Cell-free symbiotic communication perception system optimization method based on passive metamaterial tag

Abstract

This invention provides an optimization method for a cellless symbiotic communication and sensing system based on passive metamaterial tags, relating to the field of Internet of Things (IoT) technology. The method includes: Step S1, constructing a cellless symbiotic communication and sensing system based on passive metamaterial tags; Step S2, constructing a joint beamforming multi-objective optimization problem, which simultaneously maximizes the communication and rate of the main system and the communication and rate of the secondary system. Constraints include sensing SINR under multiple sensing objectives, and optimization variables are the AP transmit beamforming matrix, the AP receive equalization vector, and the metamaterial tag reflection phase matrix; Step S3, using a constraint transformation-based Pareto optimization framework, transforming the multi-objective optimization problem into a single-objective optimization problem, and obtaining the final beamforming optimization result by iteratively solving the optimization variables described in Step S2. This invention can effectively improve the symbiotic communication and sensing performance of cellless systems.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to an optimization method for a cellless symbiotic communication and sensing system based on passive metamaterial tags. Background Technology

[0002] As the Internet of Things (IoT) evolves towards large-scale, high-density, and multifunctional applications, traditional cellular networks face significant bottlenecks in addressing the combined demands of massive terminal access, high spectral efficiency, and high-precision sensing. Cell-free multiple-input multiple-output (CF-MIMO) systems, through distributed deployment of access points (APs) and coordinated transmission, can effectively eliminate cell boundaries, suppress interference, provide uniform coverage and stable service for the IoT, and effectively improve system communication and sensing performance. However, traditional IoT communication systems are often limited by terminal power supply and hardware costs, making large-scale deployment difficult. To overcome these bottlenecks, Symbiotic Radio (SR) technology has emerged. Its core lies in utilizing a backscatter communication mechanism, enabling passive tags to achieve secondary system data transmission by modulating and reflecting radio frequency signals from APs. This eliminates the need for independent spectrum and high-power radio frequency components, thereby significantly reducing terminal power consumption and hardware costs while improving spectrum utilization. However, traditional backscatter tags are affected by cascaded link fading effects, and their communication performance is limited. Therefore, symbiotic radio technology based on metamaterial tags has emerged. By introducing passive metamaterial arrays into the symbiotic radio system, it can simultaneously assume the dual roles of environmental reconstruction and passive tagging. While assisting the main system in communication and sensing, it can also realize its own secondary system data transmission, thus forming a symbiotic communication and sensing architecture based on passive metamaterial tags.

[0003] Against this backdrop, a symbiotic radio system based on passive metamaterial tags is combined with a cellless system. This allows the access point (AP) to simultaneously transmit data and sense and locate the tags by receiving echo signals from them, thus constructing an integrated symbiotic communication and sensing system. This system fully leverages the macro diversity and near-user deployment advantages of cellless systems, as well as the low cost and low power consumption of passive metamaterial arrays, achieving a synergistic improvement in primary communication, secondary communication, and sensing performance within the system.

[0004] However, existing research has not addressed the collaborative design and optimization of cell-free symbiotic communication and sensing based on passive metamaterial tags, especially the multi-objective optimization requirements for symbiotic communication and sensing performance, and related joint beamforming design schemes are lacking. Summary of the Invention

[0005] The main objective of this invention is to propose an optimization method for cellless symbiotic communication and sensing systems based on passive metamaterial tags, which can effectively improve the symbiotic communication and sensing performance of cellless systems.

[0006] This invention is achieved through the following technical solution:

[0007] An optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags includes the following steps:

[0008] Step S1: Construct a cellless symbiotic communication and sensing system based on passive metamaterial tags. The system includes multiple full-duplex APs, multiple passive metamaterial tags, multiple main system receiving terminals and one secondary system receiving terminal. The system simultaneously has active data communication from APs to main system receiving terminals, passive data communication from passive metamaterial tags to secondary system receiving terminals, and multi-target sensing and detection of passive metamaterial tags by APs.

[0009] Step S2: Construct a joint beamforming multi-objective optimization problem. This multi-objective optimization problem simultaneously maximizes the communication and rate of the main system and the communication and rate of the subsystem. The constraints include the sensing SINR under multiple sensing targets. The optimization variables are the AP transmit beamforming matrix, the AP receive equalization vector, and the metamaterial tag reflection phase matrix.

[0010] Step S3: Using a Pareto optimization framework based on constraint transformation, the multi-objective optimization problem is transformed into a single-objective optimization problem, and the optimization variables described in step S2 are solved by alternating iterations to obtain the final beamforming optimization result.

[0011] Furthermore, in step S1, for the active data communication, M APs, assisted by L passive metamaterial tags, collaboratively send active communication signals to K main system receivers. For the passive data communication, each passive metamaterial tag passively sends its environmental perception information to the secondary system receiver terminal through backscattering technology. For multi-target perception and detection, the M APs use the received echo signals to collaboratively perceive the L passive metamaterial tags.

[0012] Furthermore, in step S2, the multi-objective optimization problem is expressed as: ,in, Let m be the transmit beamforming matrix of the m-th AP. To sense the AP receiving equalization vector of the l-th metamaterial tag. Let l be the reflection phase matrix of the l-th metamaterial tag. Main system communication and speed, For secondary system communication and speed, For the sensing SINR of the l-th passive metamaterial tag, To detect the minimum SINR threshold, Maximum transmit power for each AP In order to seek The absolute value, For the l-th metamaterial tag, the first The reflection phase of each reflecting unit The number of reflective units equipped for each passive metamaterial tag. For diagonal matrix operators, , , .

[0013] Furthermore, in step S2, the main system communication and rate are expressed as follows: ,in, This is the nth communication data symbol transmitted by the l-th metamaterial tag using BPSK modulation. , This indicates the calculation of mathematical expectation. The received signal-to-dryness ratio of the k-th primary system receiving terminal is represented by: The communication and rate of the secondary system are expressed as: Q represents the period of the secondary communication data symbol being Q times the period of the primary communication data symbol. The number of antennas equipped for each AP, Let V be the variance of the Gaussian channel noise contained in the received signal of the subsystem terminal within the period of the q-th main communication symbol in the n-th secondary communication data symbol. Let l be the reflection amplitude coefficient of the l-th metamaterial tag. For the l-th metamaterial tag to the secondary system receiving terminal channel, For each AP to the l-th metamaterial tag, The total transmit beamforming matrix for M APs, For size The identity matrix.

[0014] Furthermore, in step S2, the sensing SINR of the l-th passive metamaterial tag is represented as: ,in, This represents the channel from the l'th passive metamaterial tag to the M APs.

[0015] Furthermore, in step S3, based on the Pareto optimization framework of constraint transformation, the single-objective optimization problem is expressed as follows: By adjusting different thresholds for subsystem communication and rate It solves single-objective optimization problems under different thresholds to obtain a set of optimal solutions for multi-objective problems, and introduces auxiliary variables to... , The objective function of the single-objective optimization problem is transformed and decoupled, becoming a convex function form that is easier to handle. Then, the positive semidefinite relaxation and alternating iterative optimization methods are used to alternately optimize the transmit beamforming matrix. AP receive equalization vector Metamaterial tag reflection phase matrix and auxiliary variables , The process continues until the convergence condition is met, at which point the final beamforming optimization result is output, where... It represents the set of complex numbers.

[0016] Furthermore, in step S3, for the transmitted beamforming matrix... Fixed AP receive equalization vector Metamaterial tag reflection phase matrix and auxiliary variables , And introduce auxiliary variables Then the single-objective optimization problem is transformed into a problem concerning auxiliary variables. The sub-optimization problem is solved by using a positive semidefinite relaxation technique to obtain auxiliary variables. The solution, if the obtained auxiliary variable satisfy Then use eigenvalue decomposition to equivalently restore Otherwise, Gaussian randomization is used to obtain... The approximate optimal solution, where, , .

[0017] Furthermore, in step S3, for the metamaterial tag reflection phase matrix Fixed AP transmit beamforming matrix AP receive equalization vector and auxiliary variables , And introduce auxiliary variables Then the single-objective optimization problem is transformed into a problem about The optimization subproblem is solved by employing semidefinite relaxation and Gaussian randomization techniques to obtain an approximate optimal solution for the metamaterial tag reflection phase matrix. , For matrix A vector of diagonal elements.

[0018] Furthermore, in step S3, for the AP receive equalization vector Fixed alternating optimization of AP transmit beamforming matrix Metamaterial tag reflection phase matrix and auxiliary variables , L can be established respectively regarding Independent subproblems This problem takes the form of a generalized Rayleigh quotient, and the AP receive equalization vector can be obtained by performing eigenvalue decomposition on it. The optimal solution, where , .

[0019] Furthermore, in step S3, for auxiliary variables... , Fixed AP transmit beamforming matrix AP receive equalization vector and metamaterial tag reflection phase matrix Afterwards, information about , The convex optimization problem can be derived based on the weighted least mean square error technique and first-order differentiation. , The optimal closed-form solution.

[0020] As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following beneficial effects:

[0021] This invention constructs a joint beamforming multi-objective optimization problem for a cellless symbiotic communication and sensing system based on passive metamaterial tags. This multi-objective optimization problem simultaneously maximizes the communication and rate of the main system and the communication and rate of the subsystem. Constraints include sensing SINR under multiple sensing objectives. Optimization variables are the AP transmit beamforming matrix, the AP receive equalization vector, and the metamaterial tag reflection phase matrix. A Pareto optimization framework based on constraint transformation is used to transform this multi-objective optimization problem into a single-objective optimization problem. The final beamforming optimization result is obtained by iteratively solving each optimization variable. This achieves the collaborative design and optimization of cellless symbiotic communication and sensing based on passive metamaterial tags. The obtained joint beamforming optimization result meets the multi-objective optimization requirements of symbiotic communication and sensing performance, thereby effectively improving the symbiotic communication and sensing performance of the cellless system. Attached Figure Description

[0022] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Figure 1 This is a flowchart of the present invention.

[0024] Figure 2 This is a detailed flowchart of the present invention.

[0025] Figure 3 This is a schematic diagram of the system structure of the present invention.

[0026] Figure 4 This is a schematic diagram of the transmitting signal structure of the present invention.

[0027] Figure 5 This is a comparison chart showing the performance of the main system communication and rate of the present invention and the comparative scheme as the minimum threshold of the sensing SINR changes.

[0028] Figure 6 This is a comparison chart showing the performance of the main system communication and rate of the present invention and the comparative scheme as the communication and rate threshold of the secondary system changes. Detailed Implementation

[0029] The present invention will be further described below through specific embodiments.

[0030] like Figure 1 and Figure 2 As shown, the optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags includes the following steps:

[0031] Step S1: Construct a cellless symbiotic communication and sensing system based on passive metamaterial tags. The system includes multiple full-duplex APs, multiple passive metamaterial tags, multiple main system receiving terminals and one secondary system receiving terminal. The system has three functions: active data communication from APs to main system receiving terminals, passive data communication from passive metamaterial tags to secondary system receiving terminals, and multi-target sensing and detection of passive metamaterial tags by APs.

[0032] like Figure 3 As shown, the system specifically includes one CPU and M full-duplex APs (i.e., Figure 2 The system consists of N access points (APs), L passive metamaterial tags, K main system receivers, and one secondary system receiver. All APs and metamaterial tags are connected to the CPU via backhaul and control links, respectively. Each AP is equipped with N... t One antenna per receiving terminal, and N antennas per passive metamaterial tag. R There are 1 reflective unit. The system simultaneously includes active data communication from the AP to the main system receiving terminal (i.e., main system communication), passive data communication from the passive metamaterial tag to the secondary system receiving terminal (i.e., secondary system communication), and multi-target sensing and detection of the passive metamaterial tag by the AP. Regarding active data communication, M APs, assisted by L passive metamaterial tags, collaboratively send active communication signals (such as...) to K main system receiving terminals. Figure 2 (As shown by the red arrow in the middle); In terms of passive data communication, each passive metamaterial tag passively transmits its environmental perception information to the secondary system receiving terminal (e.g., ...) via backscattering technology. Figure 2 (As shown by the blue arrow in the middle); In terms of multi-target perception, M APs use the received echo signals to collaboratively sense L passive metamaterial tags (e.g., Figure 2(As indicated by the yellow arrow in the middle).

[0033] Assumption This represents the direct connection channels from M APs to the k-th master system receiving terminal. This represents the channel from the l-th metamaterial tag to the k-th master system receiving terminal. This represents the channels from M APs to the l-th metamaterial tag. This represents the direct connection channels from M APs to the receiving terminals of the subsystem. This represents the channel from the l-th metamaterial tag to the secondary system receiving terminal. The system employs a co-occurrence transmission scheme; therefore, the transmission signal structure between the AP and the metamaterial tag is as follows: Figure 4 As shown in the diagram. The period of the secondary communication data symbol is Q times the period of the primary communication data symbol, and the channel remains unchanged within one secondary communication data symbol period. Simultaneously, M APs send the same data symbols to the same primary system receiving terminal to achieve coordinated transmission. Therefore, the data signal sent by the m-th AP within the q-th primary communication data symbol period of the n-th secondary communication data symbol can be expressed as: ,in, Let m be the beamforming matrix for the m-th access point. Let q be the vector of the q-th main data symbol sent by the access point during the nth communication data symbol period, which follows a complex Gaussian distribution.

[0034] In terms of main system data communication, M APs transmit main communication data to each main system receiving terminal through direct AP-main system receiving terminal links and cascaded links assisted by metamaterial tags. Based on this, the main system signal transmission model is constructed as follows: ,in, This represents the received signal of the k-th master system receiving terminal within the period of the q-th main communication data symbol in the n-th secondary communication data symbol. This indicates that the nth communication data symbol transmitted by the l-th metamaterial tag using binary phase shift keying (BPSK) modulation is... The total beamforming matrix for M APs, This represents the reflection amplitude coefficient of the l-th metamaterial tag. Let l be the reflection phase matrix of the l-th metamaterial tag. The noise level is Gaussian. Since the symbol period of the secondary communication data is much longer than that of the primary communication data, the passive metamaterial tag-assisted cascaded channel can be considered as an additional multipath component to assist the primary data communication. Therefore, the received signal-to-interference-plus-noise ratio (SIR) of the k-th primary system receiving terminal can be derived as follows: ,in, Beamforming matrix The k-th column vector is used to deduce the main system communication speed. ,in This represents a vector consisting of L communication data symbols. This represents the mathematical expectation. Since each communication data symbol... There are two possible values, 0 and 1. Therefore, the communication data symbol vector Total Let possible combinations of values ​​be . express The r-th possible realization.

[0035] On the other hand, for secondary system data communication, the metamaterial tag assists the AP main system in receiving and transmitting the main communication data from the terminal equipment, while simultaneously performing BPSK modulation on the main communication data signal to generate secondary communication data symbols. The signal is then transmitted to the secondary system receiving terminal. Based on this, the following secondary system signal transmission model is constructed: , This represents the received signal of the secondary system receiving terminal within the period of the q-th main communication data symbol in the n-th secondary communication data symbol. The noise is Gaussian channel noise. To improve the communication performance of the secondary system, a method based on Successive Interference Cancellation (SIC) is considered to sequentially decode all primary data symbols and secondary communication data symbols in the received signal. Specifically, the receiving terminal equipment first uses SIC technology to sequentially decode all primary data symbols within one secondary communication data symbol period. Decoding is performed. After decoding, the receiving terminal device combines all decoded main data symbols based on the maximum ratio combining method, and then uses SIC technology to sequentially decode all secondary communication data symbols. Assuming perfect decoding can be achieved under ideal SIC conditions, the communication and rate of the subsystem can be derived as follows: ,in, For size The identity matrix.

[0036] In addition to primary system data communication and secondary system data communication, the M full-duplex APs will also utilize the received echo signals to perform collaborative tag detection and sensing on the L passive metamaterial tags. Assuming that all APs have perfect channel state information and can utilize self-interference cancellation techniques to avoid inter-AP interference and self-interference, the following AP echo signal receiving model is constructed: ,in, This represents the received echo signal of the m-th AP within the period of the q-th main data symbol in the n-th communication data symbol. This represents Gaussian channel noise. In a cellless communication system, the received echo signals from all access points (APs) are transmitted to the CPU for merging and multiple sets of receive equalization vectors are used. The merged signal is processed to improve the system's sensing performance. The equalized signal used to sense the l-th metamaterial tag can be represented as: ,in Indicates the merged signal. This represents the noise after merging. This invention uses perceived SINR to measure sensing performance. Based on the constructed received echo signal model, the perceived SINR for the l-th passive metamaterial tag can be expressed as: ,in, This represents the channel from the l'-th passive metamaterial tag to the M APs. denoted as the reflection amplitude coefficient of the l'-th passive metamaterial tag.

[0037] Step S2: Construct a joint beamforming multi-objective optimization problem. This multi-objective optimization problem simultaneously maximizes the communication and rate of the main system and the communication and rate of the subsystem. The constraints include the sensing SINR under multiple sensing targets. The optimization variables are the AP transmit beamforming matrix, the AP receive equalization vector, and the metamaterial tag reflection phase matrix.

[0038] Specifically, this invention aims to simultaneously maximize the communication and rate of the main system in a cell-free co-existing communication sensing system. and subsystem communication and speed To optimize the target, and with the constraint of ensuring the SINR of the received signal under multiple sensing targets, a joint design of the AP transmit beamforming matrix is ​​proposed. The reflection phase matrix of metamaterial tags AP receives equalization vector , This is a diagonal matrix operator. Due to the limited transmit power of each AP, the transmit beamforming matrix for each AP is... Power constraints must be met. Furthermore, since each metamaterial reflective unit of the passive metamaterial tag only changes the phase of the signal, the l-th metamaterial tag's... The reflection phase of each reflecting unit The unit modulus constraint must be satisfied. Based on this, the following multi-objective joint optimization problem for active beamforming at the transceiver end and passive beamforming using metamaterials is established: ,in, Maximum transmit power for each AP To detect the minimum SINR threshold, In order to seek The absolute value, , , , The number of reflective units equipped for each passive metamaterial tag.

[0039] Step S3: Using a Pareto optimization framework based on constraint transformation, the multi-objective optimization problem is transformed into a single-objective optimization problem, and the optimization variables described in step S2 are solved by alternating iterations to obtain the final beamforming optimization result.

[0040] To effectively solve the aforementioned multi-objective optimization problem of joint beamforming, this invention employs a Pareto optimization framework based on constraint transformation. The core of this framework lies in transforming the subsystem communication and rate optimization objectives into constraints under different thresholds, thereby converting the multi-objective optimization problem into a single-objective optimization problem that maximizes the main system's communication and rate, i.e.: Then, by adjusting different thresholds... This study addresses single-objective problems under different thresholds to obtain a set of optimal solutions for multi-objective problems. Furthermore, to address the strong non-convexity and strong variable coupling in this single-objective optimization problem, auxiliary variables are introduced based on the Weighted Minimum Mean-Square Error (WMMSE) technique. , The objective function of a single-objective optimization problem Transform it into a convex function form that is easier to handle: ,in, , as well as The k-th master system receiving terminal is respectively The r-th possible value The MSE weighting coefficient, user reception equalization coefficient, and MSE are as follows. Let M be the equivalent channel from the k-th master system receiving terminal to the M APs. In order to seek conjugate, , express A vector of all zeros of size To find the real part, the AP transmit beamforming matrix is ​​then alternately optimized using the semidefinite relaxation and alternating iterative optimization techniques. AP receive equalization vector Metamaterial tag reflection phase matrix and auxiliary variables , The algorithm continues until it meets the convergence condition, and finally outputs the joint beamforming optimization results, where... express.

[0041] For AP transmit beamforming matrix Fixed AP receive equalization vector Metamaterial tag reflection phase matrix and auxiliary variables , And introduce auxiliary variables Then the above single-objective optimization problem is transformed into an optimization problem concerning auxiliary variables. The sub-optimization problem, unless the convex rank constraint is met. In addition, it concerns the optimization variables. This is a convex optimization problem. A semidefinite relaxation technique is used to obtain auxiliary variables. The solution, if the obtained auxiliary variable satisfy Then use eigenvalue decomposition to equivalently restore Otherwise, Gaussian randomization is used to obtain... The approximate optimal solution, where, , Using eigenvalue decomposition to equivalently reduce And obtain using Gaussian randomization technique The specific process of finding the approximate optimal solution is based on existing technology.

[0042] For the reflection phase matrix of metamaterial tags Fixed AP transmit beamforming matrix AP receive equalization vector and auxiliary variables , And introduce auxiliary variables Then the above single-objective optimization problem is transformed into a problem about... The optimization subproblem, and the above regarding The sub-optimization problem is similar, except that, except for the convex rank 1 constraint, it is a problem with respect to the optimization variables. For the convex optimization problem, the semidefinite relaxation technique and Gaussian randomization technique are also used to obtain the metamaterial tag reflection phase matrix. The approximate optimal solution is obtained using existing technology, where... , For matrix A vector of diagonal elements.

[0043] For AP receive equalization vector Fixed alternating optimization of AP transmit beamforming matrix Metamaterial tag reflection phase matrix and auxiliary variables , L can be established respectively regarding Independent subproblems This problem takes the form of a generalized Rayleigh quotient, and the AP receive equalization vector can be obtained by performing eigenvalue decomposition on it. The optimal solution, specifically the solution process, is based on existing technology, where... , ,in, For the first The reflection phase matrix of a metamaterial tag reflective unit. Indicates M APs to the first Channels for a metamaterial tag.

[0044] For auxiliary variables , Fixed AP transmit beamforming matrix AP receive equalization vector and metamaterial tag reflection phase matrix Afterwards, information about , The convex optimization problem can be derived based on the weighted least mean square error technique and first-order differentiation. , The optimal closed-form solution is obtained, and the specific solution process is based on existing technology.

[0045] The above describes how to solve the AP transmit beamforming matrix separately. AP receive equalization vector Metamaterial tag reflection phase matrix and auxiliary variables , The specific process involves iteratively solving for each optimization variable until the calculation results converge, thereby obtaining a fixed threshold. The results of the joint beamforming optimization are shown below.

[0046] Figure 5 and Figure 6 The comparison scheme in the middle is:

[0047] Random phase coefficient scheme: The elements in the phase vector of the metamaterial tag reflection are random complex numbers with a modulus of 1;

[0048] Fixed phase coefficient scheme: The elements in the phase vector of the metamaterial tag reflection are the solution problem. get:

[0049] from Figure 5 and Figure 6 It is evident that the present invention achieves significant performance advantages compared to the comparative schemes, and helps to improve the co-occurrence communication and sensing performance of cellless systems.

[0050] In this invention, the terms "first," "second," and "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. The use of terms such as "upper," "lower," "left," "right," "front," and "rear" to indicate orientation or positional relationships is based on the orientation or positional relationships shown in the accompanying drawings and is only for the convenience of describing the invention, not to indicate or imply that the device referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation on the scope of protection of this invention. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0051] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0052] The above are merely specific embodiments of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept shall be considered as infringing upon the protection scope of the present invention.

Claims

1. An optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags, characterized in that: Includes the following steps: Step S1: Construct a cellless symbiotic communication and sensing system based on passive metamaterial tags. The system includes multiple full-duplex APs, multiple passive metamaterial tags, multiple main system receiving terminals and one secondary system receiving terminal. The system simultaneously has active data communication from APs to main system receiving terminals, passive data communication from passive metamaterial tags to secondary system receiving terminals, and multi-target sensing and detection of passive metamaterial tags by APs. Step S2: Construct a joint beamforming multi-objective optimization problem. This multi-objective optimization problem simultaneously maximizes the communication and rate of the main system and the communication and rate of the subsystem. The constraints include the sensing SINR under multiple sensing targets. The optimization variables are the AP transmit beamforming matrix, the AP receive equalization vector, and the metamaterial tag reflection phase matrix. Step S3: Using a Pareto optimization framework based on constraint transformation, the multi-objective optimization problem is transformed into a single-objective optimization problem, and the optimization variables described in step S2 are solved by alternating iterations to obtain the final beamforming optimization result.

2. The optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags according to claim 1, characterized in that: In step S1, for the active data communication, M APs, assisted by L passive metamaterial tags, collaboratively send active communication signals to K main system receivers. For the passive data communication, each passive metamaterial tag passively sends its environmental perception information to the secondary system receiver terminal through backscattering technology. For multi-target perception and detection, the M APs use the received echo signals to collaboratively perceive the L passive metamaterial tags.

3. The optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags according to claim 2, characterized in that: In step S2, the multi-objective optimization problem is expressed as: ,in, Let m be the transmit beamforming matrix of the m-th AP. To sense the AP receiving equalization vector of the l-th metamaterial tag. Let l be the reflection phase matrix of the l-th metamaterial tag. Main system communication and speed, For secondary system communication and speed, For the sensing SINR of the l-th passive metamaterial tag, To detect the minimum SINR threshold, Maximum transmit power for each AP In order to seek The absolute value, For the l-th metamaterial tag, the first The reflection phase of each reflecting unit The number of reflective units equipped for each passive metamaterial tag. For diagonal matrix operators, , , .

4. The optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags according to claim 3, characterized in that: In step S2, the main system communication and rate are expressed as follows: ,in, This is the nth communication data symbol transmitted by the l-th metamaterial tag using BPSK modulation. , This indicates the calculation of mathematical expectation. The received signal-to-dryness ratio of the k-th primary system receiving terminal is represented by: The communication and rate of the secondary system are expressed as: Q represents the period of the secondary communication data symbol being Q times the period of the primary communication data symbol. The number of antennas equipped for each AP, Let V be the variance of the Gaussian channel noise contained in the received signal of the subsystem terminal within the period of the q-th main communication symbol in the n-th secondary communication data symbol. Let l be the reflection amplitude coefficient of the l-th metamaterial tag. For the l-th metamaterial tag to the secondary system receiving terminal channel, For each AP to the l-th metamaterial tag, The total transmit beamforming matrix for M APs, For size The identity matrix.

5. The optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags according to claim 4, characterized in that: In step S2, the sensing SINR of the l-th passive metamaterial tag is represented as: ,in, This represents the channel from the l'th passive metamaterial tag to the M APs.

6. The optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags according to claim 5, characterized in that: In step S3, the single-objective optimization problem is expressed using the Pareto optimization framework based on constraint transformation as follows: By adjusting different thresholds for subsystem communication and rate It solves single-objective optimization problems under different thresholds to obtain a set of optimal solutions for multi-objective problems, and introduces auxiliary variables to... , The objective function of the single-objective optimization problem is transformed and decoupled, becoming a convex function form that is easier to handle. Then, the positive semidefinite relaxation and alternating iterative optimization methods are used to alternately optimize the transmit beamforming matrix. AP receive equalization vector Metamaterial tag reflection phase matrix and auxiliary variables , The process continues until the convergence condition is met, at which point the final beamforming optimization result is output, where... It represents the set of complex numbers.

7. The optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags according to claim 6, characterized in that: In step S3, for the transmitted beamforming matrix Fixed AP receive equalization vector Metamaterial tag reflection phase matrix and auxiliary variables , And introduce auxiliary variables Then the single-objective optimization problem is transformed into a problem concerning auxiliary variables. The sub-optimization problem is solved by using a positive semidefinite relaxation technique to obtain auxiliary variables. The solution, if the obtained auxiliary variable satisfy Then use eigenvalue decomposition to equivalently restore Otherwise, Gaussian randomization is used to obtain... The approximate optimal solution, where, , .

8. The optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags according to claim 7, characterized in that: In step S3, the reflection phase matrix of the metamaterial tag is... Fixed AP transmit beamforming matrix AP receive equalization vector and auxiliary variables , And introduce auxiliary variables Then the single-objective optimization problem is transformed into a problem about The optimization subproblem is solved by employing semidefinite relaxation and Gaussian randomization techniques to obtain an approximate optimal solution for the metamaterial tag reflection phase matrix. , For matrix A vector of diagonal elements.

9. The optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags according to claim 8, characterized in that: In step S3, for the AP receive equalization vector Fixed alternating optimization of AP transmit beamforming matrix Metamaterial tag reflection phase matrix and auxiliary variables , L can be established respectively regarding Independent subproblems This problem takes the form of a generalized Rayleigh quotient, and the AP receive equalization vector can be obtained by performing eigenvalue decomposition on it. The optimal solution, where , .

10. The optimization method for a cell-free co-occurrence communication sensing system based on passive metamaterial tags according to claim 9, characterized in that: In step S3, for auxiliary variables , Fixed AP transmit beamforming matrix AP receive equalization vector and metamaterial tag reflection phase matrix Afterwards, information about , The convex optimization problem can be derived based on the weighted least mean square error technique and first-order differentiation. , The optimal closed-form solution.

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