A Communication-Sensing Integrated Beamforming Method and System Based on Reconfigurable Smart Surfaces

Abstract

This invention proposes an integrated beamforming method and system for communication and sensing based on reconfigurable smart surfaces, belonging to the field of wireless communication technology. The aim is to design suitable beamforming to maximize communication and data rate while ensuring adequate radar sensing performance. The method includes: first, constructing an ISAC system containing J RIS elements, a base station (BS), and M single-antenna mobile users (MUs), and completing the modeling of baseband signals and user received signals; second, constructing user communication and data rate models and radar target detection signal-to-noise ratio models, establishing unit mode constraints on RIS reflection coefficients and transmit power constraints; third, employing an improved algorithm combining alternating optimization and fractional programming to decompose the non-convex optimization problem into sub-problems for iterative solution, ultimately maximizing communication and data rate and radar sensing performance, while also meeting the dual functional requirements of communication and sensing. This invention is applicable to providing target detection and communication functions for vehicles.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a communication and sensing integrated beamforming method and system based on a reconfigurable smart surface. Background Technology

[0002] With the rapid development of the Internet of Things (IoT), sixth-generation (6G) wireless communication networks will propel society towards "holographic communication" and "digital twins," facilitating comprehensive interaction between intelligent agents with interconnected sensing capabilities and achieving full interconnection between the virtual and real worlds. This has also driven the emergence of new technologies such as autonomous driving, smart healthcare, and smart manufacturing. In the practical application scenarios of these emerging technologies, the form of information interaction has transcended information transmission and expanded to information perception and computation. Currently, the integration of target perception, positioning, and communication capabilities has become an indispensable basic requirement. Simultaneously, the rapid growth of wireless service devices has led to a scarcity of spectrum resources. Therefore, Integrated Sensing and Communication (ISAC), as a technology to improve spectrum efficiency, has been incorporated into the technical field of 6G networks.

[0003] Currently, many advanced technologies are being used in the research of integrated sensing and communication systems, among which multiple-input multiple-output (MIMO) systems are frequently used in dual-function beamforming. This system can simultaneously improve both communication and sensing beamforming gains by increasing spatial degrees of freedom. Therefore, beam pattern design is a key research issue in integrated sensing and communication systems. Although MIMO architecture can significantly enhance the communication and sensing performance of the system, performance degradation occurs under harsh propagation conditions (such as congestion and multipath fading). To address this issue, smart transmitting surface technology may offer a good solution.

[0004] Reconfigurable Intelligent Surfaces (RIS), also known as smart surfaces, are a key technology in the field of wireless communication. They are two-dimensional artificial electromagnetic surfaces composed of numerous tunable transmitting elements. By adjusting their transmission characteristics, they can reconstruct the wireless signal transmission environment. In the absence of line-of-sight (LoS) wireless transmission, the deployment of RIS can establish a communication link between the base station and the communication user, thereby improving system performance. For this reason, RIS has been widely used in wireless communication and sensing. Summary of the Invention

[0005] To address the challenge of achieving efficient coordination and dynamic balance between communication and sensing performance in complex propagation environments within the existing technology of Integrated Communication and Sensing (ISAC) systems, this invention proposes an integrated communication and sensing beamforming method and system based on reconfigurable smart surfaces. This method employs a RIS-assisted multi-antenna base station to sense multiple targets and communicate with multiple single-antenna users. The aim is to design suitable beamforming to maximize radar sensing performance, communication capabilities, and data rate.

[0006] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a communication-sensing integrated beamforming method based on a reconfigurable smart surface, the method comprising the following steps: Step 1: Model the baseband signal and the user received signal, and build a system containing J RIS components, a base station (BS), and... M An ISAC system for a single-antenna mobile user unit (MU); Step 2: Model the performance indicators and problem models, construct the signal-to-interference-plus-noise ratio (SINR) model for each user based on the channel between the BS and the user, and then obtain the sum rate model for all communication users, and construct the signal-to-noise ratio (SNR) model for radar target detection. Step 3: Joint beamforming optimization design. Under the constraint of transmit power, an improved alternating optimization algorithm and a fractional programming algorithm are combined to decompose the non-convex optimization problem into multiple sub-problems for solving, thereby maximizing the sum rate of communication users and radar SNR.

[0007] Furthermore, in step 1 above: The base station BS has both radar and communication functions, and the base station BS is equipped with N transmitting antennas and N receiving antennas of a uniform linear array ULA with half-wavelength spacing. The base station BS simultaneously transmits detection signals to the target and data signals to the MU. The signals transmitted by the base station BS reach the target through a direct path and a RIS reflection path, and are reflected back to the base station through these two paths. At the same time, a baseband signal model is constructed based on the radar cross section RCS, the target channel of BS and RIS, and additive white Gaussian noise (AWGN).

[0008] Furthermore, the aforementioned baseband signal is represented as:

[0009] in, Represents the radar cross section (RCS). and These represent the channels between the BS and the target, and the channels between the RIS and the target, respectively. It is additive white Gaussian noise (AWGN).

[0010] Furthermore, after receiving the target reflection signal, the aforementioned base station BS collects T samples and combines them to form an echo signal sample set for radar perception and analysis.

[0011] Furthermore, the above echo signal sample set is represented as:

[0012] in, Q The estimation is performed using a fast fading and slow fading timescale channel estimation framework.

[0013] Furthermore, step 3 above specifically includes: Step 3.1: Objective function reconstruction and simplification. By introducing the first and second auxiliary variables, the Lagrange duality method in the fractional programming algorithm is used to reconstruct the objective function that maximizes the number of communication users and the rate. At the same time, the reconstructed objective function is expanded by quadratic terms to obtain an objective function expression that is easy to optimize. Then, it is further simplified to form an objective function about the first and second auxiliary variables. Step 3.2: Auxiliary variable update: Fix all other variables except the two auxiliary variables, transform the non-convex optimization of the first and second auxiliary variables into a convex optimization subproblem, solve the optimal values ​​of the two auxiliary variables by using a convex optimization algorithm, and introduce a regularization term into the optimization model of the second auxiliary variable; Step 3.3: Receiver filter update: Optimize the receive filter using the method of maximizing radar signal-to-noise ratio (SNR), and then reorganize the radar SNR after optimization; Step 3.4: Beamforming matrix update: Based on the determined first auxiliary variable, second auxiliary variable and optimal receiving filter, the optimization problem of beamforming matrix is ​​transformed into a convex optimization problem, and the optimal beamforming matrix is ​​obtained by solving the problem through a convex optimization algorithm; Step 3.5: Construction of RIS reflection coefficient matrix optimization model: Based on the determined first auxiliary variable, second auxiliary variable, optimal receiving filter and optimal beamforming matrix, construct an optimization model of the RIS reflection coefficient matrix. Using the properties of the Kronecker product and the basic properties of matrices, organize and expand the constraints in the model to obtain an expansion containing non-convex quadratic terms. Step 3.6: Linearization of non-convex terms: Using the first-order Taylor expansion method, the non-convex quadratic terms in the expansion are linearized at the current iteration point. The linearized expression is then substituted into the original constraint conditions to transform the original non-convex constraints into linear constraints. Step 3.7: Alternating optimization initialization: Under the transformed linear constraints and the unit modulus constraints of the RIS reflection coefficients, a third auxiliary variable is introduced to rewrite the update problem of the RIS reflection coefficient matrix as an optimization problem with the third auxiliary variable; then, the dual variables of the Lagrange multipliers and the penalty factor are introduced to construct the augmented Lagrange function to solve the optimization problem. Step 3.8: RIS Reflection Coefficient Matrix Update: Fix the first auxiliary variable, the second auxiliary variable, the receiving filter, the beamforming matrix, the third auxiliary variable, the Lagrange multiplier, and the penalty factor, and transform the optimization problem of the RIS reflection coefficient matrix into a convex optimization problem. Solve the optimal RIS reflection coefficient matrix for the current iteration using a convex optimization algorithm. Step 3.9: Third Auxiliary Variable Update: Based on the optimal RIS reflection coefficient matrix of the current iteration, project each element of the third auxiliary variable onto the unit circle to ensure that the RIS reflection coefficients satisfy the constant constraint; Step 4.0: Dual variable update: Based on the optimal RIS reflection coefficient matrix of the current iteration and the updated third auxiliary variable, the Lagrange multiplier dual variable is iteratively updated using a preset iterative formula; Step 4.1: Penalty Factor Update: Adjust the penalty factor according to the preset dynamic update rules to adapt the penalty factor to the constraint satisfaction situation in the iteration process; Step 4.2: Iterative convergence judgment: Determine whether the objective function value of the current iteration meets the preset convergence condition; if the convergence condition is not met, return to step 3.2 and repeat the steps from auxiliary variable update to penalty factor update until the convergence condition is met, and finally converge to the local optimum of the problem.

[0014] Furthermore, the above convergence conditions include at least one of the following: the objective function value no longer increases, the number of iterations reaches a preset threshold, and the difference between the objective function value and the previous iteration is less than a preset accuracy threshold.

[0015] Secondly, the integrated beamforming method for communication and sensing based on reconfigurable smart surfaces described in this invention can be entirely implemented using computer software. Therefore, correspondingly, this invention also provides an integrated beamforming system for communication and sensing based on reconfigurable smart surfaces.

[0016] Thirdly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the communication-sensing integrated beamforming method based on reconfigurable smart surfaces described above.

[0017] Fourthly, the present invention also provides a computer device, the device including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the communication-sensing integrated beamforming method based on reconfigurable smart surfaces described in any of the above.

[0018] The beneficial effects of this invention are as follows: This invention proposes a RIS-assisted integrated sensing and communication (ISAC) system design scheme. First, the baseband signal and user received signal are modeled, as well as the performance indicators and problem model are modeled. Then, under the constraints of radar signal-to-noise ratio and transmit power, an algorithm combining fractional programming and an improved alternating optimization algorithm is adopted. By introducing auxiliary variables, regularization processing, first-order Taylor expansion non-convex terms, and dynamically adjusting the penalty factor, the non-convex optimization problem is decomposed into sub-problems for iterative solution, ultimately maximizing the number of communication users and the rate.

[0019] This invention is applicable to providing target detection and communication functions for vehicles. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the ISAC system described in this invention; Figure 2 These are schematic diagrams of radar beams for different schemes described in this invention; Figure 3 This is a schematic diagram illustrating the impact of the RIS reflection coefficient on communication functionality as described in this invention. Figure 4 This is a schematic diagram illustrating the effect of power on communication functions as described in this invention. Detailed Implementation

[0022] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

[0023] Example 1, Combination Figures 1 to 4This embodiment describes a communication-sensing integrated beamforming method and system based on reconfigurable smart surfaces. The method uses a RIS-assisted multi-antenna base station to sense multiple targets and communicate with multiple single-antenna users. The aim is to design a suitable beamforming to maximize communication and speed while ensuring certain radar sensing performance.

[0024] The integrated communication and sensing beamforming method includes the following steps: Step 1: Model the baseband signal and the user received signal, and build a system containing J RIS components, a base station (BS), and... M An ISAC system for a single-antenna mobile user unit (MU); Step 2: Model the performance indicators and problem models, construct the signal-to-interference-plus-noise ratio (SINR) model for each user based on the channel between the BS and the user, and then obtain the sum rate model for all communication users, and construct the signal-to-noise ratio (SNR) model for radar target detection. Step 3: Joint beamforming optimization design. Under the constraint of transmit power, an improved alternating optimization algorithm and a fractional programming algorithm are combined to decompose the non-convex optimization problem into multiple sub-problems for solving, thereby maximizing the sum rate of communication users and radar SNR.

[0025] Furthermore, in step 1 above, the base station BS has both radar and communication functions, and the base station BS is configured with N transmitting antennas and N receiving antennas of a uniform linear array ULA with half-wavelength spacing. The base station BS simultaneously transmits detection signals to the target and data signals to the MU. The signals transmitted by the base station BS reach the target through a direct path and a RIS reflection path, and are reflected back to the base station through these two paths. At the same time, a baseband signal model is constructed based on the radar cross section RCS, the target channel of BS and RIS, and additive white Gaussian noise (AWGN).

[0026] Furthermore, after receiving the target reflection signal, the BS collects T samples and combines them to form an echo signal sample set for radar perception and analysis.

[0027] Furthermore, step 3 above specifically includes: Step 3.1: Objective function reconstruction and simplification. By introducing the first and second auxiliary variables, the Lagrange duality method in the fractional programming algorithm is used to reconstruct the objective function that maximizes the number of communication users and the rate. At the same time, the reconstructed objective function is expanded by quadratic terms to obtain an objective function expression that is easy to optimize. Then, it is further simplified to form an objective function about the first and second auxiliary variables. Step 3.2: Auxiliary variable update: Fix all other variables except the two auxiliary variables, transform the non-convex optimization of the first and second auxiliary variables into a convex optimization subproblem, solve the optimal values ​​of the two auxiliary variables by using a convex optimization algorithm, and introduce a regularization term into the optimization model of the second auxiliary variable; Step 3.3: Receiver filter update: Optimize the receive filter using the method of maximizing radar signal-to-noise ratio (SNR), and then reorganize the radar SNR after optimization; Step 3.4: Beamforming matrix update: Based on the determined first auxiliary variable, second auxiliary variable and optimal receiving filter, the optimization problem of beamforming matrix is ​​transformed into a convex optimization problem, and the optimal beamforming matrix is ​​obtained by solving the problem through a convex optimization algorithm; Step 3.5: Construction of RIS reflection coefficient matrix optimization model: Based on the determined first auxiliary variable, second auxiliary variable, optimal receiving filter and optimal beamforming matrix, construct an optimization model of the RIS reflection coefficient matrix. Using the properties of the Kronecker product and the basic properties of matrices, organize and expand the constraints in the model to obtain an expansion containing non-convex quadratic terms. Step 3.6: Linearization of non-convex terms: Using the first-order Taylor expansion method, the non-convex quadratic terms in the expansion are linearized at the current iteration point. The linearized expression is then substituted into the original constraint conditions to transform the original non-convex constraints into linear constraints. Step 3.7: Alternating optimization initialization: Under the transformed linear constraints and the unit modulus constraints of the RIS reflection coefficients, a third auxiliary variable is introduced to rewrite the update problem of the RIS reflection coefficient matrix as an optimization problem with the third auxiliary variable; then, the dual variables of the Lagrange multipliers and the penalty factor are introduced to construct the augmented Lagrange function to solve the optimization problem. Step 3.8: RIS Reflection Coefficient Matrix Update: Fix the first auxiliary variable, the second auxiliary variable, the receiving filter, the beamforming matrix, the third auxiliary variable, the Lagrange multiplier, and the penalty factor, and transform the optimization problem of the RIS reflection coefficient matrix into a convex optimization problem. Solve the optimal RIS reflection coefficient matrix for the current iteration using a convex optimization algorithm. Step 3.9: Third Auxiliary Variable Update: Based on the optimal RIS reflection coefficient matrix of the current iteration, project each element of the third auxiliary variable onto the unit circle to ensure that the RIS reflection coefficients satisfy the constant constraint; Step 4.0: Dual variable update: Based on the optimal RIS reflection coefficient matrix of the current iteration and the updated third auxiliary variable, the Lagrange multiplier dual variable is iteratively updated using a preset iterative formula; Step 4.1: Penalty Factor Update: Adjust the penalty factor according to the preset dynamic update rules to adapt the penalty factor to the constraint satisfaction situation in the iteration process; Step 4.2: Iterative Convergence Judgment: Determine whether the objective function value of the current iteration satisfies the preset convergence condition; the convergence condition includes at least one of the following: the objective function value no longer increases, the number of iterations reaches a preset threshold, and the difference between the objective function value and the previous iteration is less than a preset precision threshold; if the convergence condition is not met, return to step 3.2 and repeatedly execute the steps from auxiliary variable update to penalty factor update until the convergence condition is met, and finally converge to the local optimum of the problem.

[0028] The following is combined Figure 1 This invention provides a detailed description of the integrated communication and sensing beamforming method based on reconfigurable smart surfaces proposed in this invention.

[0029] In step one, as Figure 1 The diagram shows an ISAC system assisted by J RIS elements. In this system, the BS acts as both a radar and a communication transmitter. The BS has N transmit antennas and N receive antennas in a uniform linear array (ULA) with half-wavelength spacing. It simultaneously transmits detection signals to the target and data signals to M single-antenna mobile users (MUs). Let the set of MUs be... .

[0030] The signal transmitted by the BS reaches the target via a direct path and a RIS reflection path, and is reflected back to the base station through these two paths. Since the RIS operates in full-duplex mode and is composed of passive components, there is no self-interference. After the signal reflected from the target is received by the BS receiving array, its baseband signal can be expressed as: (1) in, This refers to the radar cross section (RCS). and These represent the channels between the BS and the target, and the channels between the RIS and the target, respectively. The additive white Gaussian noise (AWGN) is superimposed. This is achieved by setting the range between the BS / RIS and the target to LoS, and... ,in This is the DoA (DoA) of the target reference BS / RIS. Simultaneously, beamforming design at the BS can further improve target detection and parameter estimation. Radar sensing requires analyzing the echo signals reflected from sampled data. This invention uses T samples, combining these samples as follows: (2) Step two involves performance metrics and a problem model. Communication users and rate are classic performance metrics in multi-user communication. Following the NOMA (Normally Instantaneous Multi-User Communication) principle, communication users decode signals based on channel strength. Therefore, we arrange the communication user channels as follows: .

[0031] According to the above, the first m The user first uses SIC to detect and remove interference from all weaker users, and then compares the user with the user m Strong user interference is considered noise, then the user m The signal-to-interference-plus-noise ratio (SINR) is: (3) Among them, the channel between the BS and the user For the sake of brevity and clarity, this invention defines... , It means Given the z-th column of the matrix, the sum rate of all communication users can be expressed as: .

[0032] In radar sensing, target detection is a crucial task, and signal-to-noise ratio (SNR) is a key indicator in target detection. Therefore, this invention defines the radar SNR as follows: (4) Step three involves beamforming design for the ISAC system. This invention addresses the design problem of ISAC beamforming by analyzing the problem model. The invention finds that the high complexity of the objective function and its constraints makes the non-convex optimization problem difficult to solve. Therefore, this invention proposes combining fractional programming algorithms with an improved alternating optimization algorithm to transform the non-convex problem into several sub-problems for solution.

[0033] First, in order to transform the complex objective function into an expression that is easier to optimize, this invention introduces a first auxiliary variable. Second auxiliary variable The objective function is reconstructed using the Lagrange duality method in fractional programming, and a quadratic expansion is performed to obtain the following equation. (5) Furthermore, to facilitate subsequent algorithm improvements, this invention further simplifies the objective function regarding: (6) renew and : respectively fixed except and Other than variables, then auxiliary variables The optimization problem is thus transformed from a non-convex optimization problem into a subproblem of convex optimization, through... This convex optimization problem can be solved using auxiliary variables. The optimization can be expressed as: (7) Similarly, auxiliary variables The optimization problem can be expressed as: (8) in, This is a regularization term that ensures the stability of the data.

[0034] Update the accept filter Optimize the receiving filter by keeping other parameters constant and using a method that maximizes the signal-to-noise ratio. The specific optimization formula is as follows: (9) Updating the receive filter Subsequently, this invention re-organizes the radar signal-to-noise ratio: (10) It is worth noting that, during the reorganization process, this invention will It is limited to a non-negative real number, and the present invention will... Defined as .

[0035] Update beamforming matrix : After obtaining the determined auxiliary variables and Receiver filter Subsequently, this invention discovered beamforming matrix The optimization of this problem is a simple convex optimization problem, which can be represented as follows: (11) Update the RIS reflection coefficient matrix Determine auxiliary variables and Receiver filter Beamforming matrix Next, the RIS reflection coefficient matrix The optimization can be expressed as: (12) because The constraint is a non-convex constraint. Non-convex unit modulus constraints cannot be solved directly; therefore, they need to be processed first. This invention utilizes the properties of the Kronecker product and the fundamental properties of matrices to solve for... Rearrange and expand to obtain the following equation: (13) In the above equation, the last term is a non-convex term, which is clearly a very difficult problem to handle in subsequent optimizations. Therefore, this invention considers using a first-order Taylor expansion to handle the quadratic term at the iteration point. Expand on: (14) Substituting the expanded terms into the original expression, we get about linear functions : (15) linearized Substituting the original constraints, equation (12) can be transformed into: (16) It can be seen that equation (16) is clearly a linear constraint. Under the constraints of the unit modulus and the new constraint, we propose to use the alternating optimization algorithm to optimize the objective function. First, we need to introduce a third auxiliary variable based on the original problem. It will be updated The question should be rewritten as follows: (17) Introducing Lagrange multipliers and penalty factors An augmented Lagrangian function is constructed to solve the above optimization problem: (18) in, As dual variables. Penalty coefficient. Dynamically update the conformity (19) (19) Updating the RIS reflection matrix: With other variables fixed, the optimization problem of the reflection matrix is ​​transformed into a convex optimization problem, which can then be solved using a convex optimization algorithm.

[0036] Update the third auxiliary variable In updating During the process, each element is projected onto the unit circle to ensure the constant constraint: (20) (twenty one) Update dual variables In determining the reflection matrix Auxiliary variables Then, dual variables The iterative update can be performed using the following equation: (twenty two) Based on the above analysis, the specific implementation process of the joint beamforming and transmission design scheme under the worst-case radar signal-to-noise ratio constraint in a multi-target, multi-user scenario is as follows: After adopting an initialization strategy, the algorithm gradually optimizes each variable through iterative loops until the convergence condition is met.

[0037] Furthermore, to verify the communication performance, sensing beam optimization effect, and resource utilization efficiency of the present invention (RIS-assisted integrated sensing beamforming design method), a relevant experimental system was built and comparative tests were conducted.

[0038] Figure 2 The diagram shows radar beam patterns for different schemes. The horizontal axis represents the angle θ, and the vertical axis (Beamoutput (dB)Average) represents the beam output. The beam pattern represents the average beam direction, the desired beam pattern represents the desired beam direction, No RIS-assist represents the scheme without RIS (Reconfigurable Smart Reflective Surface) assistance, and RIS-assist represents the scheme with RIS assistance (i.e., this invention). As can be seen from the diagram, it verifies that RIS assistance can effectively optimize the beamforming effect of the integrated sensing system: making the beam gain higher and the directivity better in the target direction, and closer to the desired beam performance, providing beam-level support for subsequent communication and data rate improvement and sensing accuracy assurance.

[0039] Figure 3 The diagram illustrates the impact of the RIS reflection coefficient on communication functionality. The horizontal axis (Number of reflecting elements J) represents the number of reflecting elements J, and the vertical axis (Sum-rate) represents the sum-rate. "Communication" represents a pure communication scheme (focusing solely on communication performance without considering sensing constraints), "Proposed designs" represents the present invention's scheme (a RIS-assisted integrated sensing design scheme), and "No RIS assistance" represents a scheme without RIS assistance. The diagram demonstrates a positive correlation between the number of RIS reflecting elements and the communication sum-rate. Under the dual-function constraint of sensing, the present invention's scheme can significantly improve the communication sum-rate by increasing the number of RIS reflecting elements, and its performance is far superior to the scheme without RIS assistance.

[0040] Figure 4 The diagram illustrates the impact of power on communication functionality; the horizontal axis represents the transmission power. P t () is the transmission power P tThe vertical axis (Sum-rate) represents the sum rate; Communication represents a pure communication scheme (focusing only on communication performance and not considering sensing constraints), Proposed designs represent the present invention scheme (RIS-assisted integrated sensing and communication design scheme), and No RIS assistance represents a scheme without RIS assistance. As can be seen from the figure, this figure verifies that the present invention scheme can significantly improve the utilization efficiency of transmission power: under the constraint of dual sensing and communication functions, through RIS assistance and joint beamforming optimization, the communication and sum rate at the same transmission power are far superior to the scheme without RIS assistance, taking into account the engineering practicality of both communication performance and sensing requirements.

[0041] Example 2: The communication and sensing integrated beamforming method based on reconfigurable smart surfaces described in the above examples can all be implemented using computer software. Therefore, this example provides a communication and sensing integrated beamforming system based on reconfigurable smart surfaces.

[0042] Example 3: This example provides a computer-readable storage medium storing a computer program. When the computer program is run by a processor, it executes the communication-sensing integrated beamforming method based on reconfigurable smart surfaces described in the above examples.

[0043] Those skilled in the art will understand that implementing all or part of the processes in the above embodiments can be accomplished by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0044] Example 4: This example provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the communication-sensing integrated beamforming method based on reconfigurable smart surfaces described in the above examples.

[0045] This embodiment provides a computer device. The hardware device in this part is a general model and is not shown in the figure. The system includes a processor and a memory. The processor and the memory can be connected by a bus or other means. The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, as well as corresponding program instructions / modules. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions and modules stored in the memory, so as to realize the communication sensing integrated beamforming method and steps based on reconfigurable smart surfaces in the above method embodiment.

[0046] The memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, mobile communication networks, and combinations thereof.

[0047] One or more modules are stored in the memory. When the processor executes, it performs the method steps in the embodiments. In this way, the invention objective can be achieved through the method, apparatus and process of the present invention. The specific details of the computer device described above can be understood by referring to the relevant descriptions and effects in the embodiments, and will not be repeated here.

[0048] The above description of the technical solution provided by the present invention through several specific embodiments is intended to highlight the advantages and benefits of the technical solution provided by the present invention. However, the above-described specific embodiments are not intended to limit the present invention. Any reasonable modifications and improvements to the present invention, reasonable combinations of implementation methods and equivalent substitutions based on the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A communication-sensing integrated beamforming method based on reconfigurable smart surfaces, characterized in that, The methods include: Step 1: Model the baseband signal and the user received signal, and build a system containing J RIS components, a base station (BS), and... M An ISAC system for a single-antenna mobile user unit (MU); Step 2: Model the performance indicators and problem models, construct the signal-to-interference-plus-noise ratio (SINR) model for each user based on the channel between the BS and the user, and then obtain the sum rate model for all communication users, and construct the signal-to-noise ratio (SNR) model for radar target detection. Step 3: Joint beamforming optimization design. Under the constraint of transmit power, an improved alternating optimization algorithm and a fractional programming algorithm are combined to decompose the non-convex optimization problem into multiple sub-problems for solving, thereby maximizing the sum rate of communication users and radar SNR.

2. The integrated beamforming method for communication and sensing according to claim 1, characterized in that, In step 1: The base station BS has both radar and communication functions, and the base station BS is equipped with N transmitting antennas and N receiving antennas of a uniform linear array ULA with half wavelength spacing. The base station BS simultaneously transmits detection signals to the target and data signals to the MU. The signals transmitted by the base station BS reach the target through a direct path and a RIS reflection path, and are reflected back to the base station through these two paths. At the same time, a baseband signal model is constructed based on the radar cross section RCS, the target channel of BS and RIS, and additive white Gaussian noise (AWGN).

3. The communication-sensing integrated beamforming method according to claim 2, characterized in that, The baseband signal is represented as: in, Represents the radar cross section (RCS). and These represent the channels between the BS and the target, and the channels between the RIS and the target, respectively. It is additive white Gaussian noise (AWGN).

4. The communication-sensing integrated beamforming method according to claim 3, characterized in that, After receiving the target reflection signal, the BS collects T samples and combines them to form an echo signal sample set for radar perception and analysis.

5. The communication-sensing integrated beamforming method according to claim 4, characterized in that, The echo signal sample set is represented as follows: in, Q The estimation is performed using a fast fading and slow fading timescale channel estimation framework.

6. The integrated communication and sensing beamforming method according to claim 1, characterized in that, Step 3 specifically involves: Step 3.1: Objective function reconstruction and simplification. By introducing the first and second auxiliary variables, the Lagrange duality method in the fractional programming algorithm is used to reconstruct the objective function that maximizes the number of communication users and the rate. At the same time, the reconstructed objective function is expanded by quadratic terms to obtain an objective function expression that is easy to optimize. Then, it is further simplified to form an objective function about the first and second auxiliary variables. Step 3.2: Auxiliary variable update: Fix all other variables except the two auxiliary variables, transform the non-convex optimization of the first and second auxiliary variables into a convex optimization subproblem, solve the optimal values ​​of the two auxiliary variables by using a convex optimization algorithm, and introduce a regularization term into the optimization model of the second auxiliary variable; Step 3.3: Receiver filter update: Optimize the receive filter using the method of maximizing radar signal-to-noise ratio (SNR), and then re-adjust the lower bound of radar SNR after optimization; Step 3.4: Beamforming matrix update: Based on the determined first auxiliary variable, second auxiliary variable and optimal receiving filter, the optimization problem of beamforming matrix is ​​transformed into a convex optimization problem, and the optimal beamforming matrix is ​​obtained by solving the problem through a convex optimization algorithm; Step 3.5: Construction of RIS reflection coefficient matrix optimization model: Based on the determined first auxiliary variable, second auxiliary variable, optimal receiving filter and optimal beamforming matrix, construct an optimization model of the RIS reflection coefficient matrix. Using the properties of the Kronecker product and the basic properties of matrices, organize and expand the constraints in the model to obtain an expansion containing non-convex quadratic terms. Step 3.6: Linearization of non-convex terms: Using the first-order Taylor expansion method, the non-convex quadratic terms in the expansion are linearized at the current iteration point. The linearized expression is then substituted into the original constraint conditions to transform the original non-convex constraints into linear constraints. Step 3.7: Alternating optimization initialization: Under the transformed linear constraints and the unit modulus constraints of the RIS reflection coefficients, a third auxiliary variable is introduced to rewrite the update problem of the RIS reflection coefficient matrix into an optimization problem with the third auxiliary variable; By introducing the dual variables of the Lagrange multipliers and the penalty factor, an augmented Lagrange function is constructed to solve the optimization problem. Step 3.8: RIS Reflection Coefficient Matrix Update: Fix the first auxiliary variable, the second auxiliary variable, the receiving filter, the beamforming matrix, the third auxiliary variable, the Lagrange multiplier, and the penalty factor, and transform the optimization problem of the RIS reflection coefficient matrix into a convex optimization problem. Solve the optimal RIS reflection coefficient matrix for the current iteration using a convex optimization algorithm. Step 3.9: Third Auxiliary Variable Update: Based on the optimal RIS reflection coefficient matrix of the current iteration, project each element of the third auxiliary variable onto the unit circle to ensure that the RIS reflection coefficients satisfy the constant constraint; Step 4.0: Dual variable update: Based on the optimal RIS reflection coefficient matrix of the current iteration and the updated third auxiliary variable, the Lagrange multiplier dual variable is iteratively updated using a preset iterative formula; Step 4.1: Penalty Factor Update: Adjust the penalty factor according to the preset dynamic update rules to adapt the penalty factor to the constraint satisfaction situation in the iteration process; Step 4.2: Iterative Convergence Judgment: Determine whether the objective function value of the current iteration satisfies the preset convergence condition; If the convergence condition is not met, return to step 3.2 and repeat the steps from updating the auxiliary variable to updating the penalty factor until the convergence condition is met, and finally converge to the local optimum of the problem.

7. The integrated communication and sensing beamforming method according to claim 6, characterized in that, The convergence conditions include at least one of the following: the objective function value no longer increases, the number of iterations reaches a preset threshold, and the difference between the objective function value and the previous iteration is less than a preset accuracy threshold.

8. A communication-sensing integrated beamforming system based on reconfigurable smart surfaces, characterized in that, The system is implemented based on the communication and sensing integrated beamforming method based on reconfigurable smart surfaces as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the communication-sensing integrated beamforming method based on a reconfigurable smart surface as described in any one of claims 1-7.

10. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the communication-sensing integrated beamforming method based on reconfigurable smart surfaces as described in any one of claims 1-7.

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