Q passive reflector elements. The communication base station provides services to K multi-antenna communication users within the same frequency band. The IRS, as a programmable passive intermediary, serves both radar sensing and communication transmission functions. Specifically, the MIMO radar's transmitted signal is intelligently reflected by the IRS, and the downlink signal from the communication base station is also reflected by the IRS. However, due to spectrum sharing, there are direct mutual interference links between the radar and communication systems. Therefore, the essence of the entire system lies in jointly optimizing the diagonal phase shift matrix of the intelligent reflector, the spatiotemporal waveform corresponding to the pulses transmitted by the radar's transmitting antenna, and the codewords transmitted by the communication users. This allows the IRS to strengthen its respective useful signal path while simultaneously achieving coordinated interference suppression between the two systems, solving the problem of multiple interference superposition in complex electromagnetic environments, and achieving an overall synergistic improvement in radar and communication performance. The radar is used for far-field target detection, and the IRS uses the diagonal phase shift matrix... Dynamically adjust the phase of electromagnetic waves, considering only a single reflection scenario, I D Let θ represent a D-dimensional identity matrix. Q This represents the angle of the Qth passive reflective element of the smart reflective surface. Let represent a complex space of QD×QD dimensions. Within the pulse repetition interval (PRI), each transmitting antenna of the radar transmits D pulses, whose spatiotemporal waveform is represented as follows. s(D) represents the Dth pulse transmitted by the radar's transmitting antenna. Each communication user transmits D codewords within the pulse repetition interval (PRI) (acting as the transmitter at this time). The codeword transmitted to the kth communication user is defined as x. kAt this point, the k-th user is the receiver, receiving specific codewords for itself. That is, the communication user has both a transmitter and a receiver, transmitting D codewords while also receiving the corresponding codewords.

[0107] The modeling of the radar system receiver is given by the following formula:

[0108]

[0109] The first item represents the echo reflected from the target detected by the radar. R The signal received by the radar system receiver is represented by s, and the spatiotemporal waveform during radar transmission is represented by s. The equivalent channels from the target to the radar, from the target to the IRS, and from the IRS to the radar are modeled as follows: and The overall channel is B. t,R =B tR +B rR Θ H B tr ,in,() H Represents the Hermitian operator. b Rt (·) and b Rr (·) represent the radar's transmit steering vector and receive steering vector, respectively, and ω0 represents the direction in which the radar detects the target. The second term o s This represents the echo of the radar signal reflected from H1 scattering points. This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. in, This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. This represents the clutter echo signal generated when the radar signal reaches the h1th scattering point and After being reflected, it reaches the radar receiving antenna. This indicates the position of the h1th scattering point. The third term, o... c This characterizes the mutual interference caused to the radar received signal by the signal transmitted by the user after being relayed by the base station (BS). Furthermore, the baseband equivalent channels from the base station to the radar, from the base station to the IRS, and from the IRS to the radar are respectively represented by... Interference from communication user transmitted signals received by the radar receiver can be divided into two categories: one is interference from signals relayed directly from the BS to the radar receiver, and the other is interference arriving at the radar receiver via the IRS. Therefore, the channel matrix G of the interference from the communication end to the radar end is... b,R =G bR +F rR Θ H F br Furthermore, n RThis represents the additive noise at the radar end, with variance .

[0110] Similar to the receiving model of a radar system, the signal received by the k-th communication user at the communication end can be represented as...

[0111]

[0112] o C,k This represents the signal received by the k-th communication user at the communication terminal and the signal received by the communication base station. Its first term represents the direct signal received by the k-th communication user from the base station and the signal emitted by the base station and reflected by the IRS. Therefore, the equivalent channels from the base station to the k-th communication user, from the base station to the IRS, and from the IRS to the k-th communication user can be represented as follows: and The equivalent channel between the base station, the user, and the smart reflector is: Similarly, the equivalent channels from the base station to the t-th target and from the IRS to the t-th target can be represented as H, respectively. bu,t H ru,t Then the channel matrix for interference from the base station to the t-th target is: This represents the equivalent channel between the radar, the user, and the smart reflector. This represents the equivalent channel for radar reaching the k-th user. This represents the equivalent channel from the smart reflector to the k-th user. Let m1 represent the equivalent channel from the radar to the smart reflector, m1 represent the m1-th element of the smart reflector, and M1 represent the total number of reflector elements of the smart reflector. The second and third terms represent the interference caused by the remaining K-1 users to the k-th communication user and the impact of the path echo of the radar transmitted signal on the user's receiver, respectively. Let be the noise at the receiver of the k-th communication user.

[0113] S2. Formulate optimization problems

[0114] The SINR of the radar receiver can be given by the following formula.

[0115]

[0116] in, The covariance matrices representing interference and noise at the radar receiver, the covariance matrix of point clutter interference, and the covariance matrix of communication system interference are given by... as well as To express. This represents the variance of the additive noise at the radar end. Subsequently, the SINR maximization criterion is used to obtain the optimal filter vector. Substituting it into formula (3), we can obtain:

[0117]

[0118] Therefore, the mutual information on the radar side can be expressed by the following formula.

[0119]

[0120] Where I represents the identity matrix.

[0121] The mutual information formula on the communication side can be characterized as follows:

[0122]

[0123] Among them, R Cin,k Let the covariance matrix of noise and interference at the k-th user receiver be denoted as and This represents the variance of the noise at the k-th user's receiver. Let V be the variance matrix representing the interference at the k-th user receiver.

[0124] In this section, a model for the coexistence of the IRS-assisted radar and communication system spectrum is established. Simultaneously, considering the limited transmit power of the radar system, the power of the radar-side transmit antenna is limited, i.e., ||s|| 2 =P R , where || || 2 P represents the square of the modulus. R This indicates the power threshold of the transmitting antenna on the radar side.

[0125] Furthermore, to ensure the coexistence of the radar system with licensed communication systems in the surrounding spectrum, spectrum compatibility constraints need to be incorporated into the optimization model. In other words, it is necessary to ensure that the radar system does not cause excessive interference to nearby licensed communication systems when transmitting signals. For the radar system, the t-th... R The d-th pulse transmitted by the antenna can be represented as: Therefore, the total energy of the radar transmitted beam in different directions and M different frequency bands can be expressed as:

[0126]

[0127] Here, and These represent the minimum and maximum frequency limits for the m-th surrounding electromagnetic radiator, respectively. and These represent the minimum and maximum angular limits for the m-th surrounding electromagnetic radiator, respectively, and each electromagnetic radiator is assumed to be located within the normalized space. d1 and d2 represent the t-th time respectively.R The d1-th and d2-th pulses emitted by N antennas are given, where θ represents the angle of the electromagnetic radiator and f represents the frequency of the electromagnetic radiator. To calculate and constrain the total spectral energy emitted by all radar antennas, the dimension needs to be expanded to include all N antennas. R,t One antenna, therefore we can obtain The submatrix representing the total spectral energy on the radar side, Π m Let represent the total spectral energy on the radar side. Then, the spectral compatibility constraints on the radar side are expressed as: Similarly, the power constraint of the transmitting antenna of the communication system is established as ||x k || 2 =P C ,k=1,...,K. Definition Λ represents the h-th spectral energy submatrix on the communication side. h This represents the energy of the h-th spectrum on the communication side. Indicates a dimension of N C,t The identity matrix, P C Let the power threshold of the transmitting antenna of the communication system be represented, then the spectrum compatibility constraint on the communication side can be expressed as: for have

[0128]

[0129] in, d1 and d2 represent the lowest and highest frequency limits of the h-th spectrum, respectively, and d3 and d4 represent the highest frequency limits of the t-th spectrum, respectively. R The d3rd and d4th pulses transmitted by the antenna Let represent the minimum and maximum angle limits of the h-th spectrum, respectively, and let c represent the communication system.

[0130] The final model of the IRS-assisted radar and communication coexistence system is shown below:

[0131]

[0132] Among them κ1 and γ k Representing the non-negative weights assigned to radar and communication metrics respectively, m represents the m-th electromagnetic radiator, m = 1, 2, 3...M, where M represents the total number of electromagnetic radiators, E R,m This indicates the upper limit of the energy constraint on the radar side. θ represents the upper limit of the energy constraint on the communication side. qLet represent the angle of the q-th passive reflective element of the smart reflective surface, and Q represent the q-th passive reflective element. Considering the non-convexity of the overall optimization problem, the MM framework, ADPM algorithm, and BCD scheme are designed to optimize the variables. Among them, compared with the traditional ADMM scheme, ADPM incorporates the updating of penalty parameters, resulting in faster convergence speed and better adaptability and robustness.

[0133] S3. Solving the optimization problem

[0134] (1){x k}design

[0135] First, we need to fix the variables s and Θ, and then optimize the variable {x}. k Then the subproblem can be formulated as

[0136]

[0137] Note 1. The proposed formula reveals its non-convexity, making a direct solution a challenging task. However, it is clear that the radar side... Compared to It exhibits convexity, while the communication side Showing about (X) k ,R Cin,k ) is a joint convex.

[0138] Lemma 1. In the MM method, the algorithm does not directly maximize MI, but optimizes a series of approximate objective functions, which can be simplified to f(x). Defined as x k The set of constraints in the design. In summary, the surrogate function. The following conditions must be met to claim convergence.

[0139]

[0140] Proposition 1: For any x satisfying the constraints of problem (10) k and The surrogate function of the objective function in problem (10) It can be represented as

[0141]

[0142] in

[0143] By incorporating the positive semi-definite criterion of the matrix, the objective function can be rewritten as follows: Right now

[0144]

[0145] Then, the ADPM algorithm was proposed to solve the problem (13). Let A positive semi-definite matrix Decomposition. Define variable x. k Z and its corresponding real-valued form are Z r , Therefore, the above problem can be written with an auxiliary variable f. h and the essential form of d

[0146]

[0147] The corresponding Lagrange form is

[0148]

[0149] Where, μ h χ is a Lagrange multiplier, and ρ1 and ρ2 are penalty parameters. Next, we need to solve each variable individually.

[0150] ① Update variables

[0151]

[0152] The first-order optimality condition for problem (16) is:

[0153]

[0154] Then, we can get The solution is as follows:

[0155]

[0156] ② For any given h = 1, ..., H, update variable f h .

[0157]

[0158] This problem is a convex problem, therefore the optimal solution can be obtained as follows: Considering spectrum compatibility constraints, the final result is

[0159]

[0160] ③ Update variable d.

[0161]

[0162] Similarly, it is also a convex problem, and its optimal solution is After taking power constraints into account, we can obtain

[0163]

[0164] ④ Update μ h And χ.

[0165]

[0166] ⑤ Update ρ l , where l = 1, 2.

[0167]

[0168] Here For l = 1, 2, 0 < ζ l1 <1, ξ l2 >1.

[0169] (2)s design

[0170] With solving variable x k Similarly, we will now solve for the variable s. The variable x is fixed. k With Θ and optimization variable s, the original objective function is reduced to subproblems.

[0171]

[0172] Let S = ss H and

[0173] Note that when x << 1, log(1+x) can be well approximated by x. Therefore, if the interference scenario satisfies The MI1 at the radar receiver can be approximated as a trace.

[0174]

[0175] Obviously, it can be seen that λ j For matrix The eigenvalues ​​of . Given For (S,R) scn ) is a joint convex structure, therefore in Perform a first-order Taylor expansion of the function to obtain its lower bound.

[0176]

[0177] in,

[0178]

[0179] Next, we approximate MI2 on the communication side using a first-order Taylor expansion, and the simplified form is as follows:

[0180]

[0181] Here,

[0182] Furthermore, define The objective function is simplified to a quadratic form.

[0183]

[0184] in,

[0185] Similarly, based on the positive semi-definite standard of the matrix, formula (31) is expressed as

[0186]

[0187] If the positive semidefinite matrix Π m Decomposed into Define variables s, matrix Y, and The real-valued form of s is r Y r ,as well as Therefore, it has an auxiliary variable g. m The first-order Lagrange form of problem (32) with r is:

[0188]

[0189] ① Update variable s r .

[0190]

[0191] The first-order optimality condition for problem (34) is:

[0192]

[0193] Therefore, we can obtain the variable s. r The optimal solution is

[0194]

[0195] ② For any given m = 1, ..., M, update variable g m .

[0196]

[0197] Because the formula is convex, its optimal solution... It can be obtained, and considering spectrum compatibility constraints, the final result is...

[0198]

[0199] ③ Update variable r.

[0200]

[0201] You can get it afterward. Considering the power constraint, the iterative formula for variable r is as follows:

[0202]

[0203] ④ Update γ m and ε.

[0204]

[0205] ⑤ For l = 3, 4, update ρ l .

[0206]

[0207] Here, residual term For l = 3, 4, 0 < ζ l3 <1, ξ l4 >1.

[0208] (3) Θ Design

[0209] Then, fix the variable {x} k} and s, update variable Θ, the subproblem can be characterized as follows

[0210]

[0211] Proposition 2: The weighted summation scheme of the problem (44) concerning Θ can be written as

[0212]

[0213] Among them, κ θ It is a term independent of θ. The proofs for T, v, and w are as follows.

[0214] Proof: According to Note 1, Compared to (B) t,R ,R scn The joint convexity, therefore, in The first-order Taylor expansion expression at can be obtained, i.e.

[0215]

[0216] in,

[0217] Similarly, On the communication side It is a joint convex shape.

[0218]

[0219] in,

[0220]

[0221] Then, these two parts are merged and simplified into expressions about the variable Θ, resulting in matrices V and W. v and w are set as the sets of diagonal elements of matrices V and W, respectively. Then, let Q... D To replace QD. In other words, and in

[0222]

[0223] Next, the diagonal elements of matrix Θ are represented as Then define Therefore, we can obtain the quadratic form of the variable θ.

[0224]

[0225] in,

[0226]

[0227] This concludes the proof.

[0228] In addition, due to It is a unit complex number with a modulus of 1. Therefore, problem (51) can be equivalently written as:

[0229]

[0230] In this section, y, The real-valued forms of T, v, and w are defined as y r , T r v r and w r Subsequently, the ADPM and MM-EBCD algorithms were proposed to address the problem (53).

[0231] ①ADPM algorithm

[0232] The expression for problem (5) can be restated in real-valued form. Therefore, the corresponding Lagrange formula can be derived.

[0233]

[0234] a. Update variable y r

[0235]

[0236] The first-order optimality condition of problem (55) is:

[0237]

[0238] therefore,

[0239]

[0240] b. Update variables

[0241]

[0242] After taking constraints into account, equation (58) is equivalent to It has a closed solution

[0243]

[0244] c. Update β.

[0245]

[0246] d. Update ρ5.

[0247]

[0248] in,

[0249] ②MM-EBCD algorithm

[0250] To address problem (51), the MM-EBCD algorithm is applied. Definition Where λ max It is the largest eigenvalue of matrix T, and its real-valued form is represented as In the l3rd iteration, for the solution and any feasible It can be obtained The upper realm

[0251]

[0252] because Since is a constant, then the first and third terms of (62) are also constants. Therefore, the problem to be solved can be rewritten in the following form.

[0253]

[0254] Here,

[0255] Therefore, problem (63) can be reformulated as

[0256]

[0257] The closed-form solution can be obtained as follows:

[0258]

[0259] In this embodiment, the parameter settings will be described in detail and the simulation results will be presented. Consider a centralized MIMO radar system using a uniform linear array, the configuration of which includes N R,r =4 receiving antennas and N R,t =4 transmitting antennas. The communication base station is equipped with N C,t =5 transmitting antennas, with two single-antenna receivers. The detection target is set at 0°, with a signal-to-noise ratio (SNR) of 10dB. Considering clutter effects, H1 = 3 clutter sources are set, with an interference-to-noise ratio (CNR) of 30dB. The transmission angle is randomly selected from [25°, 40°], and the reception angle is selected from [-65°, -50°]. The incident angle [45°, 60°] of the intelligent reflector (IRS) is the reflection angle range [-60°, -45°]. Furthermore, the interference matrix of the communication system to the radar system (interference-to-noise ratio INR1 is 10dB) is generated as follows: the transmission angle is selected from [-45°, -30°], the reception angle is selected from [45°, 60°], the IRS incident angle is selected from [-55°, -40°], and the reflection angle is selected from [40°, 55°]. Conversely, the interference matrix of the radar system to the communication system (interference-to-noise ratio INR2 is 10dB) is generated by selecting the transmission angle from [45°, 60°], the reception angle from [-45°, -30°], the IRS incident angle from [35°, 50°], and the reflection angle from [-50°, -35°]. The noise variance of the communication and radar systems is set to... The transmit antenna power constraint is P R =P C =1. To ensure spectrum compatibility, constraint E is applied. R,m =E Ck,h =0.1. The operating frequency band of the communication system is... to Angle range to Radar system configuration frequency band to Angle range to The parameters are set to κ1 = 0.5 and γ. k = (1-κ1) / K.

[0260] exist Figure 2In the study, a single iteration with one univariate variable, three alternating iterations with two variables, and two iterations with three variables using MM-ADPM and MM-EBCD methods were selected to verify the convergence performance. It can be seen that the curves all tend to converge. Furthermore, when iterating over variables, regardless of whether MM-ADPM or MM-EBCD is used for x... k and The target value converged by alternating iterations with two variables is always higher than that obtained by using either variable alone. The objective value converged to the single-variable iteration, while the convergence objective values ​​obtained by iterating using the MM-ADPM and MM-EBCD methods respectively were higher than the objective values ​​obtained by alternating iterations with two different variables. This reflects that, with a fixed degree of freedom, increasing the design of variables can improve the value of the objective function. Finally, for x... k s and The optimal target value was achieved through joint design. The target value achieved using the MM-ADPM method was 2.154, while the target value achieved using the MM-EBCD method was 2.075, which is close to the ideal upper bound, i.e., the target value of 2.3979 achieved when the signal-to-noise ratio is 10dB.

[0261] Figure 3 These are the residual curves for different constraints on different variables, showing the convergence of the internal iterations to demonstrate the effectiveness of iterating over different constraints. Here, l1, l2, and l3 represent x... k s and The number of internal iterations when using the MM-ADPM method is set to 50. Figure 3 In (a), Represents variable x k Regarding the residuals related to spectrum compatibility constraints, and e d Represents variable x k Regarding the residuals related to power constraints. Similarly, in Figure 3 In (b), This represents the residual of variable s with respect to spectral compatibility constraints, while e r This represents the residual of variable s with respect to the power constraint. Figure 3 In (c), e y This represents a variable. Regarding the residuals related to the unit modulus constraint, it can be seen that the curves in these three figures have all converged effectively.

[0262] Figure 4The results show that the objective value obtained through three-variable iterative optimization is higher than that obtained through two-variable iterative optimization, and the value obtained through alternating two-variable iteration is higher than that obtained through single-variable iteration. This verifies that increasing the number of optimization variables can also improve the objective function value. In the two-variable iterative optimization case, compared with other two-variable optimization strategies, x k and Simultaneous optimization yielded better results. The theoretical upper limit for a signal-to-noise ratio of 0dB is 0.6931. Compared to the MM-ADPM algorithm, the MM-EBCD algorithm achieved a better target value. The results obtained by using the MM-EBCD algorithm and the MM-ADPM algorithm for three-variable alternating iterative optimization were 0.4217 and 0.4198, respectively.

[0263] In summary, this invention studies the joint design of a radar and multi-user communication system coexistence scheme with IRS-assisted spectrum compatibility. Transmit power and spectrum compatibility are imposed as constraints, and the mutual information between the radar and communication systems is used as a performance indicator. The original complex non-convex problem is decomposed into three separate subproblems for iterative solution. Furthermore, MM-ADPM and MM-EBCD algorithms are proposed to address the constant mode constraint imposed by the IRS. Finally, simulations demonstrate the performance improvement of the radar-communication coexistence system brought about by the introduction of the IRS, indicating that increasing the degrees of freedom of variables can improve the performance of the coexistence system to some extent. Furthermore, convergence analysis verifies the effectiveness of the proposed design. Moreover, a comparison with the ADMM and SDR algorithms shows the advantages of the two proposed algorithms in handling spectrum compatibility. Finally, this chapter compares and analyzes the performance of the IRS-assisted coexistence system at low signal-to-interference-plus-noise ratio (SINR), showing that the proposed design remains effective in this scenario.

[0264] Example 2

[0265] Based on Embodiment 1, Embodiment 2 of the present invention also provides a spectrum compatibility device for a radar communication system with intelligent reflector assistance, comprising:

[0266] The model building module is used to build a system that includes a radar, a communication base station, and a smart reflector, which are interconnected. It also models the signals received by the radar system receiver and the communication base station.

[0267] The problem formulation module is used to calculate the mutual information of the radar side and the mutual information of the communication side using the modeling results. The objective is to maximize the weighted sum of the mutual information of the radar side and the mutual information of the communication side. Power constraints are set for the radar side transmitting antenna, the communication system transmitting antenna, the radar side spectrum compatibility constraints, the communication system transmitting antenna, and the electromagnetic wave phase constraints of the smart reflector. The diagonal phase shift matrix of the smart reflector, the spatiotemporal waveform corresponding to the pulse transmitted by the radar transmitting antenna, and the codeword transmitted by the communication user are used as the three variables to be solved in the optimization problem.

[0268] The problem-solving module is used to solve the optimization problem by fixing two of the three variables to be solved in sequence and solving the third variable. This transforms the optimization problem into three subproblems. When the spatiotemporal waveform corresponding to the pulse transmitted by the radar's transmitting antenna or the codeword transmitted by the communication user is used as the third variable, the corresponding subproblem is simplified using the MM algorithm and then solved using the ADPM algorithm. When the diagonal phase shift matrix of the intelligent reflector is used as the third variable, the corresponding subproblem is simplified using the MM algorithm and then solved using the ADPM algorithm or the EBCD algorithm.

[0269] Specifically, the modeling of the radar system receiver is as follows:

[0270]

[0271] Among them, o R B represents the signal received by the radar system receiver, where s represents the spatiotemporal waveform during radar transmission; tR For the equivalent channel from the target to the radar, B tr For the equivalent channel from the target to the smart reflector, B rR If the equivalent channel from the smart reflector to the radar is B, then the overall channel is B. t,R =B tR +B rR Θ H B tr Θ is the diagonal phase shift matrix of the intelligent reflector. H Represents the Hermitian operator. b Rt (·) and b Rr (·) represent the radar's transmit steering vector and receive steering vector, respectively. D Let H represent the D-dimensional identity matrix, where ω0 represents the direction in which the radar detects the target; h1 represents the h1-th scattering point, and H1 represents the total number of scattering points. This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. This represents the clutter echo signal generated when the radar signal reaches the h1th scattering point and The h1-th scattering point is represented; k represents the k-th communication user; K represents the total number of communication users; G b,R Let G be the channel matrix of interference from the communication end to the radar end. b,R =G bR +F rR Θ H F br G bR For the baseband equivalent channel from the base station to the radar, F br The baseband equivalent channel from the base station to the smart reflector, F rR From smart reflectors to radar baseband equivalent channels; n R This represents the additive noise at the radar end.

[0272] More specifically, the formula for modeling the signal received by the communication base station is as follows:

[0273]

[0274] Among them, o C,k This represents the signal received by the k-th communication user at the communication terminal and the signal received by the communication base station. The equivalent channel between the base station, the user, and the smart reflector. H bu,k H is the equivalent channel from the base station to the k-th communication user. ru,k This is the equivalent channel from the intelligent transmitter to the k-th communication user; Let be the channel matrix of interference from the base station to the t-th target, and H bu,t H is the equivalent channel from the base station to the t-th target. ru,t Let be the equivalent channel from the intelligent reflector to the t-th target; Represents the equivalent channel between the radar, the user, and the smart reflector. F m1Ru,k This represents the equivalent channel for radar reaching the k-th user. This represents the equivalent channel from the smart reflector to the k-th user. This represents the equivalent channel of the radar reaching the smart reflector, where m1 represents the m1-th element of the smart reflector, M1 represents the total number of reflecting elements in the smart reflector, and n... X,k Let be the noise at the receiver of the k-th communication user.

[0275] More specifically, the calculation of mutual information on the radar side and mutual information on the communication side using the modeling results includes:

[0276] The signal-to-interference-plus-noise ratio (SIR) at the radar receiver is given by the following formula.

[0277]

[0278] Among them, R scn ({x k},s,Θ) represents the covariance matrix of interference and noise at the radar receiver, and Let be the covariance matrix of point clutter interference and The variance of the additive noise at the radar end. Let be the covariance matrix of the interference in the communication system and Let w represent the variance of the additive noise at the radar end, and w be the optimal filter vector. Substituting this into the formula for the signal-to-interference-plus-noise ratio (SIR) at the radar receiver, we obtain the simplified form of the SIR at the radar receiver:

[0279]

[0280] Therefore, the mutual information on the radar side is expressed by the following formula.

[0281] MI1({x k},s,Θ)=logdet(I+R scn ({x k},s,Θ) -1 B t,R ss H B t,R H )

[0282] Where I represents the identity matrix;

[0283] The mutual information formula on the communication side is expressed as follows:

[0284]

[0285] Where D represents the D codewords sent by the communication user, R Cin,k Let the covariance matrix of noise and interference at the k-th user receiver be denoted as and This represents the variance of the noise at the k-th user's receiver. Let V be the variance matrix representing the interference at the k-th user receiver.

[0286] More specifically, the optimization problem includes:

[0287]

[0288] st||s|| 2 =P R

[0289] ||x k || 2 =P C k = 1, ..., K

[0290] s H Π m s≤E R,m m=1,...,M

[0291]

[0292] 0≤θ q ≤2π, q=1,…,Q,

[0293] Among them, κ1 and γ k These represent the non-negative weights assigned to radar and communication metrics, respectively. 2 P represents the square of the modulus. R P represents the power threshold of the transmitting antenna on the radar side. C The power threshold of the transmitting antenna of the communication system is represented by m, where m = 1, 2, 3...M, M represents the total number of electromagnetic radiators, h represents the h-th spectrum on the communication side, and H represents the total number of spectrums on the communication side; Π m Λ represents the total spectral energy on the radar side. h E represents the energy of the h-th spectrum on the communication side. R,m This indicates the upper limit of the energy constraint on the radar side. θ represents the upper limit of the energy constraint on the communication side. q Let represent the angle of the q-th passive reflective element of the intelligent reflective surface, where Q represents the Q-th passive reflective element.

[0294] More specifically, the problem-solving module is also used for:

[0295] By fixing variables s and Θ among the three variables to be solved, the variable x is solved. k The optimization problem is transformed into the first subproblem.

[0296]

[0297] More specifically, the problem-solving module is also used for:

[0298] By fixing the variable x among the three variables to be solved k And Θ, solve for variable s, and transform the optimization problem into a second subproblem.

[0299]

[0300] More specifically, the problem-solving module is also used for:

[0301] By fixing the variable x among the three variables to be solved k Given s, solve for the variable Θ, and transform the optimization problem into a third subproblem.

[0302]

[0303] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A spectrum compatibility method for a radar communication system assisted by an intelligent reflector, characterized in that, include: S1. Build a system that includes radar, communication base station and intelligent reflector, and the radar, communication base station and intelligent reflector are interconnected. Model the signals received by the radar system receiver and the communication base station; S2. Calculate the mutual information on the radar side and the mutual information on the communication side using the modeling results. With the goal of maximizing the weighted sum of the mutual information on the radar side and the mutual information on the communication side, set power constraints on the radar side's transmitting antenna, power constraints on the communication system's transmitting antenna, spectrum compatibility constraints on the radar side, power constraints on the communication system's transmitting antenna, and electromagnetic wave phase constraints on the smart reflector. Formulate an optimization problem by taking the diagonal phase shift matrix of the smart reflector, the spatiotemporal waveform corresponding to the pulse transmitted by the radar's transmitting antenna, and the codeword transmitted by the communication user as the three variables to be solved in the optimization problem. S3. By fixing two of the three variables to be solved in sequence and solving the third variable, the optimization problem is transformed into three sub-problems. When the spatiotemporal waveform corresponding to the pulse transmitted by the radar transmitting antenna or the codeword transmitted by the communication user is used as the third variable, the corresponding sub-problem is simplified by the MM algorithm and then solved by the ADPM algorithm. When the diagonal phase shift matrix of the intelligent reflector is used as the third variable, the corresponding sub-problem is simplified by the MM algorithm and then solved by the ADPM algorithm or the EBCD algorithm.

2. The spectrum compatibility method for a radar communication system assisted by a smart reflector according to claim 1, characterized in that, The modeling of the radar system receiver is as follows: Among them, o R B represents the signal received by the radar system receiver, where s represents the spatiotemporal waveform during radar transmission; tR For the equivalent channel from the target to the radar, B tr For the equivalent channel from the target to the smart reflector, B rR If the equivalent channel from the smart reflector to the radar is B, then the overall channel is B. t,R =B tR +B rR Θ H B tr Θ is the diagonal phase shift matrix of the intelligent reflector. H Represents the Hermitian operator. b Rt (·) and b Rr (·) represent the radar's transmit steering vector and receive steering vector, respectively. D Let H represent the D-dimensional identity matrix, where ω0 represents the direction in which the radar detects the target; h1 represents the h1-th scattering point, and H1 represents the total number of scattering points. This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. This represents the clutter echo signal generated when the radar signal reaches the h1th scattering point and The h1-th scattering point is represented; k represents the k-th communication user; K represents the total number of communication users; G b,R Let G be the channel matrix of interference from the communication end to the radar end. b,R =G bR +F rR Θ H F br G bR For the baseband equivalent channel from the base station to the radar, F br The baseband equivalent channel from the base station to the smart reflector, F rR From smart reflectors to radar baseband equivalent channels; n R This represents the additive noise at the radar end.

3. The spectrum compatibility method for a radar communication system assisted by a smart reflector according to claim 2, characterized in that, The formula for modeling the signal received by the communication base station is as follows: Among them, o C,k This represents the signal received by the k-th communication user at the communication terminal and the signal received by the communication base station. The equivalent channel between the base station, the user, and the smart reflector. H bu,k H is the equivalent channel from the base station to the k-th communication user. ru,k This is the equivalent channel from the intelligent transmitter to the k-th communication user; Let be the channel matrix of interference from the base station to the t-th target, and H bu,t H is the equivalent channel from the base station to the t-th target. ru,t Let be the equivalent channel from the intelligent reflector to the t-th target; Represents the equivalent channel between the radar, the user, and the smart reflector. This represents the equivalent channel for radar reaching the k-th user. This represents the equivalent channel from the smart reflector to the k-th user. This represents the equivalent channel of the radar reaching the smart reflector, where m1 represents the m1-th element of the smart reflector, M1 represents the total number of reflecting elements in the smart reflector, and n... X,k Let be the noise at the receiver of the k-th communication user.

4. The spectrum compatibility method for a radar communication system assisted by an intelligent reflector according to claim 3, characterized in that, The calculation of mutual information on the radar side and mutual information on the communication side using the modeling results includes: The signal-to-interference-plus-noise ratio (SIR) at the radar receiver is given by the following formula. Among them, R scn ({x k },s,Θ) represents the covariance matrix of interference and noise at the radar receiver, and Let be the covariance matrix of point clutter interference and The variance of the additive noise at the radar end. Let be the covariance matrix of the interference in the communication system and Let w represent the variance of the additive noise at the radar end, and w be the optimal filter vector. Substituting this into the formula for the signal-to-interference-plus-noise ratio (SIR) at the radar receiver, we obtain the simplified form of the SIR at the radar receiver: Therefore, the mutual information on the radar side is expressed by the following formula. MI1({x k },s,Θ)=logdet(I+R scn ({x k },s,Θ) -1 B t,R ss H B t,R H ) Where I represents the identity matrix; The mutual information formula on the communication side is expressed as follows: Where D represents the D codewords sent by the communication user, R Cin,k Let the covariance matrix of noise and interference at the k-th user receiver be denoted as and This represents the variance of the noise at the k-th user's receiver. Let V be the variance matrix representing the interference at the k-th user receiver.

5. The spectrum compatibility method for a radar communication system assisted by an intelligent reflector according to claim 4, characterized in that, The optimization problem includes: s.t.||s|| 2 =P R ||x k || 2 =P C ,k=1,...,K s H P m s≤E R,m ,m=1,...,M 0≤θ q ≤2π,q=1,…,Q, Among them, κ1 and γ k Represent the non-negative weights assigned to radar and communication metrics, respectively. 2 P represents the square of the modulus. R P represents the power threshold of the transmitting antenna on the radar side. C The power threshold of the transmitting antenna of the communication system is represented by m, where m = 1, 2, 3...M, M represents the total number of electromagnetic radiators, h represents the h-th spectrum on the communication side, and H represents the total number of spectrums on the communication side; Π m Λ represents the total spectral energy on the radar side. h E represents the energy of the h-th spectrum on the communication side. R,m This indicates the upper limit of the energy constraint on the radar side. θ represents the upper limit of the energy constraint on the communication side. q Let represent the angle of the q-th passive reflective element of the intelligent reflective surface, where Q represents the Q-th passive reflective element.

6. The spectrum compatibility method for a radar communication system assisted by a smart reflector according to claim 5, characterized in that, S3 includes: By fixing variables s and Θ among the three variables to be solved, the variable x is solved. k The optimization problem is transformed into the first subproblem.

7. A spectrum compatibility method for a radar communication system assisted by a smart reflector according to claim 6, characterized in that, S3 further includes: By fixing the variable x among the three variables to be solved k And Θ, solve for variable s, and transform the optimization problem into a second subproblem.

8. A spectrum compatibility method for a radar communication system assisted by a smart reflector according to claim 7, characterized in that, S3 further includes: By fixing the variable x among the three variables to be solved k Given s, solve for the variable Θ, and transform the optimization problem into a third subproblem.

9. A spectrum-compatible device for a radar communication system assisted by an intelligent reflector, characterized in that, include: The model building module is used to build a system that includes radar, communication base station and intelligent reflector, and the radar, communication base station and intelligent reflector are interconnected. Model the signals received by the radar system receiver and the communication base station; The problem formulation module is used to calculate the mutual information of the radar side and the mutual information of the communication side using the modeling results. The objective is to maximize the weighted sum of the mutual information of the radar side and the mutual information of the communication side. Power constraints are set for the radar side transmitting antenna, the communication system transmitting antenna, the radar side spectrum compatibility constraints, the communication system transmitting antenna, and the electromagnetic wave phase constraints of the smart reflector. The diagonal phase shift matrix of the smart reflector, the spatiotemporal waveform corresponding to the pulse transmitted by the radar transmitting antenna, and the codeword transmitted by the communication user are used as the three variables to be solved in the optimization problem. The problem-solving module is used to solve the optimization problem by fixing two of the three variables to be solved in sequence and solving the third variable. This transforms the optimization problem into three subproblems. When the spatiotemporal waveform corresponding to the pulse transmitted by the radar's transmitting antenna or the codeword transmitted by the communication user is used as the third variable, the corresponding subproblem is simplified using the MM algorithm and then solved using the ADPM algorithm. When the diagonal phase shift matrix of the intelligent reflector is used as the third variable, the corresponding subproblem is simplified using the MM algorithm and then solved using the ADPM algorithm or the EBCD algorithm.

10. A spectrum-compatible device for a radar communication system assisted by a smart reflector, as described in claim 9, is characterized in that... The modeling of the radar system receiver is as follows: Among them, o R B represents the signal received by the radar system receiver, where s represents the spatiotemporal waveform during radar transmission; tR For the equivalent channel from the target to the radar, B tr For the equivalent channel from the target to the smart reflector, B rR If the equivalent channel from the smart reflector to the radar is B, then the overall channel is B. t,R =B tR +B rR Θ H B tr Θ is the diagonal phase shift matrix of the intelligent reflector. H Represents the Hermitian operator. b Rt (·) and b Rr (·) represent the radar's transmit steering vector and receive steering vector, respectively. D Let H represent the D-dimensional identity matrix, where ω0 represents the direction in which the radar detects the target; h1 represents the h1-th scattering point, and H1 represents the total number of scattering points. This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. This represents the clutter echo signal generated when the signal from the smart reflector reaches the h1th scattering point. This represents the clutter echo signal generated when the radar signal reaches the h1th scattering point and The h1-th scattering point is represented; k represents the k-th communication user; K represents the total number of communication users; G b,R Let G be the channel matrix of interference from the communication end to the radar end. b,R =G bR +F rR Θ H F br G bR For the baseband equivalent channel from the base station to the radar, F br The baseband equivalent channel from the base station to the smart reflector, F rR From smart reflectors to radar baseband equivalent channels; n R This represents the additive noise at the radar end.

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