Method and device for optimizing integrated perception and communication system
By optimizing the transmitted waveform and reconfigurable smart surface phase shift using Riemannian manifold constraints and conjugate gradient descent algorithms, the problem of multi-user interference in multi-RIS assisted ISAC systems is solved, improving the system's communication and sensing performance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-16
- Publication Date
- 2026-05-15
AI Technical Summary
Existing multi-RIS assisted ISAC systems ignore multi-user interference issues when multiple users access the system, leading to a decline in communication link quality and impaired sensing accuracy. There is an urgent need to jointly optimize the signal-to-interference-plus-noise ratio and multi-user interference to improve system performance.
The conjugate gradient descent algorithm on the Riemannian manifold is used to optimize the transmitted waveform and the phase shift of the reconfigurable smart surface. The objective function of maximizing the sensing signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication is decomposed into multiple sub-problems and solved step by step using Riemannian manifold constraints and the conjugate gradient descent algorithm.
It improves the reliability and efficiency of system communication, enhances the detection accuracy of sensed targets and the accuracy of parameter estimation, realizes dynamic adjustment of communication and sensing capabilities, and optimizes the system to meet different business needs.
Smart Images
Figure CN122052845A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of communication technology, and in particular to an optimization method and apparatus for an integrated sensing and communication system. Background Technology
[0002] Integrated Sensing and Communication (ISAC), a cutting-edge technology, aims to achieve high-precision sensing and efficient communication within a single system by sharing spectrum resources. ISAC systems assisted by Reconfigurable Intelligent Surfaces (RIS) can significantly improve performance by introducing additional design freedom and more flexible beamforming mechanisms. On one hand, RIS can concentrate signal energy towards the target user or the object to be sensed, effectively enhancing the strength of useful signals and improving sensing accuracy. On the other hand, through beam steering, interference energy can be directed to non-sensitive areas, significantly suppressing receiver interference, thereby comprehensively improving system performance. Dual-RIS assisted ISAC systems are widely used. When two RIS work together, they can reflect and manipulate signals from different angles, forming more diverse combinations of signal propagation paths and greatly expanding the dimensions of electromagnetic environment manipulation.
[0003] Current research on multi-input multi-output (MIMO) communication systems with multiple RIS-assisted ISACs commonly focuses on signal-to-interference-and-noise ratio (SINR) as an optimization objective to enhance target detection and parameter estimation capabilities. However, these studies generally overlook the multi-user interference (MUI) problem caused by multiple user access. In real-world complex interference environments, MUI severely restricts system performance, leading to a series of adverse effects such as degraded communication link quality, reduced data transmission rates, and impaired sensing accuracy.
[0004] Therefore, there is an urgent need for an optimization method that integrates sensing and communication systems to jointly optimize the signal-to-interference-plus-noise ratio and multi-user interference, thereby improving the reliability and efficiency of system communication, as well as the detection accuracy and parameter estimation accuracy of sensed targets. Summary of the Invention
[0005] This application provides an optimization method and apparatus for an integrated sensing and communication system, which jointly optimizes the signal-to-interference-plus-noise ratio and multi-user interference, thereby improving the reliability and efficiency of system communication, as well as the detection accuracy and parameter estimation accuracy of the sensed target.
[0006] In a first aspect, this application provides an optimization method for an integrated sensing and communication system, the method comprising: For an integrated sensing and communication system comprising N reconfigurable smart surfaces, the following steps are performed in each round of optimization iteration: With the joint optimization objective function of maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication, the phase shifts of N reconfigurable smart surfaces are fixed, and the transmitted waveform is optimized to obtain the optimized waveform for the current round. Following the optimization order of N reconfigurable smart surfaces, with minimizing multi-user communication interference as the optimization objective function, the optimized waveform of the current round and the phase shift of other reconfigurable smart surfaces are fixed, and the phase shift of each reconfigurable smart surface is optimized sequentially to obtain the optimized phase shift of each reconfigurable smart surface in the current round.
[0007] Optionally, N equals two; the optimization of the transmitted waveform to obtain the current round's optimized waveform, using the joint optimization objective function of maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication, and fixing the phase shifts of N reconfigurable smart surfaces, includes: With maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication as the joint optimization objective function, the phase shifts of the first and second reconfigurable smart surfaces are fixed, and the transmitted waveform is optimized to obtain the optimized waveform for the current round. The optimization process, following the order of N reconfigurable smart surfaces and using minimizing multi-user communication interference as the objective function, involves fixing the optimized waveform of the current round and the phase shift of other reconfigurable smart surfaces, and sequentially optimizing the phase shift of each reconfigurable smart surface to obtain the optimized phase shift of each reconfigurable smart surface in the current round. This includes: Using minimizing multi-user interference in communication as the optimization objective function, the optimized waveform of the current round and the optimized phase shift of the second reconfigurable smart surface in the previous round are fixed, and the phase shift of the first reconfigurable smart surface is optimized to obtain the optimized phase shift of the first reconfigurable smart surface in the current round. Using minimizing multi-user interference in communication as the optimization objective function, the optimized waveform of the current round and the optimized phase shift of the first reconfigurable smart surface in the current round are fixed, and the phase shift of the second reconfigurable smart surface is optimized to obtain the optimized phase shift of the second reconfigurable smart surface in the current round.
[0008] Optionally, the optimization of the transmitted waveform to obtain the optimized waveform for the current round, using the joint optimization objective function of maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication, and fixing the phase shifts of the first and second reconfigurable smart surfaces, includes: The joint optimization objective function is transformed into a first and second quadratic integral optimization function; Based on the constant modulus constraint of the transmitted signal, the solution space of the first quadratic integral optimization function is defined as a Riemannian manifold; The first quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, and the feasible solution is used as the optimization waveform for the current round.
[0009] Optionally, the step of optimizing the phase shift of the first reconfigurable smart surface to obtain the optimized phase shift of the first reconfigurable smart surface in the current round by fixing the optimized waveform of the round and the optimized phase shift of the second reconfigurable smart surface in the previous round, with the objective function of minimizing multi-user interference in communication, includes: Based on the channel model, the optimization objective function is transformed into a second quadratic integral optimization function with respect to the phase shift of the first reconfigurable smart surface; the channel model is the base station to user channel matrix. Based on the constant mode constraint of the transmitted signal, the solution space of the second quadratic integral optimization function is defined as a Riemannian manifold; The second quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, which is then used as the optimized phase shift for the current round of the first reconfigurable smart surface.
[0010] Optionally, the step of optimizing the phase shift of the second reconfigurable smart surface to obtain the optimized phase shift of the second reconfigurable smart surface for the current round by fixing the optimized waveform of the round and the optimized phase shift of the first reconfigurable smart surface for the current round, with the objective function of minimizing multi-user interference in communication, includes: Based on the channel model, the optimization objective function is transformed into a third quadratic integral optimization function with respect to the phase shift of the second reconfigurable smart surface; the channel model is the base station to user channel matrix. Based on the constant mode constraint of the transmitted signal, the solution space of the third quadratic integral optimization function is defined as a Riemannian manifold; The third quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, which is then used as the optimized phase shift for the current round of the second reconfigurable smart surface.
[0011] Optionally, the conjugate gradient descent algorithm on the Riemannian manifold can be used to solve the quadratic integral optimization function to obtain a feasible solution, including: Calculate the Euclidean gradient of the quadratic integral optimization function, and project the Euclidean gradient onto the tangent space of the Riemannian manifold to obtain the Riemannian gradient. The conjugate gradient descent direction of the current round is calculated based on the Riemann gradient and the conjugate gradient descent direction of the previous round. The step size is obtained based on the conjugate gradient descent direction and step size search strategy of the current round; A feasible solution is obtained based on the conjugate gradient descent direction of the current round and the step size.
[0012] Secondly, this application also provides an optimization device for an integrated sensing and communication system, the device comprising: The waveform optimization module is used to optimize the transmitted waveform to obtain the optimized waveform for the current round by fixing the phase shift of N reconfigurable smart surfaces, with the joint optimization objective function of maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication. The phase shift optimization module is used to optimize the phase shift of each reconfigurable smart surface in the current round according to the optimization order of N reconfigurable smart surfaces, with minimizing multi-user communication interference as the optimization objective function. The module fixes the optimized waveform of the current round and the phase shift of other reconfigurable smart surfaces, and sequentially optimizes the phase shift of each reconfigurable smart surface to obtain the optimized phase shift of each reconfigurable smart surface in the current round.
[0013] Optionally, N equals two; the waveform optimization module is further configured to optimize the transmitted waveform to obtain the optimized waveform for the current round by fixing the phase shift of the first reconfigurable smart surface and the second reconfigurable smart surface with the joint optimization objective function of maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication; The phase shift optimization module is further configured to optimize the phase shift of the first reconfigurable smart surface to obtain the optimized phase shift of the first reconfigurable smart surface in the current round by fixing the optimized waveform of the current round and the optimized phase shift of the second reconfigurable smart surface in the previous round, with the optimization objective function being to minimize multi-user interference in communication; The phase shift optimization module is further configured to optimize the phase shift of the second reconfigurable smart surface to obtain the optimized phase shift of the second reconfigurable smart surface in the current round by fixing the optimized waveform of the current round and the optimized phase shift of the first reconfigurable smart surface in the current round, with the optimization objective function being to minimize multi-user interference in communication.
[0014] Optionally, the waveform optimization module is further configured to convert the joint optimization objective function into a first quadratic integral optimization function; Based on the constant modulus constraint of the transmitted signal, the solution space of the first quadratic integral optimization function is defined as a Riemannian manifold; The first quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, and the feasible solution is used as the optimization waveform for the current round.
[0015] Optionally, the phase shift optimization module is further configured to convert the optimization objective function into a second quadratic integral optimization function with respect to the phase shift of the first reconfigurable smart surface, based on the channel model; the channel model is the base station to user channel matrix; Based on the constant mode constraint of the transmitted signal, the solution space of the second quadratic integral optimization function is defined as a Riemannian manifold; The second quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, which is then used as the optimized phase shift for the current round of the first reconfigurable smart surface.
[0016] Optionally, the phase shift optimization module is further configured to convert the optimization objective function into a third quadratic integral optimization function with respect to the phase shift of the second reconfigurable smart surface based on the channel model; the channel model is the base station to user channel matrix; Based on the constant mode constraint of the transmitted signal, the solution space of the third quadratic integral optimization function is defined as a Riemannian manifold; The third quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, which is then used as the optimized phase shift for the current round of the second reconfigurable smart surface.
[0017] Optionally, the waveform optimization module and the phase shift optimization module are further configured to: Calculate the Euclidean gradient of the quadratic integral optimization function, and project the Euclidean gradient onto the tangent space of the Riemannian manifold to obtain the Riemannian gradient. The conjugate gradient descent direction of the current round is calculated based on the Riemann gradient and the conjugate gradient descent direction of the previous round. The step size is obtained based on the conjugate gradient descent direction and step size search strategy of the current round; A feasible solution is obtained based on the conjugate gradient descent direction of the current round and the step size.
[0018] Thirdly, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the optimization method of the integrated sensing and communication system as described above.
[0019] Fourthly, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the optimization method for the integrated sensing and communication system as described above.
[0020] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the optimization method for the integrated sensing and communication system as described above.
[0021] This application provides an optimization method for an integrated sensing and communication system. In an integrated sensing and communication system including N reconfigurable smart surfaces, a joint optimization problem is proposed. The joint optimization objective function is to maximize the sensing signal-to-interference-plus-noise ratio and minimize multi-user interference in communication. The waveform and phase shift are optimized, which can improve the reliability and efficiency of system communication, as well as the detection accuracy and parameter estimation accuracy of the sensing target. It enables dynamic adjustment of communication and sensing capabilities and allows for flexible allocation of system resources according to the needs of actual application scenarios, ensuring that the system is in the best operating state under different business requirements. Attached Figure Description
[0022] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0023] Figure 1 A flowchart illustrating an optimization method for an integrated sensing and communication system provided in this application embodiment. Figure 1 ; Figure 2 A schematic diagram of a dual RIS-assisted ISAC system provided in an embodiment of this application; Figure 3 A flowchart illustrating an optimization method for an integrated sensing and communication system provided in this application embodiment. Figure 2 ; Figure 4 A flowchart illustrating a method for solving a quadratic integral optimization function, provided as an embodiment of this application; Figure 5 A schematic diagram of the structure of an optimization device for an integrated sensing and communication system provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0024] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0025] All actions involving the acquisition of signal information or data in this application are carried out in accordance with the relevant data protection laws and policies of the country where the application is located, and with the authorization granted by the owner of the relevant device.
[0026] In the embodiments of this application, "multiple" refers to two or more. Terms such as "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or order.
[0027] Figure 1 A flowchart illustrating an optimization method for an integrated sensing and communication system provided in this application embodiment is shown below. Figure 1 As shown, for an integrated sensing and communication system comprising N reconfigurable smart surfaces, the following steps are performed in each round of optimization iteration: Step 110: Using the joint optimization objective function of maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication, fix the phase shift of N reconfigurable smart surfaces, and optimize the transmitted waveform to obtain the optimized waveform for the current round.
[0028] The signal-to-interference-plus-noise ratio (SINR) directly determines the detection accuracy and parameter estimation accuracy of the sensed target. Multi-user interference (MUI) in communication primarily affects the reliability and efficiency of communication. In an integrated sensing and communication ISAC system comprising N reconfigurable smart surfaces (RIS), assuming that the RISs do not reflect each other's signals, by introducing... The joint optimization objective function for communication MUI and sensing SINR is constructed as shown in formula (1).
[0029] (1a) (1b) (1c) in, The number of transmitting antennas is ; the number of reflecting elements in the nth RIS is . .
[0030] Weighting factors The specific value can be set according to actual needs. ,when When the weights are biased towards minimizing the communication MUI, the system can achieve a higher communication rate; when When the weights are biased towards maximizing the perceived SINR, the system can achieve higher perception performance.
[0031] The joint optimization objective function (1) is decomposed into a first optimization problem and a second optimization problem. The first optimization problem is to solve for the optimized waveform, and the second optimization problem is to solve for the optimized phase shift.
[0032] Specifically, for the first optimization problem, the specific algorithms of communication MUI and sensing SINR are substituted into formula (1) to obtain formula (2). in, ; ; For sampling time slots; ; ; For noise energy; .
[0033] Using formula (2) as the joint optimization objective function, the phase shifts of N reconfigurable smart surfaces are fixed. The transmitted waveform is optimized to obtain the optimized waveform for the current round. .
[0034] Step 120: Following the optimization order of the N reconfigurable smart surfaces, with minimizing multi-user communication interference as the optimization objective function, fix the optimized waveform of the current round and the phase shift of other reconfigurable smart surfaces, and sequentially optimize the phase shift of each reconfigurable smart surface to obtain the optimized phase shift of each reconfigurable smart surface in the current round.
[0035] Specifically, the second optimization problem includes N subproblems, including: (1) Using formula (3a) as the objective function, fix the optimization waveform of the current round. The optimization phase shift in the previous round on other reconfigurable intelligent surfaces The optimized phase shift of the first reconfigurable smart surface in the current round is obtained by optimizing the phase shift of the first reconfigurable smart surface. .
[0036] (3a) (2) Using formula (3b) as the objective function, fix the optimization waveform of the current round. The optimized phase shift of the first reconfigurable smart surface in the current round. The optimization phase shift in the previous round on other reconfigurable intelligent surfaces The optimized phase shift of the second reconfigurable smart surface in the current round is obtained by optimizing the phase shift of the second reconfigurable smart surface. .
[0037] (3b) (3) Using formula (3c) as the objective function, fix the optimization waveform of the current round. The optimized phase shift of the first and second reconfigurable smart surfaces in the current round. The optimization phase shift in the previous round on other reconfigurable intelligent surfaces The optimized phase shift of the third reconfigurable smart surface in the current round is obtained by optimizing the phase shift of the third reconfigurable smart surface. .
[0038] (3c) (4) By analogy, the optimized phase shift of each reconfigurable smart surface in the current round is obtained according to the optimization order of the N reconfigurable smart surfaces.
[0039] For the Nth reconfigurable smart surface, using formula (3d) as the optimization objective function, the optimization waveform of the current round is fixed. Optimized phase shift of other reconfigurable smart surfaces in the current round The optimized phase shift of the Nth reconfigurable smart surface in the current round is obtained by optimizing the phase shift of the Nth reconfigurable smart surface. .
[0040] (3d) When N equals two, the integrated sensing and communication system includes two reconfigurable smart surfaces. Figure 2 A schematic diagram of a dual RIS-assisted ISAC system provided in an embodiment of this application is shown below. Figure 2 The system shown includes an ISAC base station (ISAC-BS), dual reconfigurable smart surfaces (RIS1 and RIS2), and multiple users (User1 to Userk): The ISAC base station is used to transmit data to multiple users and to emit probe beams to sense targets in the environment. RIS1 and RIS2 consist of numerous programmable reflection units used to dynamically adjust the phase of the incident signal to intelligently reconstruct the wireless propagation environment. Multiple users are the communication service targets of the system. The signals received by users include signals directly received from the ISAC base station and signals emitted from the ISAC base station and reflected by RIS1. For example, the signals received by user Userk include the signal Hbu,k directly received from the ISAC base station, the signal Hru1,k reflected by RIS1 from Hbr1 emitted from the ISAC base station, and the signal Hru2,k reflected by RIS2 from Hbr2 emitted from the ISAC base station.
[0041] In a dual RIS-assisted ISAC system, the MUI power can be calculated using formula (4).
[0042] (4) in .
[0043] In a dual RIS-assisted ISAC system, considering the beammap design pattern, the sensing SINR can be calculated using formula (5).
[0044] (5) Figure 3 A flowchart illustrating another optimization method for an integrated sensing and communication system provided in this application embodiment is shown below. Figure 3 As shown, for the integrated sensing and communication system of a dual reconfigurable smart surface, the following steps are performed in each round of optimization iteration: Step 310: Using the joint optimization objective function of maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication, fix the phase shifts of the first and second reconfigurable smart surfaces, and optimize the transmitted waveform to obtain the optimized waveform for the current round.
[0045] In the dual RIS-assisted ISAC system, the joint optimization objective function of communication MUI and sensing SINR is constructed by introducing a weight factor ρ, as shown in formula (6).
[0046] (6a) (6b) (6c) in, This refers to the number of transmitting antennas; and These represent the number of reflection units in RIS1 and RIS2, respectively.
[0047] Weighting factors The specific value can be set according to actual needs. ,when When the weights are biased towards minimizing the communication MUI, the system can achieve a higher communication rate; when When the weights are biased towards maximizing the perceived SINR, the system can achieve higher perception performance.
[0048] The joint optimization objective function (6) is decomposed into a third optimization problem and a fourth optimization problem. The third optimization problem is to solve for the optimized waveform, and the fourth optimization problem is to solve for the optimized phase shift. The fourth optimization problem includes optimizing the phase shift of the first reconfigurable smart surface and optimizing the phase shift of the second reconfigurable smart surface.
[0049] Specifically, for the first optimization problem, the specific algorithms of communication MUI and sensing SINR are substituted into formula (6) to obtain formula (7).
[0050] (7) in, ; ; For sampling time slots; ; ; For noise energy; .
[0051] Using formula (7) as the joint optimization objective function, the phase shift of the first reconfigurable smart surface RIS1 is fixed. Phase shift of the second reconfigurable smart surface RIS2 The transmitted waveform is optimized to obtain the optimized waveform for the current round. .
[0052] In one possible implementation, step 310 specifically includes the following steps: Step 311: Convert the joint optimization objective function into a first and second quadratic integral optimization function.
[0053] Specifically, define ,in, , The above formula (7) can be written as formula (8).
[0054] (8a) (8b) in, .
[0055] The quadratic fraction in formula (8) makes it difficult to solve. Dinkelbach's lemma can be used to transform it into a quadratic integral optimization problem. The first quadratic integral optimization function is shown in formula (9).
[0056] (9a) (9b) in, ; .
[0057] Step 312: Based on the constant mode constraint of the transmitted signal, the solution space of the first and second quadratic integral optimization functions is defined as a Riemannian manifold.
[0058] Specifically, the Riemannian flow with CM-constrained projection path smoothing is described. The above is no longer restricted when solving the problem, and the Riemannian manifold is represented by formula (10).
[0059] (10) Formula (9) is popular The above can be rewritten as formula (11).
[0060] (11) Step 313: Use the conjugate gradient descent algorithm on the Riemannian manifold to solve the first and second quadratic integral optimization functions to obtain feasible solutions, and use the feasible solutions as the optimization waveforms for the current round.
[0061] Specifically, the conjugate gradient descent algorithm on the Riemannian manifold is used to solve formula (11) to obtain a feasible solution, and the feasible solution is used as the optimized waveform of the current round.
[0062] Step 320: Using minimizing multi-user interference in communication as the optimization objective function, fix the optimized waveform of the current round and the optimized phase shift of the second reconfigurable smart surface in the previous round, and optimize the phase shift of the first reconfigurable smart surface to obtain the optimized phase shift of the first reconfigurable smart surface in the current round.
[0063] Specifically, the phase shift of the first reconfigurable smart surface is optimized, with formula (12) as the optimization objective function, and the optimization waveform of the current round is fixed. Phase shift optimization in the previous round of the second reconfigurable smart surface RIS2 The optimized phase shift of the first reconfigurable smart surface RIS1 is obtained by optimizing the phase shift of the first reconfigurable smart surface in the current round. .
[0064] (12) In one possible implementation, step 320 specifically includes the following steps: Step 321: Based on the channel model, transform the optimization objective function into a second quadratic integral optimization function with respect to the phase shift of the first reconfigurable smart surface. The channel model is the channel matrix from the base station to the user.
[0065] Specifically, formula (13) is obtained from formula (12).
[0066] (13a) (13b) in, It is the optimized waveform matrix for the current round, which incorporates the channel model. Substitute the expression into (13) and eliminate the AND. Irrelevant terms are solved. Formula (14).
[0067] (14a) (14b) in, ; ; ; .
[0068] Further, definition , Formula (14) can be written as a second quadratic integral optimization function with respect to the phase shift of the first reconfigurable smart surface, as shown in Formula (15).
[0069] (15a) (15b) in, .
[0070] Step 322: Based on the constant mode constraint of the transmitted signal, the solution space of the second quadratic integral optimization function is defined as a Riemannian manifold.
[0071] Specifically, the Riemannian flow with CM-constrained projection path smoothing is described. The above is no longer restricted when solving the problem, and the Riemannian manifold is represented by formula (16).
[0072] (16) Formula (15) is popular The above can be rewritten as formula (17).
[0073] (17) Step 323: Use the conjugate gradient descent algorithm on the Riemannian manifold to solve the second quadratic integral optimization function to obtain a feasible solution, and use the feasible solution as the optimization phase shift of the first reconfigurable smart surface in the current round.
[0074] Specifically, the feasible solution is obtained by solving formula (17) using the conjugate gradient descent algorithm on the Riemannian manifold, and the feasible solution is used as the optimized phase shift of the first reconfigurable smart surface in the current round.
[0075] Step 330: Using minimizing multi-user interference in communication as the optimization objective function, fix the optimized waveform of the current round and the optimized phase shift of the first reconfigurable smart surface in the current round, and optimize the phase shift of the second reconfigurable smart surface to obtain the optimized phase shift of the second reconfigurable smart surface in the current round.
[0076] Specifically, the phase shift of the second reconfigurable smart surface is optimized using formula (18) as the objective function, while fixing the optimization waveform of the current round. The optimized phase shift of the first reconfigurable smart surface RIS1 in the current round The optimized phase shift of the second reconfigurable smart surface RIS2 is obtained by optimizing the phase shift of the first reconfigurable smart surface in the current round. .
[0077] (18) In one possible implementation, step 330 specifically includes the following steps: Step 331: Based on the channel model, the optimization objective function is transformed into a third quadratic integral optimization function with respect to the phase shift of the second reconfigurable smart surface; the channel model is the channel matrix from the base station to the user.
[0078] Specifically, formula (19) is obtained from formula (18).
[0079] (19a) (19b) in, It is the optimized waveform matrix for the current round, which incorporates the channel model. Substitute the expression into (19) and eliminate the AND. Irrelevant terms are solved. Formula (20).
[0080] (20a) (20b) in, ; ; ; .
[0081] definition Formula (20) can be written as a third quadratic integral optimization function with respect to the phase shift of the second reconfigurable smart surface, as shown in Formula (21).
[0082] (21a) (21b) in, .
[0083] Step 332: Based on the constant mode constraint of the transmitted signal, the solution space of the third quadratic integral optimization function is defined as a Riemannian manifold.
[0084] Specifically, the Riemannian flow with CM-constrained projection path smoothing is described. The above is no longer restricted when solving the problem, and the Riemannian manifold is represented by formula (22).
[0085] (twenty two) Formula (21) in popular The above can be rewritten as formula (23).
[0086] (twenty three) Step 333: Use the conjugate gradient descent algorithm on the Riemannian manifold to solve the third quadratic integral optimization function to obtain a feasible solution, and use the feasible solution as the optimization phase shift of the second reconfigurable smart surface in the current round.
[0087] Specifically, the feasible solution is obtained by solving formula (17) using the conjugate gradient descent algorithm on the Riemannian manifold, and the feasible solution is used as the optimized phase shift of the current round of the second reconfigurable smart surface.
[0088] In one possible implementation, refer to Figure 4 In steps 313, 323, and 333, the conjugate gradient descent algorithm on the Riemannian manifold is used to solve the quadratic integral optimization function to obtain a feasible solution, including the following steps: Step 410: Calculate the Euclidean gradient of the quadratic integral optimization function, and project the Euclidean gradient onto the tangent space of the Riemannian manifold to obtain the Riemannian gradient.
[0089] Taking the solution of the first and second integral optimization functions to obtain a feasible solution, i.e., solving formula (11), as an example, the Euclidean gradient calculation formula in formula (11) is referenced from formula (24).
[0090] (twenty four) Riemann Hype At point The tangent space at a given point is defined as containing the Riemannian popular At point The space of all tangent vectors at point is denoted as . , It can be expressed as formula (25).
[0091] (25) in, This indicates that the real part of a complex vector is taken element by element. Indicates conjugate.
[0092] A point about the popularity of Riemann Projecting onto the tangent space is expressed as formula (26).
[0093] (26) Based on (24), (25), and (26), the Riemann gradient is obtained as formula (27).
[0094] (27) Step 420: Calculate the conjugate gradient descent direction of the current round based on the Riemann gradient and the conjugate gradient descent direction of the previous round.
[0095] Taking the solution of the first and second integral optimization functions to obtain a feasible solution, i.e., solving formula (11), as an example, in order to better improve computational efficiency and convergence, the Polak-Ribiere conjugate gradient descent direction is chosen. k The descent direction of the next iteration is as shown in formula (28).
[0096] (28) in, It is a vector transfer operator that ensures that Riemann gradients at different points can be compared. These are the Polak-Ribieres conjugate parameters. It is defined as formula (29).
[0097] (29) Step 430: Obtain the step size based on the conjugate gradient descent direction and step size search strategy of the current round.
[0098] Taking the solution of the first and second integral optimization functions to obtain a feasible solution, i.e., solving formula (11), as an example, in order to save computational resources, the step size search strategy is to choose Armijo line search to ensure that the value of the objective function can be sufficiently reduced in each iteration. k The step size search strategy for each iteration is as shown in formula (30).
[0099] (30) in, ; ; ; This is the conjugate transpose.
[0100] Step 440: Obtain a feasible solution based on the conjugate gradient descent direction and step size of the current round.
[0101] Taking the solution of the first and second integral optimization functions to obtain a feasible solution, i.e., solving formula (11), as an example, in the tangent space... The obtained trial points are shown in formula (31).
[0102] (31) By using formula (31) To obtain the next feasible solution, we return to the Riemannian manifold, referring to formula (32).
[0103] (32) in, It is the calculation of Hadamard's element-wise deviation. It is a vector Element-wise modulus.
[0104] It should be noted that the process of solving the second and third quadratic integral optimization functions is similar to that of solving the first quadratic integral optimization function, and the specific solution process will not be described in detail here. Among them, the Euclidean gradient of the second quadratic integral optimization function, i.e., formula (15), is shown in formula (33).
[0105] (33) The third quadratic integral optimization function, i.e., the Euclidean gradient of formula (21) is shown in formula (34).
[0106] (34) In one possible implementation, for N reconfigurable smart surfaces, using formula (2) as the joint optimization objective function, the phase shifts of the N reconfigurable smart surfaces are fixed. The transmitted waveform is optimized to obtain the optimized waveform for the current round. The specific steps can be referred to the above specific steps for dual reconfigurable smart surface waveform optimization, referring to formulas (7) to (11) and formulas (24) to (32).
[0107] For the Nth reconfigurable smart surface, using formula (3d) as the optimization objective function, the optimization waveform of the current round is fixed. Optimized phase shift of other reconfigurable smart surfaces in the current round The optimized phase shift of the Nth reconfigurable smart surface in the current round is obtained by optimizing the phase shift of the Nth reconfigurable smart surface. Specifically, it includes the following steps: Step a: Based on the channel model, transform the optimization objective function into a fourth quadratic integral optimization function with respect to the phase shift of the Nth reconfigurable smart surface. The channel model is the channel matrix from the base station to the user.
[0108] Specifically, formula (35) is obtained from formula (3d).
[0109] (35a) (35b) in, = ; yes Channel to UE It is BS to The channel. Substituting (35) yields the solution. Formula (36).
[0110] (36a) (36b) in, ; ; ; .
[0111] Furthermore, formula (36) can be written as a fourth quadratic integral optimization function with respect to the phase shift of the Nth reconfigurable smart surface.
[0112] Step b: Based on the constant mode constraint of the transmitted signal, the solution space of the fourth quadratic integral optimization function is defined as a Riemannian manifold.
[0113] This step is similar to the execution process of the aforementioned dual reconfigurable smart surface; for details, please refer to steps 322 and 332.
[0114] Step c: Use the conjugate gradient descent algorithm on the Riemannian manifold to solve the fourth quadratic integral optimization function to obtain a feasible solution. Use the feasible solution as the optimization phase shift for the current round of the Nth reconfigurable smart surface. .
[0115] This step is similar to the execution process of the above-mentioned dual reconfigurable smart surface, and can be referred to formulas (24) to (32) for details.
[0116] This application provides an optimization method for an integrated sensing and communication system. In an integrated sensing and communication system including N reconfigurable smart surfaces, a joint optimization problem is proposed. The joint optimization objective function is to maximize the sensing signal-to-interference-plus-noise ratio and minimize multi-user interference in communication. The waveform and phase shift are optimized, which can improve the reliability and efficiency of system communication, as well as the detection accuracy and parameter estimation accuracy of the sensing target. It enables dynamic adjustment of communication and sensing capabilities and allows for flexible allocation of system resources according to the needs of actual application scenarios, ensuring that the system is in the best operating state under different business requirements.
[0117] The following describes an optimization device for an integrated sensing and communication system provided by the present invention. The optimization device for the integrated sensing and communication system described below and the optimization method for the integrated sensing and communication system described above can be referred to in correspondence.
[0118] Figure 5 A schematic diagram of the structure of an optimization device for an integrated sensing and communication system provided in an embodiment of this application is shown below. Figure 5 As shown, the device 500 includes: The waveform optimization module 510 is used to optimize the transmitted waveform to obtain the optimized waveform for the current round by fixing the phase shift of N reconfigurable smart surfaces, with the joint optimization objective function of maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication. The phase shift optimization module 520 is used to optimize the phase shift of each reconfigurable smart surface in the current round according to the optimization order of N reconfigurable smart surfaces, with minimizing multi-user communication interference as the optimization objective function, fixing the optimized waveform of the current round and the optimized phase shift of other reconfigurable smart surfaces, and sequentially optimizing the phase shift of each reconfigurable smart surface to obtain the optimized phase shift of each reconfigurable smart surface in the current round.
[0119] Optionally, N equals two; the waveform optimization module 510 is further configured to optimize the transmitted waveform to obtain the optimized waveform for the current round by fixing the phase shift of the first reconfigurable smart surface and the second reconfigurable smart surface with the joint optimization objective function of maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication; The phase shift optimization module 520 is further configured to use minimizing multi-user communication interference as the optimization objective function, fix the optimized waveform of the current round and the optimized phase shift of the second reconfigurable smart surface in the previous round, and optimize the phase shift of the first reconfigurable smart surface to obtain the optimized phase shift of the first reconfigurable smart surface in the current round. The phase shift optimization module 520 is further configured to optimize the phase shift of the second reconfigurable smart surface to obtain the optimized phase shift of the second reconfigurable smart surface in the current round by fixing the optimized waveform of the current round and the optimized phase shift of the first reconfigurable smart surface in the current round, with the optimization objective function being to minimize multi-user interference in communication.
[0120] Optionally, the waveform optimization module 510 is further configured to convert the joint optimization objective function into a first quadratic integral optimization function; Based on the constant modulus constraint of the transmitted signal, the solution space of the first quadratic integral optimization function is defined as a Riemannian manifold; The first quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, and the feasible solution is used as the optimization waveform for the current round.
[0121] Optionally, the phase shift optimization module 520 is further configured to convert the optimization objective function into a second quadratic integral optimization function with respect to the phase shift of the first reconfigurable smart surface based on the channel model; the channel model is the base station to user channel matrix; Based on the constant mode constraint of the transmitted signal, the solution space of the second quadratic integral optimization function is defined as a Riemannian manifold; The second quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, which is then used as the optimized phase shift for the current round of the first reconfigurable smart surface.
[0122] Optionally, the phase shift optimization module 520 is further configured to convert the optimization objective function into a third quadratic integral optimization function with respect to the phase shift of the second reconfigurable smart surface based on the channel model; the channel model is the base station to user channel matrix; Based on the constant mode constraint of the transmitted signal, the solution space of the third quadratic integral optimization function is defined as a Riemannian manifold; The third quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, which is then used as the optimized phase shift for the current round of the second reconfigurable smart surface.
[0123] Optionally, the waveform optimization module 520 and the phase shift optimization module 520 are further configured to: Calculate the Euclidean gradient of the quadratic integral optimization function, and project the Euclidean gradient onto the tangent space of the Riemannian manifold to obtain the Riemannian gradient. The conjugate gradient descent direction of the current round is calculated based on the Riemann gradient and the conjugate gradient descent direction of the previous round. The step size is obtained based on the conjugate gradient descent direction and step size search strategy of the current round; A feasible solution is obtained based on the conjugate gradient descent direction of the current round and the step size.
[0124] It should be noted that the optimization device for an integrated sensing and communication system provided in this application embodiment can implement all the method steps implemented in the above-mentioned optimization method embodiment for an integrated sensing and communication system, and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.
[0125] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions from the memory 630 to execute optimization methods for the integrated sensing and communication system.
[0126] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0127] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the optimization method for the integrated sensing and communication system provided by the above methods.
[0128] In another aspect, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the optimization methods for the integrated sensing and communication system provided by the above methods.
[0129] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 this application.
Claims
1. An optimization method for an integrated sensing and communication system, characterized in that, The method includes: For an integrated sensing and communication system comprising N reconfigurable smart surfaces, the following steps are performed in each round of optimization iteration: With the joint optimization objective function of maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication, the phase shifts of N reconfigurable smart surfaces are fixed, and the transmitted waveform is optimized to obtain the optimized waveform for the current round. Following the optimization order of N reconfigurable smart surfaces, with minimizing multi-user communication interference as the optimization objective function, the optimized waveform of the current round and the phase shift of other reconfigurable smart surfaces are fixed, and the phase shift of each reconfigurable smart surface is optimized sequentially to obtain the optimized phase shift of each reconfigurable smart surface in the current round.
2. The optimization method for the integrated sensing and communication system according to claim 1, characterized in that, N equals two; the optimization objective function, which is to maximize the perceived signal-to-interference-plus-noise ratio and minimize multi-user interference in communication, is to fix the phase shifts of N reconfigurable smart surfaces and optimize the transmitted waveform to obtain the optimized waveform for the current round, including: With maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication as the joint optimization objective function, the phase shifts of the first and second reconfigurable smart surfaces are fixed, and the transmitted waveform is optimized to obtain the optimized waveform for the current round. The optimization process, following the order of N reconfigurable smart surfaces and using minimizing multi-user communication interference as the objective function, involves fixing the optimized waveform of the current round and the phase shift of other reconfigurable smart surfaces, and sequentially optimizing the phase shift of each reconfigurable smart surface to obtain the optimized phase shift of each reconfigurable smart surface in the current round. This includes: Using minimizing multi-user interference in communication as the optimization objective function, the optimized waveform of the current round and the optimized phase shift of the second reconfigurable smart surface in the previous round are fixed, and the phase shift of the first reconfigurable smart surface is optimized to obtain the optimized phase shift of the first reconfigurable smart surface in the current round. Using minimizing multi-user interference in communication as the optimization objective function, the optimized waveform of the current round and the optimized phase shift of the first reconfigurable smart surface in the current round are fixed, and the phase shift of the second reconfigurable smart surface is optimized to obtain the optimized phase shift of the second reconfigurable smart surface in the current round.
3. The optimization method for the integrated sensing and communication system according to claim 2, characterized in that, The optimization objective function, which uses maximizing the perceived signal-to-interference-plus-noise ratio (SINR) and minimizing multi-user interference in communication, is used to fix the phase shifts of the first and second reconfigurable smart surfaces. The optimized waveform for the current round is obtained by optimizing the transmitted waveform, including: The joint optimization objective function is transformed into a first and second quadratic integral optimization function; Based on the constant modulus constraint of the transmitted signal, the solution space of the first quadratic integral optimization function is defined as a Riemannian manifold; The first quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, and the feasible solution is used as the optimization waveform for the current round.
4. The optimization method for the integrated sensing and communication system according to claim 2, characterized in that, The optimization objective function is to minimize multi-user interference in communication. This involves fixing the optimized waveform of the current round and the optimized phase shift of the second reconfigurable smart surface in the previous round, and then optimizing the phase shift of the first reconfigurable smart surface to obtain the optimized phase shift of the first reconfigurable smart surface in the current round. This includes: Based on the channel model, the optimization objective function is transformed into a second quadratic integral optimization function with respect to the phase shift of the first reconfigurable smart surface; the channel model is the base station to user channel matrix. Based on the constant mode constraint of the transmitted signal, the solution space of the second quadratic integral optimization function is defined as a Riemannian manifold; The second quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, which is then used as the optimized phase shift for the current round of the first reconfigurable smart surface.
5. The optimization method for the integrated sensing and communication system according to claim 2, characterized in that, The optimization objective function, which is to minimize multi-user interference in communication, involves fixing the optimized waveform of the current round and the optimized phase shift of the first reconfigurable smart surface in the current round, and optimizing the phase shift of the second reconfigurable smart surface to obtain the optimized phase shift of the second reconfigurable smart surface in the current round. This includes: Based on the channel model, the optimization objective function is transformed into a third quadratic integral optimization function with respect to the phase shift of the second reconfigurable smart surface; the channel model is the base station to user channel matrix. Based on the constant mode constraint of the transmitted signal, the solution space of the third quadratic integral optimization function is defined as a Riemannian manifold; The third quadratic integral optimization function is solved using the conjugate gradient descent algorithm on the Riemannian manifold to obtain a feasible solution, which is then used as the optimized phase shift for the current round of the second reconfigurable smart surface.
6. The optimization method for the integrated sensing and communication system according to any one of claims 3 to 5, characterized in that, The conjugate gradient descent algorithm on the Riemannian manifold is used to solve the quadratic integral optimization function to obtain feasible solutions, including: Calculate the Euclidean gradient of the quadratic integral optimization function, and project the Euclidean gradient onto the tangent space of the Riemannian manifold to obtain the Riemannian gradient. The conjugate gradient descent direction of the current round is calculated based on the Riemann gradient and the conjugate gradient descent direction of the previous round. The step size is obtained based on the conjugate gradient descent direction and step size search strategy of the current round; A feasible solution is obtained based on the conjugate gradient descent direction of the current round and the step size.
7. An optimization device for an integrated sensing and communication system, characterized in that, The device includes: The waveform optimization module is used to optimize the transmitted waveform to obtain the optimized waveform for the current round by fixing the phase shift of N reconfigurable smart surfaces, with the joint optimization objective function of maximizing the perceived signal-to-interference-plus-noise ratio and minimizing multi-user interference in communication. The phase shift optimization module is used to optimize the phase shift of each reconfigurable smart surface in the current round according to the optimization order of N reconfigurable smart surfaces, with minimizing multi-user communication interference as the optimization objective function. The module fixes the optimized waveform of the current round and the phase shift of other reconfigurable smart surfaces, and sequentially optimizes the phase shift of each reconfigurable smart surface to obtain the optimized phase shift of each reconfigurable smart surface in the current round.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the optimization method for the integrated sensing and communication system as described in any one of claims 1 to 6.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the optimization method for the integrated sensing and communication system as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the optimization method for the integrated sensing and communication system as described in any one of claims 1 to 6.