Wireless communication and sensing methods and systems based on smart reflective surfaces

Abstract

The application discloses a wireless communication and sensing method and system based on an intelligent reflecting surface, the intelligent reflecting surface is applied to a wireless communication and sensing system, the intelligent reflecting surface has a super-diagonal structure, the method comprises the following steps: determining a target optimization task based on a communication channel matrix, a sensing channel matrix, an initial scattering matrix, an initial precoding matrix and initial communication rate allocation information; determining a first optimization task based on the communication channel matrix, the sensing channel matrix and the initial scattering matrix under the initial precoding matrix and the initial communication rate allocation information, and obtaining a target scattering matrix by solving based on a symmetric unitary projection algorithm; transforming the target optimization task based on the target scattering matrix to obtain a second optimization task, and obtaining a target precoding matrix and target communication rate allocation information by solving, and then performing communication sensing. The application can reduce interference in the communication sensing process and improve the system communication sensing performance.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a wireless communication and sensing method and system based on an intelligent reflective surface. Background Technology

[0002] With the continuous growth of wireless communication network applications, the demand for high-quality wireless connectivity and high-precision sensing capabilities in next-generation wireless networks is becoming increasingly urgent. Integrated Sensing and Communication (ISAC), as a key communication technology, effectively integrates communication and sensing functions, aiming to support intelligent applications such as smart manufacturing and intelligent transportation. Current technologies for ISAC systems mainly focus on the design of the transmitter and receiver, neglecting proactive optimization of the wireless propagation environment. Furthermore, when a large number of devices and applications require services, the system is susceptible to interference from multiple users or resource allocation issues, making it difficult to guarantee system connectivity and service quality. How to enhance the intelligent control capabilities of ISAC systems to the wireless channel environment, while simultaneously improving interference management capabilities, and building efficient ISAC systems has become a crucial problem that urgently needs to be solved. Summary of the Invention

[0003] To address the aforementioned problems in the prior art, this invention discloses a wireless communication and sensing method and system based on an intelligent reflective surface. This method reduces interference during the communication and sensing process, thereby improving the system's communication and sensing performance and achieving a balance between communication and sensing performance. The technical solution disclosed in this invention is as follows:

[0004] According to one aspect of the embodiments disclosed in this invention, a wireless communication and sensing method based on a smart reflective surface is provided. The smart reflective surface is applied in a wireless communication and sensing system, which further includes a signal transmitter, a signal receiver, and a target to be sensed. The smart reflective surface has a super-diagonal structure. The method includes:

[0005] The system acquires the initial precoding matrix corresponding to the signal transmitter, the initial communication rate allocation information corresponding to the signal receiver, the initial scattering matrix corresponding to the smart reflector, and the echo signal of the target to be sensed received by the signal transmitter; the echo signal is reflected back to the signal transmitter by the smart reflector.

[0006] Based on the communication channel matrix corresponding to the signal receiver, the sensing channel matrix corresponding to the target to be sensed, the initial scattering matrix, the initial precoding matrix, and the initial communication rate allocation information, a target optimization task is determined. The target optimization task aims to maximize the sensing signal-to-noise ratio of the echo signal and optimizes the initial scattering matrix, the initial precoding matrix, and the initial communication rate allocation information to satisfy scattering constraints, communication rate constraints, and communication power constraints.

[0007] Based on the initial precoding matrix and the initial communication rate allocation information, a first optimization task is determined according to the communication channel matrix, the sensing channel matrix, and the initial scattering matrix. The first optimization task aims to maximize the channel gain and optimizes the initial scattering matrix to satisfy the scattering constraints.

[0008] Based on the symmetric unitary projection algorithm, the first optimization task is solved to obtain the target scattering matrix;

[0009] The target optimization task is transformed based on the target scattering matrix to obtain a second optimization task;

[0010] The second optimization task is solved to obtain the target precoding matrix and target communication rate allocation information;

[0011] Communication and sensing are performed based on the target scattering matrix, the target precoding matrix, and the target communication rate allocation information.

[0012] Optionally, the target optimization task includes a first objective function, the scattering constraint, the communication rate constraint, and the communication power constraint. Determining the target optimization task based on the communication channel matrix corresponding to the signal receiver, the sensing channel matrix corresponding to the target to be sensed, the initial scattering matrix, the initial precoding matrix, and the initial communication rate allocation information includes:

[0013] The transmitted signal from the signal transmitter is obtained. The transmitted signal carries a target public message and a target private message. The initial precoding matrix includes a public precoding vector for encoding the target public message and a private precoding vector for encoding the target private message.

[0014] Based on the communication channel matrix, the public precoding vector, the proprietary precoding vector, and the initial communication rate allocation information, the communication rate constraint conditions are determined.

[0015] Based on the sensing channel matrix and the initial precoding matrix, the sensing signal-to-noise ratio of the echo signal is calculated.

[0016] The maximum value of the perceived signal-to-noise ratio of the echo signal is determined as the first objective function;

[0017] The transmit power is calculated based on the initial precoding matrix and its conjugate transpose.

[0018] The communication power constraints are determined based on the relationship between the transmission power and the total power of the system.

[0019] The scattering constraint conditions are determined based on the relationship between the initial scattering matrix and the preset non-convex set.

[0020] Optionally, determining the communication rate constraint based on the communication channel matrix, the public precoding vector, the proprietary precoding vector, and the initial communication rate allocation information includes:

[0021] Based on the communication channel matrix, the public precoding vector, and the proprietary precoding vector, the first transmission rate of the target public message corresponding to the signal receiving end is calculated;

[0022] Based on the communication channel matrix and the proprietary precoding vector, the second transmission rate of the target proprietary message corresponding to the signal receiving end is calculated.

[0023] Based on the relationship between the first transmission rate and the initial communication rate allocation information, the first communication rate constraint condition is determined.

[0024] Based on the initial communication rate allocation information, the second transmission rate, and the preset lower limit transmission rate corresponding to the signal receiving end, the second communication rate constraint condition is determined.

[0025] The communication rate constraint is determined based on the first communication rate constraint, the second communication rate constraint, and the third communication rate constraint corresponding to the initial communication rate allocation information.

[0026] Optionally, the first optimization task includes a second objective function and the scattering constraints, and determining the first optimization task based on the communication channel matrix, the sensing channel matrix, and the initial scattering matrix includes:

[0027] Based on the communication channel matrix and the initial scattering matrix, the communication channel gain is calculated.

[0028] Based on the sensing channel matrix and the initial scattering matrix, the sensing channel gain is calculated;

[0029] The maximum value of the sum of the communication channel gain and the sensing channel gain is determined as the second objective function;

[0030] The scattering constraint conditions are determined based on the relationship between the initial scattering matrix and the preset non-convex set.

[0031] Optionally, the communication channel matrix includes a first channel matrix from the signal transmitter to the smart reflector and a second channel matrix from the smart reflector to the signal receiver, the sensing channel matrix includes the first channel matrix and a third channel matrix from the smart reflector to the target to be sensed, and the first optimization task includes a second objective function;

[0032] The target scattering matrix obtained by solving the first optimization task based on the symmetric unitary projection algorithm includes:

[0033] The initial scattering matrix is ​​processed into column vectors to obtain the initial scattering vector;

[0034] The transpose of the product of the first channel matrix and its conjugate transpose is multiplied by the product of the second channel matrix and its conjugate transpose, and the product of the third channel matrix and its conjugate transpose, respectively, to obtain the first target matrix and the second target matrix.

[0035] The first optimization task is transformed to obtain a first optimization subtask; the first optimization subtask includes a third objective function and a first scattering constraint condition. The third objective function is obtained by transforming the second objective function based on the initial scattering vector, the first objective matrix, and the second objective matrix. The first scattering constraint condition is determined based on the relationship between the initial scattering vector and a preset convex set. The preset convex set is obtained by relaxing a preset non-convex set.

[0036] The first target matrix and the second target matrix are summed to obtain the target self-conjugate matrix, which includes the first target unitary matrix.

[0037] Based on the first target unitary matrix, the first optimization subtask is solved to obtain the target relaxed scattering vector;

[0038] The target relaxation scattering vector is reconstructed to obtain the first target relaxation scattering matrix;

[0039] The first target relaxed scattering matrix is ​​projected onto the preset non-convex set and eigenvalue decomposition is performed to obtain the decomposed scattering matrix, which includes the second target unitary matrix.

[0040] The target scattering matrix is ​​determined based on the second target unitary matrix.

[0041] Optionally, after summing the first target matrix and the second target matrix to obtain the target self-conjugate matrix, the method further includes:

[0042] The first optimization subtask is transformed based on the target self-conjugate matrix to obtain the second optimization subtask. The second optimization subtask includes a fourth objective function and the first scattering constraint condition. The fourth objective function is obtained by transforming the third objective function based on the target self-conjugate matrix.

[0043] Accordingly, the step of solving the first optimization subtask based on the first target unitary matrix to obtain the target relaxed scattering vector includes:

[0044] Based on the first target unitary matrix, the second optimization subtask is solved to obtain the second target relaxed scattering matrix;

[0045] The target relaxation scattering vector is determined based on the second target relaxation scattering matrix.

[0046] Optionally, transforming the first optimization task to obtain the first optimization subtask includes:

[0047] The first optimization task is transformed to obtain a third optimization subtask; the third optimization subtask includes the second objective function and the second scattering constraint, the second scattering constraint is determined based on the relationship between the initial scattering matrix and the preset convex set, and the first objective relaxed scattering matrix is ​​the optimization result of the third optimization subtask;

[0048] Based on the first target matrix and the second target matrix, the third optimization subtask is transformed to obtain the first optimization subtask.

[0049] Optionally, the step of solving the second optimization task to obtain the target precoding matrix and target communication rate allocation information includes:

[0050] The transmitted signal from the signal transmitter is obtained, as well as the initial common equalization vector and the initial proprietary equalization vector corresponding to the signal receiver. The transmitted signal carries a target common message and a target proprietary message. The initial precoding matrix includes a common precoding vector for encoding the target common message and a proprietary precoding vector for encoding the target proprietary message.

[0051] The common message at the signal receiver is estimated based on the initial common equalization vector to obtain the estimated common message;

[0052] The proprietary message of the signal receiver is estimated based on the initial proprietary equalization vector to obtain the estimated proprietary message.

[0053] Based on the estimated public message and the target public message, the mean square error of the public message is calculated, and based on the estimated private message and the target private message, the mean square error of the private message is calculated.

[0054] Based on the communication channel matrix, the public precoding vector, and the proprietary precoding vector, the first transmission rate of the target public message and the second transmission rate of the target proprietary message corresponding to the signal receiving end are calculated.

[0055] Based on the correspondence between the first transmission rate, the second transmission rate, the mean square error of the public message and its corresponding first initial weight information, and the mean square error of the private message and its corresponding second initial weight information, the second optimization task is transformed to obtain the fourth optimization subtask.

[0056] Solving the fourth optimization subtask yields the target precoding matrix and the target communication rate allocation information.

[0057] Optionally, the fourth optimization subtask aims to maximize the perceived signal-to-noise ratio of the echo signal by alternately optimizing the first initial parameter combination and the second initial parameter combination to satisfy the first updated communication rate constraint and the communication power constraint. The first updated communication rate constraint is obtained by transforming the communication rate constraint based on the correspondence. The first initial parameter combination includes the initial precoding matrix and the initial communication rate allocation information. The second initial parameter combination includes the initial common equalization vector, the initial private equalization vector, the first initial weight information, and the second initial weight information. The fourth optimization subtask includes a first objective function.

[0058] The step of solving the fourth optimization subtask to obtain the target precoding matrix and the target communication rate allocation information includes:

[0059] Based on the initial precoding matrix and the initial communication rate allocation information, the second initial parameter combination is optimized to obtain the second target parameter combination; the second target parameter combination includes a target common equilibrium vector, a target specific equilibrium vector, first target weight information, and second target weight information;

[0060] Under the second objective parameter combination, the first updated communication rate constraint and the communication power constraint are transformed to obtain the second updated communication rate constraint and the first communication power constraint, and the first objective function is reconstructed and approximated to obtain the approximate objective function.

[0061] Based on the approximate objective function, the second update communication rate constraint, and the first communication power constraint, the fifth optimization subtask is obtained;

[0062] Solving the fifth optimization subtask yields the target precoding matrix and the target communication rate allocation information.

[0063] According to another aspect of the embodiments disclosed in this invention, a wireless communication and sensing system is provided. The system includes a signal transmitter, a signal receiver, a smart reflective surface, and a target to be sensed. The smart reflective surface has a super-diagonal structure and is used for signal transmission between the signal transmitter and the signal receiver, and between the smart reflective surface and the target to be sensed. The system performs wireless communication and sensing based on the wireless communication and sensing method based on the smart reflective surface described in any of the preceding claims.

[0064] The present invention provides a wireless communication and sensing method and system based on a smart reflective surface, which has the following technical effects:

[0065] In this invention, an intelligent reflective surface is applied to a wireless communication and sensing system. The system also includes a signal transmitter, a signal receiver, and a target to be sensed. The intelligent reflective surface has a super-diagonal structure. First, the initial precoding matrix corresponding to the signal transmitter, the initial communication rate allocation information corresponding to the signal receiver, the initial scattering matrix corresponding to the intelligent reflective surface, and the echo signal from the target to be sensed received by the signal transmitter are acquired. The echo signal is reflected back to the signal transmitter by the intelligent reflective surface. Then, based on the communication channel matrix corresponding to the signal receiver, the sensing channel matrix corresponding to the target, the initial scattering matrix, the initial precoding matrix, and the initial communication rate allocation information, a target optimization task is determined. The target optimization task aims to maximize the sensing signal-to-noise ratio of the echo signal, and adjusts the initial scattering matrix, initial precoding matrix, and initial communication rate allocation information. The rate allocation information is optimized to satisfy scattering constraints, communication rate constraints, and communication power constraints. Further, based on the initial precoding matrix and initial communication rate allocation information, a first optimization task is determined using the communication channel matrix, sensing channel matrix, and initial scattering matrix. This first optimization task aims to maximize channel gain by optimizing the initial scattering matrix to satisfy scattering constraints. The first optimization task is then solved using a symmetric unitary projection algorithm to obtain the target scattering matrix. Subsequently, the target optimization task is transformed based on the target scattering matrix to obtain a second optimization task, which is then solved to obtain the target precoding matrix and target communication rate allocation information. Communication and sensing are then performed based on the target scattering matrix, target precoding matrix, and target communication rate allocation information. This reduces interference during the communication and sensing process, thereby improving the system's communication and sensing performance and achieving a balance between communication and sensing performance.

[0066] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 This is a schematic diagram illustrating an application environment of a wireless communication and sensing method based on a smart reflective surface, according to an exemplary embodiment.

[0069] Figure 2 This is a flowchart illustrating a wireless communication and sensing method based on a smart reflective surface, according to an exemplary embodiment.

[0070] Figure 3 This is a flowchart illustrating a task for determining a target optimization objective, according to an exemplary embodiment.

[0071] Figure 4 This is a schematic diagram illustrating a process for determining a first optimization task according to an exemplary embodiment;

[0072] Figure 5 This is a schematic diagram illustrating a process for solving a first optimization task according to an exemplary embodiment;

[0073] Figure 6 This is a schematic diagram illustrating a process for solving a second optimization task according to an exemplary embodiment;

[0074] Figure 7 This is a schematic diagram illustrating the relationship between base station transmit power and perceived signal-to-noise ratio in a simulation experiment according to an exemplary embodiment;

[0075] Figure 8 This is a schematic diagram illustrating the relationship between the number of reflective units of a smart reflective surface and the perceived signal-to-noise ratio in a simulation experiment according to an exemplary embodiment.

[0076] Figure 9 This is a schematic diagram illustrating the relationship between the user communication rate threshold and the perceived signal-to-noise ratio in a simulation experiment according to an exemplary embodiment. Detailed Implementation

[0077] To enable those skilled in the art to better understand the technical solutions disclosed in this invention, the technical solutions in the disclosed embodiments will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0078] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention disclosed herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0079] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an application environment of a wireless communication and sensing method based on a smart reflective surface according to an exemplary embodiment. The application environment may include at least a signal transmitter, a signal receiver, a target to be sensed, and a smart reflective surface.

[0080] In an optional embodiment, the signal transmitter can be a base station, which can provide communication services to the signal receiver while simultaneously sensing the target to be sensed. The signal receiver can be a user terminal, specifically a single-antenna user terminal, and the system can include multiple signal receivers.

[0081] In real-world dense scenarios, there may be obstacles between the signal transmitter and receiver, and between the target to be sensed. That is, the line of sight (LoS) path from the base station to the user and the target may be blocked by obstacles. Deploying a smart reflector with a super-diagonal structure (Beyond-Diagonal RIS, BD-RIS) can provide communication services for multiple users and support target perception.

[0082] Specifically, the intelligent reflective surface has a super-diagonal structure, and is used for signal transmission between the signal transmitter and receiver, and between the transmitter and the target to be sensed. The signal transmitter transmits a communication sensing signal, which is reflected by the intelligent reflective surface and sent to both the signal receiver and the target. The signal transmitter provides communication services to the signal receiver. Furthermore, the echo signal received by the target is reflected by the intelligent reflective surface and sent back to the signal transmitter, which then senses the target based on this echo.

[0083] In addition, it should be noted that, Figure 1 The example shown is merely an application environment for a wireless communication and sensing method based on a smart reflective surface, and the embodiments in this specification are not limited to the above.

[0084] In the upcoming era of sixth-generation (6G) wireless communication, a variety of emerging applications such as autonomous driving, virtual reality, and digital twins are emerging. To support these innovative technologies, communication systems not only need high throughput but also high-precision environmental perception capabilities, thus placing higher demands on spectrum efficiency and resource utilization. Sensing-communication integration (ISAC), as one of the key communication technologies, shows broad prospects in realizing these applications. ISAC systems can achieve coordinated operation of communication and sensing by sharing wireless resources, signal processing frameworks, and hardware devices, thereby effectively improving spectrum efficiency and resource utilization. Existing technologies mainly focus on the design of the transmitter and receiver, such as beamforming, joint estimation of communication and sensing parameters, and optimization of wireless resource scheduling, but neglect the active optimization of the wireless propagation environment. Furthermore, the additional controllable scattering degrees of freedom bring high computational complexity. Moreover, when a large number of devices and applications need to provide services, the system is susceptible to multi-user interference or resource allocation issues, making it difficult to guarantee system connectivity and service quality. To enhance the intelligent control capabilities of sensing-communication integration systems for the wireless channel environment and improve interference management capabilities, how to build an efficient ISAC system has become an important problem that urgently needs to be solved.

[0085] The following describes a wireless communication and sensing method based on a smart reflective surface provided in this application. Figure 2 This is a flowchart illustrating a wireless communication and sensing method based on a smart reflective surface according to an exemplary embodiment. This specification provides the method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual system or server products, the method can be executed sequentially according to the embodiments or drawings, or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown... Figure 2 As shown, the above method may include:

[0086] S201: Obtain the initial precoding matrix corresponding to the signal transmitter, the initial communication rate allocation information corresponding to the signal receiver, the initial scattering matrix corresponding to the smart reflector, and the echo signal of the target to be sensed received by the signal transmitter.

[0087] In one specific embodiment, the smart reflector has a super-diagonal structure and is applied in a wireless communication and sensing system (a sensor-integrated system). This system also includes a signal transmitter, a signal receiver, and a target to be sensed. Specifically, the signal transmitter can be a base station, which can provide communication services to the signal receiver while simultaneously sensing the target. The signal receiver can be a user terminal, specifically a single-antenna user terminal, and the system can include multiple signal receivers.

[0088] In real-world dense environments, obstacles may exist between the signal transmitter, the signal receiver, and the target to be sensed. Specifically, the line-of-sight (LoS) path from the base station to the user and the target may be blocked. In an integrated sensing system, a smart reflector with a super-diagonal structure is deployed to provide communication services to multiple users and support target perception. The signal transmitter transmits a communication sensing signal, which is reflected by the smart reflector to the signal receiver and the target. The signal transmitter provides communication services to the signal receiver. Furthermore, the echo signal received by the target is reflected back to the signal transmitter via the smart reflector, allowing the signal transmitter to perceive the target. Specifically, the smart reflector can include multiple reflective elements, each of which can independently adjust its reflection characteristics.

[0089] In one specific embodiment, in order to transmit digital signals, the signal transmitter can encode the information to be sent to the signal receiver using a precoding matrix. At the signal transmitter, the precoding matrix can be used to generate precoded signals required for beamforming and data transmission. By adjusting the phase and amplitude of the antenna array, a beam pointing in a specific direction can be formed to enhance the signal transmission efficiency.

[0090] Specifically, communication rate allocation information can be public message rate allocation information. Rate-Splitting Multiple Access (RSMA) employs a user rate allocation strategy, offering a simpler and more flexible interference management approach. Through a reasonable rate allocation strategy, resource usage among different users can be balanced, improving overall network throughput and user experience. The scattering matrix of the intelligent reflector is used to intelligently control the propagation path of wireless signals to maximize signal gain and minimize interference.

[0091] Specifically, the information to be sent can be divided into a public part and a private part. The initial precoding matrix can include a public precoding vector for encoding the target public message and a private precoding vector for encoding the target private message. The public parts of all users are merged into a single public message, which can be encoded using a shared codebook (public precoding vector) and can be decoded by all users. Alternatively, private messages can be encoded using their own independent codebooks (private precoding vectors) and can only be decoded by their respective users.

[0092] In the embodiments of this specification, the number of signal receivers can be denoted as... Let the set of signal receivers be denoted as Public information and The vector composed of a combination of proprietary messages is denoted as The vector satisfies the following conditions ,in, Indicates matrix transpose. This indicates the conjugate transpose. Indicates the expectation. This indicates that the information is publicly available. This indicates a proprietary message.

[0093] S203: Determine the target optimization task based on the communication channel matrix corresponding to the signal receiver, the sensing channel matrix corresponding to the target to be sensed, the initial scattering matrix, the initial precoding matrix, and the initial communication rate allocation information.

[0094] In one specific embodiment, the communication channel matrix can be used to characterize the equivalent channel from the signal transmitter to the signal receiver. The sensing channel matrix can be used to characterize the equivalent channel between the signal transmitter and the target to be sensed, specifically the equivalent channel from the signal transmitter to the smart reflector to the target, and from the target to the smart reflector to the signal transmitter. The above-mentioned target optimization task aims to maximize the sensing signal-to-noise ratio of the echo signal, and optimizes the initial scattering matrix, the initial precoding matrix, and the initial communication rate allocation information to satisfy scattering constraints, communication rate constraints, and communication power constraints. Specifically, the target optimization task may include a first objective function, scattering constraints, communication rate constraints, and communication power constraints. The scattering constraints may be constraints related to the scattering matrix, the communication rate constraints may be constraints related to the message transmission rate, and the communication power constraints may be constraints related to the transmission power.

[0095] In an optional embodiment, such as Figure 3As shown, the target optimization task, based on the communication channel matrix corresponding to the signal receiver, the sensing channel matrix corresponding to the target to be sensed, the initial scattering matrix, the initial precoding matrix, and the initial communication rate allocation information, can include:

[0096] S301: Acquire the transmitted signal from the signal transmitter.

[0097] In one specific embodiment, the transmitted signal carries both public target information and private target information.

[0098] S303: Determine the communication rate constraints based on the communication channel matrix, public precoding vector, proprietary precoding vector, and initial communication rate allocation information.

[0099] In one specific embodiment, the communication channel matrix may include a first channel matrix from the signal transmitter to the smart reflector and a second channel matrix from the smart reflector to the signal receiver.

[0100] Optionally, the communication rate constraints determined above based on the communication channel matrix, public precoding vector, proprietary precoding vector, and initial communication rate allocation information may include:

[0101] Based on the communication channel matrix, the public precoding vector, and the proprietary precoding vector, the first transmission rate of the target public message corresponding to the signal receiver is calculated.

[0102] Based on the communication channel matrix and proprietary precoding vector, the second transmission rate of the target proprietary message corresponding to the signal receiver is calculated.

[0103] Based on the relationship between the first transmission rate and the initial communication rate allocation information, the constraints of the first communication rate are determined.

[0104] Based on the initial communication rate allocation information, the second transmission rate, and the preset lower limit transmission rate corresponding to the signal receiving end, the constraints of the second communication rate are determined.

[0105] Based on the first communication rate constraint, the second communication rate constraint, and the third communication rate constraint corresponding to the initial communication rate allocation information, the communication rate constraint is determined.

[0106] In one specific embodiment, calculating the first transmission rate of the target public message corresponding to the signal receiver based on the communication channel matrix, public precoding vector, and proprietary precoding vector may include: calculating the first signal-to-interference-plus-noise ratio (SNR) of the target public message corresponding to the signal receiver based on the communication channel matrix, public precoding vector, and proprietary precoding vector; and calculating the first transmission rate based on the first SNR. Specifically, the first SNR can be logarithmically calculated to obtain the first transmission rate.

[0107] In one specific embodiment, the calculation of the second transmission rate of the target proprietary message corresponding to the signal receiver based on the communication channel matrix and the proprietary precoding vector may include: calculating the second signal-to-interference-plus-noise ratio (SINR) of the target proprietary message corresponding to the signal receiver based on the communication channel matrix and the proprietary precoding vector; and calculating the second transmission rate based on the second SINR. Specifically, the second SINR can be logarithmically processed to obtain the second transmission rate.

[0108] In one specific embodiment, to ensure that all users can decode public messages, the overall transmission rate of public messages at the signal receiver can be the minimum of the first transmission rates of multiple signal receivers. The first communication rate constraint can be determined based on the relationship between the overall transmission rate of the public messages and the initial communication rate allocation information. Specifically, the overall transmission rate is greater than or equal to the sum of the initial communication rate allocation information of multiple signal receivers. The first communication rate constraint corresponds to the overall transmission rate of multiple signal receivers.

[0109] In one specific embodiment, determining the second communication rate constraint based on the initial communication rate allocation information, the second transmission rate, and the preset lower limit transmission rate corresponding to the signal receiver may include: summing the initial communication rate allocation information and the second transmission rate to obtain the overall transmission rate of public and private messages at the signal receiver; and determining the second communication rate constraint based on the relationship between the overall transmission rate of public and private messages and the preset lower limit transmission rate. Specifically, the second communication rate constraint corresponds to any signal receiver. The overall transmission rate of public and private messages corresponding to any signal receiver can be the sum of the public message rate (communication rate allocation information) allocated to that signal receiver and the private message transmission rate. The overall transmission rate of public and private messages corresponding to any signal receiver is greater than or equal to the preset lower limit transmission rate corresponding to that signal receiver. The preset lower limit transmission rate can be the minimum communication rate required by the corresponding signal receiver, and can be set according to actual application requirements.

[0110] In one specific embodiment, the third communication rate constraint can be a constraint on the initial communication rate allocation information, i.e., the initial communication rate allocation information should be greater than or equal to zero. The first communication rate constraint, the second communication rate constraint, and the third communication rate constraint corresponding to the initial communication rate allocation information are defined as the communication rate constraint conditions.

[0111] In practical applications, at the signal receiving end, the public message is decoded first. During this stage, the private message is considered interference. When decoding the public message, the first signal-to-interference-plus-noise ratio (SNR) of the k-th user can be calculated using the following formula:

[0112]

[0113] in, This represents the communication channel matrix from the signal transmitter to the k-th signal receiver. This represents the first channel matrix from the signal transmitter to the smart reflector. This represents the second channel matrix from the smart reflector to the k-th signal receiver. The scattering matrix of the intelligent reflector (the initial scattering matrix mentioned above) represents the scattering matrix of the intelligent reflector. This represents the aforementioned public precoding vector. This represents the proprietary precoding vector corresponding to the k-th signal receiver. Indicates the number of signal receivers. This indicates the number of transmitting antennas at the signal transmitter. This indicates the number of reflective elements in the smart reflector. After the signal is pre-coded at the signal transmitter and passes through the channel assisted by the smart reflector (from the signal transmitter to the smart reflector to the signal receiver), it is interfered with by additive white Gaussian noise. This represents the power of the noise. Because the intelligent reflector has unit-connected characteristics, its scattering matrix satisfies a predefined non-convex set. .

[0114] Then, you can use the formula The first transmission rate of the public message at the k-th signal receiver is calculated. To ensure that all signal receivers can decode the public message, the transmission rate of the public message should be the minimum of the first transmission rates of the public messages at all signal receivers, i.e. Therefore, the following relationship can be obtained between the transmission rate of public messages and the initial communication rate allocation information (i.e., the first communication rate constraint mentioned above):

[0115]

[0116] in, This indicates the initial communication rate allocation information mentioned above.

[0117] Next, after successfully decoding the public message, the public message is removed from the received signal at the signal receiver. The signal receiver can then decode the corresponding private message from the remaining messages. During this process, the private messages from other signal receivers are considered interference. The second signal-to-interference-plus-noise ratio (SINNR) for the k-th user can then be calculated using the following formula:

[0118]

[0119] Therefore, the second transmission rate of the proprietary message at the k-th signal receiver is .

[0120] The sum of the initial communication rate allocation information and the second transmission rate of the k-th signal receiver can be the overall transmission rate of the k-th signal receiver. This overall transmission rate should be greater than or equal to the minimum transmission rate required by the signal receiver (i.e., the aforementioned preset lower limit transmission rate). Based on this relationship, the second communication rate constraint condition can be obtained as follows:

[0121]

[0122] in, This indicates the preset lower limit transmission rate.

[0123] Specifically, the third communication rate constraint can be a constraint on the allocation of information for the initial communication rate, i.e. .

[0124] Ultimately, the above three constraints were determined as communication rate constraints.

[0125] S305: The perceived signal-to-noise ratio of the echo signal is calculated based on the sensing channel matrix and the initial precoding matrix.

[0126] In one specific embodiment, because the LoS path between the signal transmitter (base station) and the target to be sensed is obstructed by obstacles, the signal transmitter cannot directly receive the signal from the target. Therefore, the signal transmitter relies on the reflection channel provided by the intelligent reflector to sense the target. The intelligent reflector reflects the echo signal from the target back to the signal transmitter, which then extracts relevant information about the target using signal processing techniques. Specifically, the sensing channel matrix may include the aforementioned first channel matrix and a third channel matrix from the intelligent reflector to the target to be sensed.

[0127] Specifically, the perceived signal-to-noise ratio of the aforementioned echo signal can be determined using the following formula:

[0128]

[0129] in, This refers to the aforementioned sensing communication channel. This represents the third channel matrix from the intelligent reflective surface to the target to be sensed. This represents the precoding matrix mentioned above. Represents the trace of a matrix.

[0130] S307: The maximum value of the perceived signal-to-noise ratio of the echo signal is determined as the first objective function.

[0131] S309: The transmit power is calculated based on the initial precoding matrix and its conjugate transpose.

[0132] Specifically, the transmission power can be calculated using the formula... Calculated.

[0133] S311: Determine the communication power constraints based on the relationship between transmit power and the total power of the system.

[0134] Specifically, if the transmit power is less than or equal to the total power of the system, the communication power constraint condition can be determined as follows: , where P represents the total power of the system.

[0135] S313: Determine the scattering constraints based on the relationship between the initial scattering matrix and the preset non-convex set.

[0136] Specifically, the initial scattering matrix belongs to the pre-defined non-convex set. The scattering constraint condition can be determined as follows: .

[0137] In the embodiments of this specification, the above-mentioned target optimization task can be represented as:

[0138]

[0139] Since the objective optimization task is highly nonconvex, it is difficult to solve directly in the current form. Therefore, the optimization variables can be decoupled and the objective optimization task can be decomposed into two stages of optimization tasks, namely the first optimization task and the second optimization task.

[0140] S205: Given the initial precoding matrix and initial communication rate allocation information, determine the first optimization task based on the communication channel matrix, the sensing channel matrix, and the initial scattering matrix.

[0141] In one specific embodiment, the first optimization task aims to maximize channel gain by optimizing the initial scattering matrix to satisfy scattering constraints. Specifically, the first optimization task may include a second objective function and the aforementioned scattering constraints. In practical applications, the transmit beamforming is first fixed, and the focus is on optimizing the scattering matrix of the smart reflector to maximize channel gain.

[0142] In an optional embodiment, such as Figure 4 As shown, the determination of the first optimization task based on the communication channel matrix, the sensing channel matrix, and the initial scattering matrix may include:

[0143] S401: The communication channel gain is calculated based on the communication channel matrix and the initial scattering matrix.

[0144] Specifically, communication channel gain ,in, This represents the F-norm.

[0145] S403: The sensing channel gain is calculated based on the sensing channel matrix and the initial scattering matrix.

[0146] Specifically, sensing channel gain .

[0147] S405: The maximum value of the sum of the communication channel gain and the sensing channel gain is determined as the second objective function.

[0148] S407: Determine the scattering constraints based on the relationship between the initial scattering matrix and the preset non-convex set.

[0149] In the embodiments described in this specification, the first optimization task mentioned above can be represented as:

[0150]

[0151] in, This represents the Euclidean norm.

[0152] S207: Based on the symmetric unitary projection algorithm, the first optimization task is solved to obtain the target scattering matrix.

[0153] Specifically, the target scattering matrix can be the optimization result of the first optimization task, that is, the optimal solution of the first optimization task.

[0154] In an optional embodiment, such as Figure 5 As shown, the target scattering matrix obtained by solving the first optimization task based on the symmetric unitary projection algorithm can include:

[0155] S501: The initial scattering matrix is ​​processed into column vectors to obtain the initial scattering vector.

[0156] Specifically, the initial scattering vector The `vec` function can be used to convert a matrix into a column vector.

[0157] S503: Perform cyclic multiplication on the transpose of the product of the first channel matrix and its conjugate transpose, and the product of the second channel matrix and its conjugate transpose, and the product of the third channel matrix and its conjugate transpose, respectively, to obtain the first target matrix and the second target matrix.

[0158] Specifically, the first target matrix Second target matrix .

[0159] S505: Transform the first optimization task to obtain the first optimization subtask.

[0160] In one specific embodiment, the first optimization subtask includes a third objective function and a first scattering constraint. The third objective function is obtained by transforming the second objective function based on the initial scattering vector, the first objective matrix, and the second objective matrix. The first scattering constraint is determined based on the relationship between the initial scattering vector and a preset convex set. The preset convex set is obtained by relaxing a preset non-convex set.

[0161] Optionally, the transformation of the first optimization task described above to obtain the first optimization subtask may include:

[0162] The first optimization task is transformed to obtain the third optimization subtask;

[0163] Based on the first objective matrix and the second objective matrix, the third optimization subtask is transformed to obtain the first optimization subtask.

[0164] In one specific embodiment, the third optimization subtask may include a second objective function and a second scattering constraint, the second scattering constraint being determined based on the relationship between the initial scattering matrix and a preset convex set. Specifically, the second scattering constraint may be that the initial scattering matrix belongs to the preset convex set. The third optimization subtask may be an optimization task obtained by relaxing the first optimization subtask.

[0165] In practical applications, the objective function and constraints of the aforementioned first optimization task are both non-convex, making them difficult to solve directly. To obtain a feasible solution for the scattering matrix in the first optimization task, the aforementioned pre-defined non-convex set is first... Relaxing to a convex set is defined as follows: ,gather It is a set Therefore, the relaxed optimization task (the third optimization subtask) can be:

[0166]

[0167] in, , This indicates the gain of the aforementioned communication channel.

[0168] Since the objective function of this third optimization subtask is still non-convex, it will be further transformed next.

[0169] First, regarding the perceived channel gain By utilizing The equivalent form and the properties of the trace can be used to... The equivalent transformation is as follows:

[0170]

[0171] in, , .

[0172] Similarly, for the communication channel gain term in the third optimization subtask It can also be converted into an equivalent form:

[0173]

[0174] in, , .

[0175] Furthermore, by applying identities in, It represents the Kronecker product. and The expression can be transformed into:

[0176]

[0177]

[0178] Therefore, regarding variables The third optimization subtask can be transformed into:

[0179]

[0180] Among them, the first target matrix Second target matrix .

[0181] Then, applying the properties of the trace, the equivalent optimization task (i.e., the first optimization subtask) can be obtained as follows:

[0182]

[0183] in, .

[0184] S507: Summate the first target matrix and the second target matrix to obtain the target self-conjugate matrix.

[0185] In one specific embodiment, the target self-conjugate matrix may include a first target unitary matrix, which can be a unitary matrix obtained by eigenvalue decomposition of the target self-conjugate matrix. Specifically, the target self-conjugate matrix... . Since it is a Hermitian matrix, its eigenvalues ​​can be decomposed to obtain... ,in, and All are unitary matrices (i.e., the first objective unitary matrix mentioned above).

[0186] Optionally, after summing the first target matrix and the second target matrix to obtain the target self-conjugate matrix, the above method may further include:

[0187] The first optimization subtask is transformed based on the target self-conjugate matrix to obtain the second optimization subtask.

[0188] In one specific embodiment, the second optimization subtask may include a fourth objective function and a first scattering constraint. The fourth objective function may be obtained by transforming the third objective function based on the target self-conjugate matrix.

[0189] In practical applications, after obtaining the first optimized subtask mentioned above, due to Under this condition, the lower bound can be used as a substitute function for the third objective function (the fourth objective function). Combining this with the first scattering constraint condition of the first optimization subtask, we obtain the second optimization subtask, which can then be expressed as:

[0190]

[0191] in, Since it is a Hermitian matrix, we can perform eigenvalue decomposition to obtain... , and All are diagonal matrices.

[0192] S509: Based on the first target unitary matrix, solve the first optimization subtask to obtain the target relaxed scattering vector.

[0193] Optionally, the above-mentioned solution to the first optimization subtask based on the first target unitary matrix to obtain the target relaxed scattering vector may include:

[0194] Based on the unitary matrix of the first objective, the second optimization subtask is solved to obtain the relaxed scattering matrix of the second objective;

[0195] The target relaxation scattering vector is determined based on the second target relaxation scattering matrix.

[0196] Specifically, the second target relaxation scattering matrix can be the optimization result of the second optimization subtask, i.e., the optimal solution of the second optimization subtask. The target relaxation scattering vector can be the optimization result of the first optimization subtask, i.e., the optimal solution of the first optimization subtask.

[0197] In practical applications, the definition , as well as ,in, This represents a diagonal matrix with the elements within parentheses forming its diagonal elements, where... and Arranged in descending order, we have:

[0198]

[0199] If and only if For all , ,and When, the above equation holds true.

[0200] because, and If all are non-negative diagonal matrices, then we can obtain:

[0201]

[0202] in, Representation matrix The i-th column. Because... , , Therefore, the above formula is valid.

[0203] Furthermore, we can obtain:

[0204]

[0205] If and only if When, inequalities It can achieve the equality sign, and because Therefore when At times, inequalities It can achieve the equal sign.

[0206] inequality There are two cases where this holds true. The first case is a diagonal matrix. All eigenvalues ​​are equal, that is , However, due to the matrix It is reconstructed from the system channel, and the elements in the matrix are random. Therefore, the case where all eigenvalues ​​are the same does not hold. The second case is in the diagonal matrix. middle, It corresponds to the largest eigenvalue The non-zero eigenvalues ​​are all 0, and all other eigenvalues ​​are 0. , Therefore, if and only if , When, inequalities The equal sign is obtained.

[0207] Due to constraints Therefore, if and only if When, inequalities The equal sign is obtained.

[0208] Therefore, the optimal solution to the second optimization subtask (i.e., the second target relaxation scattering matrix) can be: ,in, It corresponds to the largest eigenvalue. eigenvectors, This represents the optimal solution. Furthermore, the optimal solution (i.e., the target relaxed scattering vector) for the first optimization subtask described above can be: .

[0209] S511: Reconstruct the target relaxation scattering vector to obtain the first target relaxation scattering matrix.

[0210] Specifically, the first target relaxation scattering matrix can be the optimization result of the third optimization subtask mentioned above, that is, the optimal solution of the third optimization subtask.

[0211] In practical applications, the target scattering vector is relaxed. Reconstructed into a square matrix This is the optimal solution for the third optimization subtask.

[0212] S513: Project the relaxed scattering matrix of the first target onto a preset non-convex set and perform eigenvalue decomposition to obtain the decomposed scattering matrix.

[0213] Specifically, the decomposed scattering matrix includes the second target unitary matrix, which can be the unitary matrix obtained by eigenvalue decomposition of the matrix projected onto a preset non-convex set from the first target relaxed scattering matrix.

[0214] In practical applications, due to the optimal solution of the third optimization subtask mentioned above... The scattering constraints in the first optimization task may not be satisfied. Therefore, the symmetric unitary projection method is used to further calculate the optimal solution of the first optimization task.

[0215] First, the optimal solution of the third optimization subtask Projected onto the aforementioned preset non-convex set Above, let:

[0216]

[0217] make Let be the rank of the matrix, for By performing eigenvalue decomposition, the above-described decomposed scattering matrix can be obtained:

[0218]

[0219] in, and All are unitary matrices, namely the unitary matrix of the second objective mentioned above.

[0220] S515: Determine the target scattering matrix based on the second target unitary matrix.

[0221] Specifically, the target scattering matrix can be the relaxed scattering matrix of the first target within a predefined non-convex set. The projection of the unicorn on the surface.

[0222] In practical applications, the target scattering matrix in, .

[0223] S209: Transform the target optimization task based on the target scattering matrix to obtain the second optimization task.

[0224] In one specific embodiment, the second optimization task can optimize the initial precoding matrix and the initial communication rate allocation information with the goal of maximizing the perceived signal-to-noise ratio of the echo signal, so as to satisfy the communication rate constraint and the communication power constraint.

[0225] In one specific embodiment, after obtaining the optimal solution of the first optimization task (the target scattering matrix) through the aforementioned method, the target optimization task can be transformed and simplified under the target scattering matrix to obtain the second optimization task. The initial precoding matrix and the initial communication rate allocation information are jointly optimized to maximize the signal-to-noise ratio of the echo signal.

[0226] Specifically, the second optimization task can be represented as:

[0227]

[0228] S211: Solve the second optimization task to obtain the target precoding matrix and target communication rate allocation information.

[0229] Specifically, the target precoding matrix and target communication rate allocation information can be the optimization result of the second optimization task, that is, the optimal solution of the second optimization task.

[0230] In an optional embodiment, such as Figure 6 As shown, the above-described solution process for the second optimization task, yielding the target precoding matrix and target communication rate allocation information, may include:

[0231] S601: Obtain the transmitted signal from the signal transmitter, and the initial common equalization vector and initial private equalization vector corresponding to the signal receiver.

[0232] Specifically, the transmitted signal carries both a target public message and a target private message. The initial public equalization vector is used to estimate the public message received by the signal receiver, and the initial private equalization vector is used to estimate the private message received by the signal receiver.

[0233] S603: Estimate the common message at the signal receiver based on the initial common equalization vector to obtain the estimated common message.

[0234] Specifically, the received signal vector at the signal receiver is obtained, and the initial common equalization vector is multiplied with the received signal vector to obtain the estimated common message.

[0235] S605: Estimate the proprietary message at the signal receiver based on the initial proprietary equalization vector to obtain the estimated proprietary message.

[0236] Specifically, the initial proprietary equalization vector is multiplied by the received signal vector after removing the common message vector to obtain the estimated proprietary message.

[0237] S607: Calculate the mean square error of the public message based on the estimated public message and the target public message, and calculate the mean square error of the proprietary message based on the estimated proprietary message and the target proprietary message.

[0238] Specifically, the mean squared error of public information can be the mean squared error between the estimated public information and the target public information (i.e., the real public information), and the mean squared error of proprietary information can be the mean squared error between the estimated proprietary information and the target proprietary information (i.e., the real proprietary information).

[0239] S609: Based on the communication channel matrix, the public precoding vector, and the private precoding vector, calculate the first transmission rate of the target public message and the second transmission rate of the target private message corresponding to the signal receiver.

[0240] Specifically, the detailed processing steps for calculating the first transmission rate of the target public message and the second transmission rate of the target private message corresponding to the signal receiver based on the communication channel matrix, public precoding vector, and private precoding vector can be found in the aforementioned detailed process of the first and second transmission rates, and will not be repeated here.

[0241] S611: Based on the correspondence between the first transmission rate, the second transmission rate, the mean square error of the public message and its corresponding first initial weight information, and the mean square error of the private message and its corresponding second initial weight information, the second optimization task is transformed to obtain the fourth optimization subtask.

[0242] In one specific embodiment, the fourth optimization subtask can aim to maximize the perceived signal-to-noise ratio of the echo signal by alternately optimizing the first initial parameter combination and the second initial parameter combination to satisfy the first update communication rate constraint and the communication power constraint. Specifically, the first initial parameter combination may include an initial precoding matrix and initial communication rate allocation information, and the second initial parameter combination may include an initial common equalization vector, an initial private equalization vector, first initial weight information, and second initial weight information.

[0243] In one specific embodiment, the fourth optimization subtask may include a first objective function, a first updated communication rate constraint, and the aforementioned communication power constraint. Specifically, the first updated communication rate constraint can be obtained by transforming the aforementioned communication rate constraint based on a correspondence.

[0244] Specifically, based on the relationship between the first transmission rate, the mean square error of the public message, and its corresponding first initial weight information, the first communication rate constraint in the communication rate constraint is transformed, that is, the mean square error of the public message and its corresponding first initial weight information replace the first transmission rate in the first communication rate constraint. Based on the relationship between the second transmission rate, the mean square error of the private message, and its corresponding second initial weight information, the second communication rate constraint in the communication rate constraint is transformed, that is, the mean square error of the private message and its corresponding second initial weight information replace the second transmission rate in the second communication rate constraint. The first updated communication rate constraint includes the above two transformed constraints and the above third communication rate constraint.

[0245] S613: Solve the fourth optimization subtask to obtain the target precoding matrix and target communication rate allocation information.

[0246] Specifically, the first initial parameter combination and the second initial parameter combination are optimized separately, and the optimal solution is obtained by alternately optimizing the two parts.

[0247] Optionally, solving the fourth optimization subtask to obtain the target precoding matrix and target communication rate allocation information may include:

[0248] Given the initial precoding matrix and initial communication rate allocation information, the second initial parameter combination is optimized to obtain the second target parameter combination;

[0249] Under the second combination of objective parameters, the first updated communication rate constraint and the communication power constraint are transformed to obtain the second updated communication rate constraint and the first communication power constraint, and the first objective function is reconstructed and approximated to obtain the approximate objective function.

[0250] Based on the approximate objective function, the second update communication rate constraint, and the first communication power constraint, the fifth optimization subtask is obtained;

[0251] Solving the fifth optimization subtask yields the target precoding matrix and target communication rate allocation information.

[0252] In one specific embodiment, the above-mentioned second target parameter combination may include a target common equilibrium vector, a target private equilibrium vector, first target weight information and second target weight information. The target common equilibrium vector, target private equilibrium vector, first target weight information and second target weight information may be the optimal common equilibrium vector, private equilibrium vector, first weight information and second weight information in the fourth optimization subtask.

[0253] In this embodiment, during the solution of the fourth optimization subtask, the precoding matrix and communication rate allocation information are first fixed. The initial common equilibrium vector, initial private equilibrium vector, first initial weight information, and second initial weight information are then jointly optimized to obtain the corresponding optimal solution. Next, based on the common message mean square error and private message mean square error, the two constraints in the first updated communication rate constraint condition (excluding the third communication rate constraint condition) are equivalently transformed to obtain the two transformed constraints. The second updated communication rate constraint condition includes the two transformed constraints and the third communication rate constraint condition. Furthermore, the communication power constraint condition is equivalently transformed to obtain the first communication power constraint condition. The first objective function is reconstructed using a matrix vectorization method, and the reconstructed objective function is approximated to obtain the aforementioned approximate objective function, thus yielding the fifth optimization subtask. Specifically, the fifth optimization subtask is a convex optimization task, which can be solved using the interior-point method to obtain the target precoding matrix and target communication rate allocation information.

[0254] In practical applications, to solve the second optimization task mentioned above, the weighted minimum mean square error framework can be applied to reconstruct the second optimization task into an equivalent optimization task, namely the fifth optimization subtask, and then the solution can be obtained. The optimal solution of the precoding matrix and communication rate allocation information is determined as the optimal solution of the second optimization task.

[0255] First, an equalizer is introduced at the k-th signal receiver. This is used to estimate public messages; after removing the public portion of the message, another equalizer is introduced. This is used to estimate the proprietary message for each signal receiver. The estimation of the public message and the estimation of the proprietary message are represented as follows:

[0256]

[0257] The result of the above formula represents the estimation of public information, and the result of the below formula represents the estimation of private information. This indicates the received signal at the signal receiving end. It can be determined using the following formula:

[0258]

[0259] in, The additive white Gaussian noise received at the k-th signal receiver has a mean of zero and a variance of . A complex Gaussian random process.

[0260] Next, calculate the mean square error of the common message and the mean square error of the proprietary message corresponding to the k-th signal receiver:

[0261]

[0262] in, This indicates the mean squared error of the public message. This indicates the mean squared error of the proprietary message. Represents the real part of a complex number.

[0263] Here, I record

[0264] ,

[0265] Solve the following equations and The minimum mean square error equalizers can be obtained as follows:

[0266]

[0267] but and The corresponding minimum mean square error can be expressed as:

[0268]

[0269] Referencing two weights and By applying the weighted minimum mean square error framework, the transmission rate of the common message at the k-th signal receiver can be obtained. The transmission rate of proprietary information The mean squared error of public messages, the mean squared error of private messages, and their corresponding weights. , The relationship between them is as follows:

[0270]

[0271] Equation (c) holds if and only if the following conditions are met:

[0272]

[0273] Inequality (d) holds if and only if For all Established.

[0274] Furthermore, based on the relaxation and transformation forms described above, the second optimization task can be transformed and reformulated as the following optimization problem (i.e., the fourth optimization subtask):

[0275]

[0276] in, and For all Established.

[0277] Then, the fourth optimization subtask mentioned above is solved, and optimizations are performed respectively. and These two parts are used to obtain the optimal solution by alternately optimizing these two parts.

[0278] when When fixed, it can be observed that This part of the optimization variables only appears in the formula. and Within the constraints. Therefore, optimization can be achieved. and To maximize Thus, the optimal result is obtained. and 。 Similarly, by maximizing This can also yield the optimal result. and The corresponding solution is given in the following formula:

[0279] ,

[0280] when When fixed, the fourth optimization subtask can be simplified to:

[0281]

[0282] Substituting the above mean squared error of public messages and mean squared error of private messages into... In the equation, we have the following:

[0283]

[0284] in, , ,as well as .

[0285] Similarly, the following equation holds:

[0286]

[0287] in, , ,and .

[0288] Furthermore, by applying matrix vectorization, the objective function of the fourth optimization subtask is reconstructed into the following form:

[0289]

[0290] in, , ,and Due to the non-convex nature of the objective function, a first-order Taylor expansion is used to approximate it. Through this approximation, the objective function can be transformed into:

[0291]

[0292] in, ,and Indicates the first The precoding matrix in the next iteration (transmit beamforming).

[0293] Therefore, the fourth optimization subtask can be transformed into the following fifth optimization subtask:

[0294]

[0295] This optimization task is a convex optimization problem, which can be solved efficiently using the interior point method.

[0296] In the optimization process described above, the two sets of parameters can be repeatedly and alternately optimized until the objective function is achieved. The solution converges, yielding the final optimal solution.

[0297] S213: Communication and sensing are based on the target scattering matrix, target precoding matrix, and target communication rate allocation information.

[0298] As can be seen from the technical solutions provided in the embodiments of this specification above, the intelligent reflective surface of this invention is applied in a wireless communication and sensing system. The system also includes a signal transmitter, a signal receiver, and a target to be sensed. The intelligent reflective surface has a super-diagonal structure. First, the initial precoding matrix corresponding to the signal transmitter, the initial communication rate allocation information corresponding to the signal receiver, the initial scattering matrix corresponding to the intelligent reflective surface, and the echo signal of the target to be sensed received by the signal transmitter are obtained. The echo signal is reflected back to the signal transmitter by the intelligent reflective surface. Then, based on the communication channel matrix corresponding to the signal receiver, the sensing channel matrix corresponding to the target to be sensed, the initial scattering matrix, the initial precoding matrix, and the initial communication rate allocation information, the target optimization task is determined. The target optimization task aims to maximize the sensing signal-to-noise ratio of the echo signal. The initial precoding matrix and initial communication rate allocation information are optimized to satisfy scattering constraints, communication rate constraints, and communication power constraints. Further, based on the communication channel matrix, sensing channel matrix, and initial scattering matrix, a first optimization task is determined, aiming to maximize channel gain. This first optimization task optimizes the initial scattering matrix to satisfy scattering constraints. The first optimization task is solved using a symmetric unitary projection algorithm to obtain the target scattering matrix. Then, the target optimization task is transformed based on the target scattering matrix to obtain a second optimization task, which is then solved to obtain the target precoding matrix and target communication rate allocation information. Finally, communication and sensing are performed based on the target scattering matrix, target precoding matrix, and target communication rate allocation information.

[0299] Therefore, in the process of jointly optimizing the precoding matrix, public message rate allocation, and scattering matrix to solve the problem of maximizing the sensing signal-to-noise ratio, the problem is decomposed into two-stage optimization problems. First, the scattering matrix is ​​optimized based on the symmetric unitary projection algorithm. Then, the precoding matrix and public message rate allocation are jointly optimized. By continuously and alternately optimizing, the optimal solution for the precoding matrix, public message rate allocation, and scattering matrix in the original optimization problem is obtained. Communication and sensing are then performed based on the optimal solution, which can reduce interference in the communication and sensing process, improve resource utilization, and thus improve the system's communication and sensing performance, achieving a balance between communication and sensing performance.

[0300] To verify the effectiveness of the wireless communication and sensing method proposed in this application in the BD-RIS-assisted common message rate allocation (ISAC) system, simulation experiments were conducted. The specific simulation parameters are shown in Table 1 below:

[0301] Table 1 Simulation Parameter Table

[0302]

[0303] Specifically, users are randomly distributed within a semicircle with a radius of 2.5m centered on the BD-RIS. Assuming the target to be sensed is a point target, the channel between the BD-RIS and the target uses a Rayleigh channel, while the sensing channel uses a line-of-sight (LoS) channel, i.e.:

[0304]

[0305] in, This represents the path loss parameter.

[0306] In addition, the line-of-sight (LoS) component of the channel 、 and It is composed of the outer product of the beam pointing vectors at both the transmitting and receiving ends. and It is the non-line of sight component, which follows a Gaussian distribution with a mean of zero and a variance of 1.

[0307] To evaluate the performance of the method proposed in this invention, the scheme of this application was compared with the following two schemes through simulation, as follows:

[0308] This application: BD-RIS assisted RSMA-based ISAC system.

[0309] Option 1: BD-RIS assisted pure perception system: This system only considers the perception tasks being performed by BD-RIS.

[0310] Option 2: BD-RIS-assisted ISAC system based on Space Division Multiple Access (SDMA): In this system, the base station does not perform public message rate allocation, and messages from all other users are considered interference.

[0311] In the embodiments described in this specification, in order to evaluate the impact of different factors in the transmitter on system performance, such as... Figure 7 As shown, increasing the transmit power from 10mW to 20mW correspondingly improves the sensing signal-to-noise ratio. This is reasonable, as the increased transmit signal power allows for more power to be allocated to sensing tasks while still meeting user communication needs, thus directly improving sensing performance. Furthermore, the BD-RIS-assisted RSMA-ISAC system maintained good performance throughout this process, consistent with previous results.

[0312] in addition, Figure 8 The impact of the number of BD-RIS reflector units on the perceived signal-to-noise ratio (SNR) is demonstrated. It is evident that in all scenarios, the perceived SNR increases with the increase in the number of BD-RIS units. This is because more BD-RIS units mean more flexible control in the beam domain, enabling more precise and narrower beam pointing towards the user and target, thus achieving higher beam gain and enhancing the strength of the target echo signal. On the other hand, under the same number of users, the perceived SNR of the RSMA-ISAC system is significantly better than that of the SDMA-assisted system. Furthermore, in the RSMA system, even with an increase in the number of users, its system performance does not significantly decrease, exhibiting good interference management capabilities and effectively reducing interference between users. In contrast, the SDMA system lacks sufficient interference management mechanisms, and the perceived SNR decreases significantly with an increase in the number of users.

[0313] Figure 9 This demonstrates the relationship between the user communication rate threshold and the perceived signal-to-noise ratio. For example... Figure 9As shown, in a pure sensing system, the sensing signal-to-noise ratio (SNR) remains essentially constant. This is because the system only performs target sensing tasks and does not involve communication requirements; therefore, changes in the user communication rate threshold do not affect its sensing performance. However, in RSMA-ISAC systems and SDMA-assisted systems, the sensing SNR gradually decreases as the user communication rate threshold increases. This is because higher communication rate requirements mean that more power needs to be allocated to communication tasks, resulting in less power available for sensing and a decline in sensing performance. These results provide a reference for the trade-off between sensing and communication performance in ISAC systems, helping to allocate resources according to specific needs and thus achieving a balance between communication and sensing performance as much as possible. Furthermore, the sensing SNR decreases significantly when the number of users increases because more power must be allocated to more communication tasks. When the number of users increases, the performance degradation of SDMA-assisted systems is significantly greater than that of RSMA systems, further demonstrating the advantages of RSMA in interference management and resource utilization in multi-user environments.

[0314] This invention also provides a wireless communication and sensing system, such as... Figure 1 As shown, the system includes a signal transmitter, a signal receiver, a smart reflector, and a target to be sensed. The smart reflector has a super-diagonal structure and is used for signal transmission between the signal transmitter and the signal receiver, and between the smart reflector and the target to be sensed. The system performs wireless communication and sensing based on the wireless communication and sensing method based on the smart reflector in the above embodiment.

[0315] Regarding the system in the above embodiments, the specific manner in which the operations are performed has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0316] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles disclosed herein and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0317] It should be understood that the present invention is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. A wireless communication and sensing method based on a smart reflective surface, characterized in that, The intelligent reflective surface is applied in a wireless communication and sensing system, the system further including a signal transmitter, a signal receiver, and a target to be sensed, the intelligent reflective surface having a super-diagonal structure, and the method comprising: The system acquires the initial precoding matrix corresponding to the signal transmitter, the initial communication rate allocation information corresponding to the signal receiver, the initial scattering matrix corresponding to the smart reflector, and the echo signal of the target to be sensed received by the signal transmitter; the echo signal is reflected back to the signal transmitter by the smart reflector. Based on the communication channel matrix corresponding to the signal receiver, the sensing channel matrix corresponding to the target to be sensed, the initial scattering matrix, the initial precoding matrix, and the initial communication rate allocation information, a target optimization task is determined. The target optimization task aims to maximize the sensing signal-to-noise ratio of the echo signal and optimizes the initial scattering matrix, the initial precoding matrix, and the initial communication rate allocation information to satisfy scattering constraints, communication rate constraints, and communication power constraints. Based on the initial precoding matrix and the initial communication rate allocation information, the communication channel gain is calculated using the communication channel matrix and the initial scattering matrix. Based on the sensing channel matrix and the initial scattering matrix, the sensing channel gain is calculated; The maximum value of the sum of the communication channel gain and the sensing channel gain is determined as the second objective function corresponding to the first optimization task; Based on the relationship between the initial scattering matrix and the preset non-convex set, the scattering constraints corresponding to the first optimization task are determined; the first optimization task aims to maximize the channel gain and optimizes the initial scattering matrix to satisfy the scattering constraints. Based on the symmetric unitary projection algorithm, the first optimization task is solved to obtain the target scattering matrix; The target optimization task is transformed based on the target scattering matrix to obtain a second optimization task; The second optimization task is solved to obtain the target precoding matrix and target communication rate allocation information; Communication and sensing are performed based on the target scattering matrix, the target precoding matrix, and the target communication rate allocation information.

2. The method according to claim 1, characterized in that, The target optimization task includes a first objective function, the scattering constraint, the communication rate constraint, and the communication power constraint. Determining the target optimization task based on the communication channel matrix corresponding to the signal receiver, the sensing channel matrix corresponding to the target to be sensed, the initial scattering matrix, the initial precoding matrix, and the initial communication rate allocation information includes: The transmitted signal from the signal transmitter is obtained. The transmitted signal carries a target public message and a target private message. The initial precoding matrix includes a public precoding vector for encoding the target public message and a private precoding vector for encoding the target private message. Based on the communication channel matrix, the public precoding vector, the proprietary precoding vector, and the initial communication rate allocation information, the communication rate constraint conditions are determined. Based on the sensing channel matrix and the initial precoding matrix, the sensing signal-to-noise ratio of the echo signal is calculated. The maximum value of the perceived signal-to-noise ratio of the echo signal is determined as the first objective function; The transmit power is calculated based on the initial precoding matrix and its conjugate transpose. The communication power constraints are determined based on the relationship between the transmission power and the total power of the system. The scattering constraint conditions are determined based on the relationship between the initial scattering matrix and the preset non-convex set.

3. The method according to claim 2, characterized in that, The step of determining the communication rate constraint based on the communication channel matrix, the public precoding vector, the proprietary precoding vector, and the initial communication rate allocation information includes: Based on the communication channel matrix, the public precoding vector, and the proprietary precoding vector, the first transmission rate of the target public message corresponding to the signal receiving end is calculated; Based on the communication channel matrix and the proprietary precoding vector, the second transmission rate of the target proprietary message corresponding to the signal receiving end is calculated. Based on the relationship between the first transmission rate and the initial communication rate allocation information, the first communication rate constraint condition is determined. Based on the initial communication rate allocation information, the second transmission rate, and the preset lower limit transmission rate corresponding to the signal receiving end, the second communication rate constraint condition is determined. The communication rate constraint is determined based on the first communication rate constraint, the second communication rate constraint, and the third communication rate constraint corresponding to the initial communication rate allocation information.

4. The method according to claim 1, characterized in that, The communication channel matrix includes a first channel matrix from the signal transmitter to the smart reflector and a second channel matrix from the smart reflector to the signal receiver; the sensing channel matrix includes the first channel matrix and a third channel matrix from the smart reflector to the target to be sensed; and the first optimization task includes a second objective function. The target scattering matrix obtained by solving the first optimization task based on the symmetric unitary projection algorithm includes: The initial scattering matrix is ​​processed into column vectors to obtain the initial scattering vector; The transpose of the product of the first channel matrix and its conjugate transpose is multiplied by the product of the second channel matrix and its conjugate transpose, and the product of the third channel matrix and its conjugate transpose, respectively, to obtain the first target matrix and the second target matrix. The first optimization task is transformed to obtain a first optimization subtask; the first optimization subtask includes a third objective function and a first scattering constraint condition. The third objective function is obtained by transforming the second objective function based on the initial scattering vector, the first objective matrix, and the second objective matrix. The first scattering constraint condition is determined based on the relationship between the initial scattering vector and a preset convex set. The preset convex set is obtained by relaxing a preset non-convex set. The first target matrix and the second target matrix are summed to obtain the target self-conjugate matrix, which includes the first target unitary matrix. Based on the first target unitary matrix, the first optimization subtask is solved to obtain the target relaxed scattering vector; The target relaxation scattering vector is reconstructed to obtain the first target relaxation scattering matrix; The first target relaxed scattering matrix is ​​projected onto the preset non-convex set and eigenvalue decomposition is performed to obtain the decomposed scattering matrix, which includes the second target unitary matrix. The target scattering matrix is ​​determined based on the second target unitary matrix.

5. The method according to claim 4, characterized in that, After summing the first target matrix and the second target matrix to obtain the target self-conjugate matrix, the method further includes: The first optimization subtask is transformed based on the target self-conjugate matrix to obtain the second optimization subtask. The second optimization subtask includes a fourth objective function and the first scattering constraint condition. The fourth objective function is obtained by transforming the third objective function based on the target self-conjugate matrix. Accordingly, the step of solving the first optimization subtask based on the first target unitary matrix to obtain the target relaxed scattering vector includes: Based on the first target unitary matrix, the second optimization subtask is solved to obtain the second target relaxed scattering matrix; The target relaxation scattering vector is determined based on the second target relaxation scattering matrix.

6. The method according to claim 4, characterized in that, The transformation of the first optimization task to obtain the first optimization subtask includes: The first optimization task is transformed to obtain a third optimization subtask; the third optimization subtask includes the second objective function and the second scattering constraint, the second scattering constraint is determined based on the relationship between the initial scattering matrix and the preset convex set, and the first objective relaxed scattering matrix is ​​the optimization result of the third optimization subtask; Based on the first target matrix and the second target matrix, the third optimization subtask is transformed to obtain the first optimization subtask.

7. The method according to claim 1, characterized in that, The initial precoding matrix includes a public precoding vector for encoding the target public message and a proprietary precoding vector for encoding the target proprietary message. Solving the second optimization task to obtain the target precoding matrix and target communication rate allocation information includes: The transmitted signal from the signal transmitter is obtained, as well as the initial common equalization vector and the initial private equalization vector corresponding to the signal receiver; the transmitted signal carries the target common message and the target private message. The common message at the signal receiver is estimated based on the initial common equalization vector to obtain the estimated common message; The proprietary message of the signal receiver is estimated based on the initial proprietary equalization vector to obtain the estimated proprietary message. Based on the estimated public message and the target public message, the mean square error of the public message is calculated, and based on the estimated proprietary message and the target proprietary message, the mean square error of the proprietary message is calculated. Based on the communication channel matrix, the public precoding vector, and the proprietary precoding vector, the first transmission rate of the target public message and the second transmission rate of the target proprietary message corresponding to the signal receiving end are calculated. Based on the correspondence between the first transmission rate, the second transmission rate, the mean square error of the public message and its corresponding first initial weight information, and the mean square error of the private message and its corresponding second initial weight information, the second optimization task is transformed to obtain the fourth optimization subtask. Solving the fourth optimization subtask yields the target precoding matrix and the target communication rate allocation information.

8. The method according to claim 7, characterized in that, The fourth optimization subtask aims to maximize the perceived signal-to-noise ratio of the echo signal by alternately optimizing the first initial parameter combination and the second initial parameter combination to satisfy the first updated communication rate constraint and the communication power constraint. The first updated communication rate constraint is obtained by transforming the communication rate constraint based on the correspondence. The first initial parameter combination includes the initial precoding matrix and the initial communication rate allocation information. The second initial parameter combination includes the initial common equalization vector, the initial private equalization vector, the first initial weight information, and the second initial weight information. The fourth optimization subtask includes the first objective function; The step of solving the fourth optimization subtask to obtain the target precoding matrix and the target communication rate allocation information includes: Based on the initial precoding matrix and the initial communication rate allocation information, the second initial parameter combination is optimized to obtain the second target parameter combination; the second target parameter combination includes a target common equilibrium vector, a target specific equilibrium vector, first target weight information, and second target weight information; Under the second objective parameter combination, the first updated communication rate constraint and the communication power constraint are transformed to obtain the second updated communication rate constraint and the first communication power constraint, and the first objective function is reconstructed and approximated to obtain the approximate objective function. Based on the approximate objective function, the second update communication rate constraint, and the first communication power constraint, the fifth optimization subtask is obtained; Solving the fifth optimization subtask yields the target precoding matrix and the target communication rate allocation information.

9. A wireless communication and sensing system, characterized in that, The system includes a signal transmitter, a signal receiver, a smart reflective surface, and a target to be sensed. The smart reflective surface has a super-diagonal structure and is used for signal transmission between the signal transmitter and the signal receiver, and between the smart reflective surface and the target to be sensed. The system performs wireless communication and sensing based on the wireless communication and sensing method based on the smart reflective surface as described in any one of claims 1 to 8.

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