Phase shift optimization method and system for communication sensing network

Abstract

The embodiment of the invention provides a phase shift optimization method and a phase shift optimization system for a communication sensing network, which can solve the technical problem of poor overall performance of a wireless system in the prior art. The method comprises the following steps: based on a phase shift vector of a metasurface, determining a signal interference noise ratio and a beam pattern gain of each user in a clustering result; constructing a phase shift optimization function based on the signal interference noise ratio, the beam pattern gain and the amplitude constraint of the metasurface; and obtaining a phase shift optimization result under the condition that the phase shift optimization function meets a convergence condition. The limitation of the coverage range is overcome, and the higher degree of freedom is obtained by dynamically adjusting the transmission and reflection factors of the units. A penalty function method is adopted, rank-one constraint serves as a penalty term, and a local optimal solution of an original problem is solved by continuously improving penalty factor iteration. The maximization of the beam pattern gain in the minimum sensing target direction is realized under the condition that the service quality of communication users is met, the sensing performance is improved, high delay and communication failure are prevented, and the reliability and stability of the system are improved.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a phase shift optimization method and system for communication sensing networks. Background Technology

[0002] In the phase-shift design of a Reconfigurable Intelligent Surface (RIS), the base station and the RIS are connected via a controller. The RIS itself consists of a large number of programmable electromagnetic units, and its phase-shift parameters cannot be directly controlled by the base station. Instead, the controller must transmit control commands to dynamically configure the phase of the electromagnetic units. The controller can be a Field-Programmable Gate Array (FPGA). FPGAs possess high parallel processing capabilities and real-time response characteristics, enabling synchronous updates of the phase-shift parameters of all RIS units within milliseconds or even microseconds. They also support dynamic reconfiguration of hardware logic, adapting to phase-shift adjustment requirements in different scenarios and dynamically adjusting the phase shift to improve system performance. Compared to a single RIS that can only reflect signals from the base station, limiting the coverage of the wireless network, using two RISs, one for reflection and the other for transmission, can solve the service coverage limitations of the RIS.

[0003] However, because the two RIS units are independent of each other, the resources and interference in the penetration and reflection zones cannot be coordinated more effectively through RIS unit phase shifting, which limits the overall performance of the wireless system. Summary of the Invention

[0004] This application provides a phase shift optimization method and system for communication sensing networks, which can solve the technical problem of poor overall performance of wireless systems in related technologies.

[0005] In a first aspect, embodiments of this application provide a phase shift optimization method for communication sensing networks. The method includes: determining the signal-to-interference-plus-noise ratio (SNR) and beammap gain of each user in the clustering result based on the phase shift vector of the metasurface; constructing a phase shift optimization function based on the SNR, beammap gain, and amplitude constraints of the metasurface; and obtaining the phase shift optimization result when the phase shift optimization function satisfies the convergence condition.

[0006] In one possible implementation of the first aspect, determining the signal-to-interference-plus-noise ratio (SNR) of each user in the clustering result based on the phase shift vector of the metasurface includes: assigning a beam to each clustering result and determining the received signal of each user in the clustering result in the beam; and performing a serial interference cancellation operation on the clustering result based on the received signal of each user in the clustering result to determine the SNR of each user in the clustering result.

[0007] In one possible implementation of the first aspect, the clustering result includes a first user and a second user; the distance between the first user and the metasurface is closer than the distance between the second user and the metasurface; determining the signal-to-interference-plus-noise ratio (SNR) of each user in the clustering result based on the phase shift vector of the metasurface includes: obtaining the SNR of the first user itself based on the power allocation factor of the first user, the out-of-cluster interference of the first user, the channel gain, and the phase shift vector; obtaining the SNR of the second user relative to the first user based on the power allocation factor of the first user, the power allocation factor of the second user, the out-of-cluster interference of the first user, the channel gain, and the phase shift vector; and obtaining the SNR of the second user itself based on the power allocation factor of the first user, the power allocation factor of the second user, the out-of-cluster interference of the second user, the channel gain, and the phase shift vector.

[0008] In one possible implementation of the first aspect, determining the beammap gain based on the phase shift vector of the metasurface includes: determining the beammap gain based on the phase shift vector, beam parameters, channel gain, and target steering vector; wherein the target steering vector is used to characterize the directional response of the transmitted signal of the array antenna; and the beam parameters are the parameters of the beam corresponding to the clustering result.

[0009] In one possible implementation of the first aspect, a phase shift optimization function is constructed based on the signal-to-interference-to-noise ratio, beammap gain, and metasurface amplitude constraints; including: introducing a positive semi-definite matrix and a penalty term into the phase shift optimization function to obtain an updated phase shift optimization function; and performing a Taylor expansion on the updated phase shift optimization function to obtain the target optimization function.

[0010] In one possible implementation of the first aspect, the optimization objective of the objective optimization function includes maximizing the minimum value among the sensing performance indicators corresponding to each objective direction; the objective directions are determined based on the signal-to-interference-to-noise ratio, beam pattern gain, and the amplitude constraint of the metasurface, respectively.

[0011] In one possible implementation of the first aspect, the phase shift optimization result is obtained when the phase shift optimization function satisfies the convergence condition, including: iteratively solving the objective optimization function based on the phase shift angle; adjusting the value of the penalty term after each iteration to make the objective optimization function converge; and comparing the optimal value after the t-th iteration with the optimal value after the t-th iteration. If the optimal value of the first iteration is less than or equal to the difference threshold, the convergence condition is satisfied, and the phase shift optimization result is obtained; where t is a positive integer greater than or equal to 2.

[0012] In one possible implementation of the first aspect, determining the phase shift vector of the metasurface includes: determining the phase shift vector based on a phase shift angle and a first factor; the first factor being the transmission or reflection factor of the metasurface.

[0013] In one possible implementation of the first aspect, the metasurface is STARRIS, a reconfigurable smart surface that simultaneously transmits and reflects light.

[0014] This application provides a phase-shift optimization method for communication sensing networks, which offers the following advantages: It overcomes coverage limitations by dynamically adjusting the transmission and reflection factors of individual units, achieving a higher degree of freedom compared to two RIS blocks. By employing a penalty function method, using the rank-one constraint as a penalty term, and iteratively increasing the penalty factor to find the local optimum of the original problem, it avoids the high complexity issues caused by Gaussian randomization. Under the condition of satisfying the QoS of communication users, it maximizes the beammap gain of the minimum sensing target direction, improves sensing performance, ensures the quality of service for communication users, prevents high latency or communication failures, and maintains the reliability and stability of the system.

[0015] Secondly, embodiments of this application provide a phase shift optimization system for a communication sensing network, used to perform the method described in the first aspect.

[0016] The phase-shift optimization system for communication sensing networks described in the second aspect above can refer to the beneficial effects of the first aspect above and any of its possible design methods, which will not be elaborated here. Attached Figure Description

[0017] The accompanying drawings are provided to better understand this solution and do not constitute a limitation on the embodiments of this application. Wherein: Figure 1 A schematic flowchart illustrating a phase shift optimization method for a communication sensing network provided in an embodiment of this application; Figure 2 A schematic diagram illustrating a phase shift optimization method for a communication sensing network provided in an embodiment of this application; Figure 3 A schematic diagram of a phase shift optimization system for a communication sensing network provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. In the description of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" are not necessarily different. Meanwhile, in the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of terms such as "exemplary" or "for example" is intended to present related concepts in a concrete manner for ease of understanding.

[0019] Furthermore, the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0020] Non-Orthogonal Multiple Access (NOMA) technology, as one of the key technologies of the sixth-generation mobile communication (6G) system, allows multiple communication users to share the same frequency domain resources. By differentiating between dimensions such as the power domain, it enables multiple users to reuse their data. This technology can significantly improve the spectrum efficiency of the system and meet the 6G requirement for high spectrum utilization.

[0021] Successive Interference Cancellation (SIC) is a key technology supporting reliable communication in non-orthogonal multiple access (NOMA) networks. In downlink NOMA systems, the design of SIC directly determines the decoding complexity of the receiver (Rx). An overly complex SIC design significantly increases the decoding complexity of the receiver, not only increasing signal processing delay but also raising hardware design costs and energy consumption, hindering the practical application of the technology.

[0022] A Reconfigurable Intelligent Surface (RIS) is an intelligent communication aid with electromagnetic control capabilities, comprising a large array of low-cost, programmable electromagnetic units. By dynamically adjusting parameters such as phase and amplitude of each electromagnetic unit through controllers like FPGAs, RIS can precisely control the reflection, refraction, and phase shift of incident wireless signals, thereby altering the signal propagation path, enhancing receiver signal quality, or extending communication coverage. In wireless networks, RIS typically work in conjunction with base stations, optimizing signal propagation characteristics to assist in communication and sensing tasks. However, traditional RIS are mostly purely reflective designs, only able to control signals within their own half-space, resulting in inherent limitations in service coverage.

[0023] In the field of RIS phase shift design technology, after the base station establishes a connection with the RIS, the phase shift parameters of the RIS can be dynamically adjusted by controllers such as Field Programmable Gate Arrays (FPGAs), thereby improving the performance of the wireless communication system. This approach has become one of the research hotspots in the field of wireless communication.

[0024] In related technologies, single reconfigurable smart surfaces typically only have signal reflection capabilities, and can only reflect signals from base stations to achieve signal coverage. Due to the lack of signal transmission capability, they cannot extend the coverage of base station signals through multiple paths, which greatly limits the service coverage of wireless networks and makes it difficult to meet the full-space coverage requirements in complex communication scenarios.

[0025] To address the limited service coverage of a single RIS (Reflection and Transmission RIS), two independent RIS are employed, one for signal reflection and the other for signal transmission. Coverage is extended through the coordinated deployment of the reflection and transmission RIS. However, the two RIS are structurally and control-independent, and their phase shift adjustment strategies cannot be synchronized. This results in inefficient coordination of resource allocation and interference suppression between the transmission area (the communication area covered by the transmission RIS) and the reflection area (the communication area covered by the reflection RIS) using the phase shift parameters of the RIS units. Consequently, the overall communication performance of the wireless system is limited, making further improvements difficult.

[0026] To address the aforementioned issues, this application provides a phase shift optimization method for communication sensing networks, adaptable to system optimization scenarios in wireless communication. For example, it can be applied to determining the phase shift scheme of the optimal unit (passive network unit) by combining "simultaneous penetration and reflection reconfigurable smart surfaces, inductive integration, and non-orthogonal multiple access technology." This application overcomes coverage limitations by dynamically adjusting the transmission and reflection factors of the unit, achieving a higher degree of freedom compared to two RIS blocks. A penalty function method is employed, using the rank-one constraint as a penalty term. By iteratively increasing the penalty factor, the local optimal solution of the original problem is found, avoiding the high complexity problem caused by Gaussian randomization. Under the condition of satisfying the QoS of communication users, the method maximizes the beammap gain of the minimum sensing target direction, improves sensing performance, ensures the service quality of communication users, prevents high latency or communication failures, and maintains the reliability and stability of the system.

[0027] Figure 1 This is a flowchart illustrating a phase shift optimization method for communication sensing networks provided in an embodiment of this application. Figure 1 As shown, in some embodiments, the phase shift optimization method for communication-aware networks includes the following steps: S101, based on the phase shift vector of the metasurface, determines the signal-to-interference-plus-noise ratio and beammap gain of each user in the clustering results.

[0028] In some embodiments, the metasurface is a simultaneously transmissive and reflective reconfigurable smart surface, STAR-RIS.

[0029] STAR-RIS, a smart surface combining reflection and transmission functions, integrates signal transmission and signal reflection units. These two types of units are logically interconnected, enabling coordinated adjustment of phase shift parameters. On one hand, the collaborative operation of the transmission and reflection units ensures full spatial coverage of the system. On the other hand, compared to a "two independent RIS" solution, STAR-RIS provides greater freedom in phase shift adjustment, enabling precise coordination of resource allocation and interference suppression, thereby significantly improving the overall performance of the wireless system.

[0030] like Figure 2 As illustrated, exemplarily, the main controller synchronously outputs phase and amplitude modulation commands to the reflecting and transmitting units based on the communication and sensing requirements (such as coverage area and interference intensity) issued by the base station. When the base station signal is incident on the STAR-RIS surface, the reflecting unit adjusts its own electromagnetic impedance to change the signal phase and reflects it to a preset reflection coverage area. The transmitting unit adjusts the signal transmission coefficient to allow part of the incident signal to penetrate the unit array to the transmission coverage area. The modulation parameters of the two types of units form a coordinated closed loop through the controller to avoid mutual interference between the reflected and transmitted signals. For example, the base station can be a dual-function base station. The STAR-RIS can be a uniform linear array of simultaneously transmissive and reflective smart surfaces (ULA-STAR-RIS). The area covered by the transmission of the STAR-RIS is called the transmission region (TRegion). The area covered by the reflection of the STAR-RIS is called the reflection region (R Region). Due to the obstruction of obstacles, the beam emitted by the base station achieves communication and sensing functions through the transmission and reflection of the STAR-RIS.

[0031] In some embodiments, determining the phase shift vector of the metasurface includes: Based on phase shift angle and the first factor Determine the phase shift vector The first factor is the transmission or reflection factor of the metasurface. For example, the phase shift vectors of the STAR-RIS transmission and reflection units are... : ; Where N represents the total number of transmission or reflection units in STAR-RIS; The transmission or reflection factor of the unit is denoted as , and .

[0032] To address the technical issues of high decoding complexity and hardware cost in existing SiC (System-in-Channel) designs, some embodiments involve clustering the communication users in the system before SiC execution. This divides the communication users into multiple independent user clusters, and then restricts interference cancellation operations to be performed within each user cluster, eliminating the need for interference cancellation between users in different clusters. This scheme achieves a reasonable limitation of the interference cancellation range through user clustering, significantly reducing receiver decoding complexity while ensuring communication quality, and simultaneously lowering hardware design costs and energy consumption, demonstrating good practicality and economy.

[0033] For example, communication users are grouped into clusters, with two communication users in each cluster.

[0034] For example, the metasurface first collects spatial location data from 10 users. User 1 and User 2 are grouped into cluster 1. User 3 and User 4 are grouped into cluster 2, and User 5 and User 6, User 7 and User 8, and User 9 and User 10 are grouped into clusters 3 through 5 respectively, ensuring that the signal of each cluster of users can be covered by the same beam.

[0035] Within a cluster, users closer to STAR-RIS (i.e., less than or equal to a distance threshold) are defined as near users, and users farther from STAR-RIS (i.e., farther than a distance threshold) are defined as far users. Each cluster shares a beam emitted from the base station. Simultaneously, the power of the communication signal can also be used to detect sensing targets. In some embodiments, the clustering result includes a first user and a second user. The distance between the first user (i.e., near user) and the metasurface is closer than the distance between the second user (i.e., far user) and the metasurface. It is understood that the specific value of the distance threshold is not limited in this application and can be determined according to the actual application scenario.

[0036] S102, based on signal interference-to-noise ratio, beammap gain, and metasurface amplitude constraints, constructs a phase shift optimization function.

[0037] In some embodiments, given the base station's emitted beam and power allocation factor, a phase shift optimization function is established to maximize the minimum beammap gain within the system while ensuring the QoS of communication users. For example, the phase shift optimization function is constructed based on the SINR calculated from the user's minimum target rate (constrained by the communication user's QoS conditions) and the amplitude constraints of each reflection or transmission unit.

[0038] S103, under the condition that the phase shift optimization function satisfies the convergence condition, the phase shift optimization result is obtained.

[0039] Using continuous convex approximation, variables The algorithm iterates continuously. The system's beamwidth W and power distribution factor α are already fixed values. A value can be randomly set for each phase shift unit. phase shift angle Next, the objective function is solved iteratively. After each iteration, the penalty factor needs to be increased to ensure algorithm convergence. When the algorithm converges, i.e., in the ... The optimal value after the nth iteration and the th iteration If the optimal value of the next iteration is reduced to a value equal to the difference threshold, a suboptimal solution to the original problem can be obtained.

[0040] In some embodiments, when performing step S101, determining the signal-to-interference-to-noise ratio (SNR) of each user in the clustering results based on the phase shift vector of the metasurface includes: S201, assign a beam to each cluster result and determine the received signal of each user in the cluster result in the beam.

[0041] For example, the superimposed signal emitted from the base station can be represented as: ; in, , Represented as The k-th cluster of the region, where s represents the transmitted signal. It is represented as the power distribution factor, and .

[0042] It is understood that the above is merely an illustrative representation of a clustering result including two users, and does not constitute a limitation on the number of users in the clustering result. The clustering result can include three, four, or other numbers of users. Taking a clustering result including three users as an example... .

[0043] The total channel gain for each user can be expressed as: ; Where G represents the channel gain from the base station (BS) to the metasurface (ULA-STAR-RIS); This is expressed as the channel gain from the STAR-RIS surface to the user; .

[0044] user The received signal is: ; in, It is represented as additive Gaussian white noise.

[0045] S202, based on the received signals of each user in the clustering results, perform serial interference cancellation operation on the clustering results to determine the signal interference-to-noise ratio of each user in the clustering results.

[0046] For ease of representation and .

[0047] Due to the complexity of receiver decoding, continuous interference cancellation (SIC) is applied to each individual cluster rather than to all users within the system. Within each cluster, closer users are considered to have stronger decoding capabilities than farther users. Therefore, signals from farther users can be decoded on both their own and those of closer users. However, due to the limited decoding capabilities of farther users, signals from closer users cannot be decoded on farther users. SIC eliminates intra-cluster interference from farther users when demodulating signals from closer users. However, intra-cluster interference from closer users cannot be eliminated when demodulating signals from farther users.

[0048] In some embodiments, the clustering result includes a first user and a second user. The distance between the first user (i.e., the near user) and the metasurface is closer than the distance between the second user (i.e., the far user) and the metasurface. Determining the signal-to-interference-plus-noise ratio (SNR) of each user in the clustering result based on the phase shift vector of the metasurface includes: Based on the power allocation factor of the first user, the out-of-cluster interference of the first user, the channel gain and the phase shift vector, the signal-to-interference-noise ratio of the first user is obtained.

[0049] For example, the signal-to-noise ratio of the first user is expressed as: .

[0050] Based on the power allocation factor of the first user, the power allocation factor of the second user, the out-of-cluster interference of the first user, the channel gain, and the phase shift vector, the signal-to-interference-noise ratio of the second user relative to the first user is obtained.

[0051] For example, the signal-to-interference-plus-noise ratio (SNR) of the second user relative to the first user is expressed as: .

[0052] Based on the power allocation factor of the first user, the power allocation factor of the second user, the out-of-cluster interference of the second user, the channel gain, and the phase shift vector, the signal-to-interference-plus-noise ratio of the second user is obtained.

[0053] For example, the signal-to-noise ratio of the second user itself is expressed as: .

[0054] Inter-cluster interference is represented as follows: .

[0055] In some embodiments, when performing step S101, determining the beammap gain based on the phase shift vector of the metasurface includes: S301 determines the beammap gain based on the phase shift vector, beam parameters, channel gain, and target steering vector.

[0056] The target steering vector characterizes the directional response of the transmitted signal from the array antenna. The beam parameters are the parameters of the beams corresponding to the clustering results.

[0057] The covariance matrix of the incident signal after transmission or reflection is expressed as: ; in, Represented as A diagonal matrix.

[0058] The beammap gain for each user can be expressed as: ; in, , which is the steering vector of the perceived target, used to describe the directional response of the signal transmitted by the array antenna.

[0059] To facilitate the solution, we introduce... ,but .

[0060] In some embodiments, when performing step S102, a phase shift optimization function is constructed based on the signal-to-interference-to-noise ratio, beammap gain, and metasurface amplitude constraints, including: S401, by introducing the positive semi-definite matrix and the penalty term into the phase shift optimization function, the updated phase shift optimization function is obtained.

[0061] In some embodiments, the phase shift optimization function can be expressed as: ; in, It can be the SINR calculated based on the user's minimum target rate; , and The constraints are the QoS conditions for the communication users; Amplitude constraints for each reflection or transmission unit. This is expressed as the sum of the corresponding transmission factor and reflection factor being 1.

[0062] This function is not a convex problem and is difficult to transform into an equivalent convex problem, as follows: The objective function (i.e., the phase shift optimization function) is an expression of the form max min, and such functions are usually not continuous.

[0063] Secondly, the optimization problem involves non-affine equality constraints: .

[0064] Faced with equality constraints as shown in the original problem, semidefinite relaxation is often used to transform quadratic equality constraints into affine equality constraints. However, semidefinite programming inevitably introduces rank-one constraints. Problems with rank-one constraints are generally considered NP-hard, and solving such problems often involves high complexity.

[0065] To address the above issues, for the max-min expression in the objective function, in some embodiments, an auxiliary variable is first introduced. Replace the original objective function. In addition, to address the non-convexity issue, we first use semidefinite programming to transform all quadratic expressions concerning the beam in the constraints into linear ones. Let... , The phase shift optimization function can be transformed into: ; in,

[0066] External interference is represented as: ; In this way, the discontinuous objective function is transformed into a continuous and linear one.

[0067] However, semidefinite programming methods inevitably introduce rank-one constraints. To address this issue, some embodiments employ a penalty function method based on continuous convex approximation to resolve this constraint. For example, a semidefinite matrix... satisfy If and only if The equation holds true when the rank of is 1.

[0068] The above relationship is added as a penalty term to the objective function, expressed as: ; in, As a penalty factor.

[0069] (12b)-(12h) is expressed as the following formula: .

[0070] S402, perform Taylor expansion on the updated phase shift optimization function to obtain the objective optimization function.

[0071] In some embodiments, the method of continuously convex approximating the penalty term, i.e., obtaining the objective optimization function by performing a first-order Taylor expansion of the original function at the iteration point using the Taylor formula, is as follows: ; in, For the first The iteration point of the nth iteration, and .

[0072] In some embodiments, the objective of the target optimization function includes maximizing the minimum value among the sensing performance metrics corresponding to each target direction. That is, maximizing the minimum beam pattern gain in the sensing direction of the system.

[0073] The target direction is determined based on the signal-to-interference-to-noise ratio, beam pattern gain, and the amplitude constraint of the metasurface.

[0074] In some embodiments, when performing step S103, if the phase shift optimization function satisfies the convergence condition, the phase shift optimization result is obtained, including: S501, based on the phase shift angle, iteratively solves the objective optimization function.

[0075] S502: After each iteration, the value of the penalty term is adjusted to make the objective optimization function converge.

[0076] S503, the optimal value after the t-th iteration and the optimal value after the t-th iteration. If the optimal value of the first iteration is less than or equal to the difference threshold, the convergence condition is satisfied, and the phase shift optimization result is obtained. Here, t is a positive integer greater than or equal to 2.

[0077] Using continuous convex approximation, variables The algorithm iterates continuously. The system's beam and power allocation factors are already fixed. A random value can be set for each phase shift unit. The phase shift angle is then determined. Next, the objective function is iteratively solved. After each iteration, the penalty factor needs to be increased to ensure algorithm convergence. When the algorithm converges, i.e., in the first iteration... The optimal value after the nth iteration and the th iteration If the optimal value of the next iteration is reduced to a value equal to the difference threshold, a suboptimal solution to the original problem can be obtained.

[0078] In some embodiments, a scheme that clusters users and assigns a beam to each cluster instead of assigning a beam to each user effectively improves beam utilization efficiency. By implementing SIC within each cluster instead of SIC for all users, the complexity and hardware cost of downlink user decoding are greatly reduced.

[0079] In some embodiments, under the premise that the original problem is non-convex, semi-definite relaxation, penalty function method, and continuous convex approximation methods effectively transform the original problem into a suboptimal problem and solve for the suboptimal solution of the original problem. The proposed scheme adopts an iterative approach based on penalty functions, which effectively avoids the high complexity problem of algorithms in NP-hard problems.

[0080] In some embodiments, by designing a max-min problem for a sensing system, the communication service quality of the sensing system and the sensing performance in each target direction are guaranteed, preventing the risk of sensing failure due to excessively small beam pattern gain in a certain target direction.

[0081] This application also provides a phase shift optimization system for communication sensing networks, used to perform the above-described phase shift optimization method.

[0082] Figure 3 This is a schematic flowchart illustrating a phase shift optimization system for a communication sensing network, provided as an embodiment of this application. Figure 3 As shown, in some embodiments, the phase-shift optimization system for communication-aware networks includes: The signal parameter calculation module is configured to use a metasurface-based phase shift vector to determine the signal-to-interference-plus-noise ratio and beammap gain for each user in the clustering results.

[0083] The optimization function construction module is configured to construct a phase shift optimization function based on signal-to-interference-to-noise ratio, beammap gain, and metasurface amplitude constraints.

[0084] The phase shift optimization solution module is configured to obtain the phase shift optimization result when the phase shift optimization function satisfies the convergence condition.

[0085] In some embodiments, the signal parameter calculation module is configured to assign a beam to each clustering result, determine the received signal of each user in the clustering result in the beam, perform serial interference cancellation operation on the clustering result based on the received signal of each user in the clustering result, and determine the signal-to-interference-plus-noise ratio of each user in the clustering result.

[0086] In some embodiments, the signal parameter calculation module is configured to: obtain the signal-to-interference-plus-noise ratio (SNR) of the first user itself based on the power allocation factor of the first user, the out-of-cluster interference of the first user, the channel gain, and the phase shift vector; obtain the SNR of the second user relative to the first user based on the power allocation factor of the first user, the power allocation factor of the second user, the out-of-cluster interference of the first user, the channel gain, and the phase shift vector; and obtain the SNR of the second user itself based on the power allocation factor of the first user, the power allocation factor of the second user, the out-of-cluster interference of the second user, the channel gain, and the phase shift vector.

[0087] In some embodiments, the signal parameter calculation module is configured to determine the beammap gain based on the phase shift vector, beam parameters, channel gain, and target steering vector; wherein the target steering vector is used to characterize the directional response of the transmitted signal of the array antenna; and the beam parameters are the parameters of the beam corresponding to the clustering result.

[0088] In some embodiments, the optimization function construction module is configured to introduce a positive semi-definite matrix and a penalty term into the phase shift optimization function to obtain an updated phase shift optimization function; and to perform a Taylor expansion on the updated phase shift optimization function to obtain the target optimization function.

[0089] In some embodiments, the phase shift optimization solution module is configured to iteratively solve the objective optimization function based on the phase shift angle; after each iteration, the value of the penalty term is adjusted to make the objective optimization function converge; the optimal value after the t-th iteration and the optimal value after the t-th iteration are compared with the optimal value after the t-th iteration. If the optimal value of the first iteration is less than or equal to the difference threshold, the convergence condition is satisfied, and the phase shift optimization result is obtained; where t is a positive integer greater than or equal to 2.

[0090] In some solutions, multiple embodiments of this application can be combined, and the combined solution can be implemented. Optionally, some operations in the processes of each method embodiment may be combined, and / or the order of some operations may be changed. Furthermore, the execution order between the steps of each process is merely exemplary and does not constitute a limitation on the execution order between steps; other execution orders are also possible. It is not intended to indicate that the execution order is the only possible order in which these operations can be performed. Those skilled in the art will conceive of various ways to reorder the operations described herein. In addition, it should be noted that the process details involved in one embodiment of this document are similarly applicable to other embodiments, or different embodiments may be combined.

[0091] Furthermore, some steps in the method embodiments can be equivalently replaced with other possible steps. Alternatively, some steps in the method embodiments may be optional and can be deleted in certain use cases. Or, other possible steps may be added to the method embodiments. Moreover, the various method embodiments can be implemented individually or in combination.

[0092] According to embodiments of the present application, the present application also provides an electronic device, a readable storage medium, and a computer program product.

[0093] Figure 4 This is a schematic block diagram of an example electronic device provided in the embodiments of this application. For example... Figure 4 As shown, the electronic device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in ROM (Read-Only Memory) 1002 or a computer program loaded from storage unit 1008 into RAM (Random Access Memory) 1003. The RAM 1003 may also store various programs and data required for the operation of the electronic device 1000. The computing unit 1001, ROM 1002, and RAM 1003 are interconnected via bus 1004. An I / O (Input / Output) interface 1005 is also connected to bus 1004.

[0094] Multiple components in electronic device 1000 are connected to I / O interface 1005, including: input unit 1006, such as keyboard, mouse, etc.; output unit 1007, such as various types of displays, speakers, etc.; storage unit 1008, such as disk, optical disk, etc.; and phase shift optimization unit 1009 for communication sensing network, such as network card, modem, wireless phase shift optimized transceiver for communication sensing network, etc. The phase shift optimization unit 1009 for communication sensing network allows electronic device 1000 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0095] The computing unit 1001 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 1001 performs the various methods and processes described above, such as a phase-shift optimization method for communication-aware networks. For example, in some embodiments, the phase-shift optimization method for communication-aware networks can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1008. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1000 via ROM 1002 and / or the phase-shift optimization unit 1009 for communication-aware networks. When the computer program is loaded into RAM 1003 and executed by computing unit 1001, one or more steps of the method described above can be performed. Alternatively, in other embodiments, computing unit 1001 can be configured to perform the aforementioned phase-shift optimization method for communication-aware networks by any other suitable means (e.g., by means of firmware).

[0096] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] Program code used to implement the methods of the embodiments of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0098] In the context of embodiments of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fibers, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0099] To provide interaction with the external environment, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the external environment (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball), through which the external environment can provide input to the computer. Other types of devices can also be used to provide interaction with the external environment; for example, feedback provided to the external environment can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the external environment can be received in any form (including sound input, voice input, or tactile input).

[0100] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., external environment computers with a graphical external environment interface or web browser, through which the external environment can interact with the implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected using digital data of any form or medium for phase-shift optimization of communication-aware networks (e.g., phase-shift optimization networks for communication-aware networks). Examples of phase-shift optimization networks for communication-aware networks include: LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0101] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact through phase-shift optimized networks used for communication-aware networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, also known as cloud computing servers or cloud hosts, a hosting product within the cloud computing service ecosystem that addresses the management difficulties and weak business scalability inherent in traditional physical hosts and VPS (Virtual Private Server) services. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0102] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A phase shift optimization method for communication sensing networks, characterized in that, The method includes: Based on the phase shift vector of the metasurface, the signal-to-interference-plus-noise ratio and beammap gain of each user in the clustering results are determined. Based on the signal interference-to-noise ratio, the beam pattern gain, and the amplitude constraint of the metasurface, a phase shift optimization function is constructed. When the phase shift optimization function satisfies the convergence condition, the phase shift optimization result is obtained.

2. The phase shift optimization method for communication sensing networks according to claim 1, characterized in that, The phase shift vector based on the metasurface is used to determine the signal-to-interference-plus-noise ratio (SNR) of each user in the clustering results, including: Assign a beam to each of the clustering results and determine the received signal of each user in the clustering results in the beam; Based on the received signals of each user in the clustering results, a serial interference cancellation operation is performed on the clustering results to determine the signal-to-interference-plus-noise ratio of each user in the clustering results.

3. The phase shift optimization method for communication sensing networks according to claim 2, characterized in that, The clustering results include a first user and a second user; the distance between the first user and the metasurface is closer than the distance between the second user and the metasurface. The phase shift vector based on the metasurface is used to determine the signal-to-interference-plus-noise ratio (SNR) of each user in the clustering results, including: Based on the power allocation factor of the first user, the out-of-cluster interference of the first user, the channel gain, and the phase shift vector, the signal-to-interference-noise ratio of the first user is obtained. Based on the power allocation factor of the first user, the power allocation factor of the second user, the out-of-cluster interference of the first user, the channel gain, and the phase shift vector, the signal-to-interference-noise ratio of the second user relative to the first user is obtained; Based on the power allocation factor of the first user, the power allocation factor of the second user, the out-of-cluster interference of the second user, the channel gain, and the phase shift vector, the signal-to-interference-plus-noise ratio of the second user is obtained.

4. The phase shift optimization method for communication sensing networks according to claim 1, characterized in that, The determination of beammap gain based on the metasurface-based phase shift vector includes: The beammap gain is determined based on the phase shift vector, beam parameters, channel gain, and target steering vector; wherein the target steering vector is used to characterize the directional response of the transmitted signal of the array antenna; and the beam parameters are the parameters of the beam corresponding to the clustering result.

5. The phase shift optimization method for communication sensing networks according to claim 4, characterized in that, The phase shift optimization function is constructed based on the signal interference-to-noise ratio, the beammap gain, and the amplitude constraint of the metasurface; including: By incorporating the positive semi-definite matrix and the penalty term into the phase shift optimization function, the updated phase shift optimization function is obtained; The updated phase shift optimization function is then subjected to Taylor expansion to obtain the target optimization function.

6. The phase shift optimization method for communication sensing networks according to claim 5, characterized in that, The optimization objective of the target optimization function includes maximizing the minimum value among the perception performance indicators corresponding to each target direction; The target direction is determined based on the signal interference-to-noise ratio, the beam pattern gain, and the amplitude constraint of the metasurface.

7. The phase shift optimization method for communication sensing networks according to claim 6, characterized in that, The step of obtaining the phase shift optimization result when the phase shift optimization function satisfies the convergence condition includes: Based on the phase shift angle, the objective optimization function is solved iteratively; After each iteration, the value of the penalty term is adjusted to bring the objective optimization function to converge. In the The optimal value after the nth iteration and the optimal value after the nth iteration If the optimal value of the next iteration is less than or equal to the difference threshold, the convergence condition is satisfied, and the phase shift optimization result is obtained; where t is a positive integer greater than or equal to 2.

8. The phase shift optimization method for communication sensing networks according to any one of claims 1-7, characterized in that, Determining the phase shift vector of the metasurface includes: The phase shift vector is determined based on the phase shift angle and a first factor; the first factor is the transmission or reflection factor of the metasurface.

9. The phase shift optimization method for communication sensing networks according to any one of claims 1-7, characterized in that, The metasurface is a reconfigurable smart surface called STAR RIS that simultaneously transmits and reflects light.

10. A phase shift optimization system for communication sensing networks, characterized in that, Used to perform the method according to any one of claims 1 to 9.

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