A method for optimizing the capacity of an omni-directional smart reflector assisted heterogeneous cooperative sensing system

Abstract

This invention discloses a capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflector. In a heterogeneous cooperative sensing integrated system composed of multiple sensing base stations and one single-function communication base station, an optimization function is constructed with the goal of maximizing the sum of achievable capacity for communication users and detection targets. A convex optimization method is used to derive the allocation scheme between the sensing integration time and the pure communication time throughout the entire transmission cycle. A first-order Taylor estimation method, a logarithmic transformation method, and a quadratic transformation method are used to derive the design schemes for the transmitting beamforming of the sensing base station and the reflection and refraction coefficient matrices of the omnidirectional intelligent reflector. After iterative optimization, the optimal solutions for the time allocation scheme, the transmitting beamforming design scheme, and the reflection and refraction coefficient matrix design scheme are obtained, thereby maximizing the sum of achievable capacity for communication users and detection targets in the heterogeneous cooperative sensing system.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication, and specifically relates to a capacity optimization method for heterogeneous cooperative sensing systems assisted by omnidirectional intelligent reflectors. Background Technology

[0002] Sensing and sensing integration is considered one of the potential key technologies for sixth-generation mobile communication systems (6G). It enables the efficient integration of communication and sensing functions through the sharing of hardware and software resources, endowing existing cellular mechanisms with inherent sensing capabilities. Existing large-scale infrastructure, such as base stations, will be able to provide ubiquitous sensing services. In future 6G, the rise of vertical application scenarios such as smart factories will place higher demands on sensing accuracy and coverage. A single sensing and sensing node, due to its limited sensing capabilities and strict resource constraints, can hardly meet these high-quality sensing requirements. Multi-base station collaborative sensing and sensing mechanisms can utilize existing communication infrastructure and signals for sensing. Through the collaboration of multiple sensing base stations, the limitations of a single sensing and sensing base station in terms of sensing capabilities and perspective can be effectively overcome.

[0003] In practical systems, the coverage of base station communication signals is often limited, thus restricting the sensing coverage. Smart reflectors, another promising technology in 6G, can reshape the intelligent radio propagation environment, particularly by creating additional line-of-sight links to help sensing base stations extend their sensing and communication coverage. Traditional smart reflectors are only suitable for scenarios where the target and the communication user are on the same side of the smart reflector. Addressing this limitation, omnidirectional smart reflectors can simultaneously enhance sensing performance even when the target and the communication user are on opposite sides of the smart reflector, possessing the unique ability to create highly flexible, all-space radio signal coverage.

[0004] Currently, most omnidirectional intelligent reflector-assisted sensing integration methods consider single-site sensing scenarios, while multi-site collaborative sensing integration methods using omnidirectional intelligent reflectors are still in their early stages; furthermore, the research scenarios only consider the existence of sensing base stations. In future practical 6G scenarios, various heterogeneous base stations (such as sensing duplex base stations and pure communication base stations) will cooperate and coexist to provide heterogeneous collaborative communication and sensing functions, meeting the demands of 6G vertical application scenarios for higher-quality communication and sensing performance. Summary of the Invention

[0005] Purpose of the invention: The technical problem to be solved by the present invention is to address the shortcomings of the prior art by providing a capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface, comprising the following steps:

[0006] Step 1: Determine the local detection probability and local false alarm probability of each integrated sensing base station, and determine the following based on the local detection probability. The probability of collaborative detection among integrated sensor base stations;

[0007] Step 2: Determine the sum of achievable capacities for M communication users within the integrated sensing cycle. Based on the detection probabilities obtained in Step 1, determine the achievable capacity of the detection target within the pure communication cycle, thereby determining the capacity within the entire transmission cycle. The sum of the reachable capacities of all communication users and detection targets is the system's reachable capacity.

[0008] Step 3: Based on the time of synesthesia integration And pure communication time allocation strategy, transmission beamforming of sensing base stations and the reflection coefficient matrix of the omnidirectional intelligent reflective surface and refractive index matrix Construct an optimization objective function with the goal of maximizing the system and achievable capacity. ;

[0009] Step 4: Based on the optimization objective function , for synesthetic integration time Optimize the allocation strategy for pure communication time;

[0010] Step 5: Based on the optimization objective function And the optimized synesthetic timing strategy Constructing a joint optimization function Beamforming for the transmission of the sensing base station And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface. Perform joint optimization;

[0011] Step 6: Repeat steps 4 and 5 until the preset convergence condition is met, thus obtaining the synesthetic timing strategy that maximizes the system's achievable capacity. Beamforming for transmission from a sensor base station And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface This maximizes the system and achievable capacity of communication users and detection targets in heterogeneous collaborative sensing systems.

[0012] In some embodiments, step 1 specifically includes:

[0013] Step 1-1: Select any integrated sensing base station The obtained local detection probability and local false alarm probability ;

[0014] Step 1-2: Determine based on the local detection probability Collaborative detection probability of a single integrated sensing base station The following formula represents it:

[0015]

[0016] In some embodiments, step 1-1 is represented by the following formula for any integrated sensing base station. The obtained local detection probability and local false alarm probability :

[0017]

[0018] in, Indicates the duration of synesthetic integration. Indicates the sampling frequency. This indicates the detection threshold, determined by the local false alarm probability. Expected value Calculated, i.e. , , Indicates a sensor base station and omnidirectional intelligent reflective surface The channel between, Indicates an omnidirectional intelligent reflective surface The channel between the target and the detection target Indicates a sensor base station The transmit beamforming matrix, Indicates a sensor base station Receiver noise, It is a complementary cumulative distribution function.

[0019] In some embodiments, step 2 specifically includes:

[0020] Step 2-1: Determine the integration cycle of induction and communication The sum of achievable capacity for each communication user And the achievable capacity of target detection within a pure communication cycle. ;

[0021] Step 2-2: Based on the parameters determined in Step 2-1, determine the parameters for the entire transmission cycle using the following formula. The system and achievable capacity for individual communication users and detection targets. :

[0022]

[0023] In some embodiments, step 2-1 is expressed by the following formula within the inductive integration cycle. The sum of achievable capacity for each communication user

[0024]

[0025] in, Indicates the time allocation strategy. Indicates communication user Signal-to-interference-to-noise ratio, Indicates an omnidirectional intelligent reflective surface With communication users The channel between, yes The Columns represent sensor base stations To communication users Transmit beamforming, This represents the reflection coefficient matrix of the omnidirectional intelligent reflective surface. Indicates a sensor base station and omnidirectional intelligent reflective surface The channel between, Indicates communication user The received noise power;

[0026] The achievable capacity of a target detection within a pure communication cycle is expressed by the following formula. :

[0027]

[0028] in, Indicates the duration of the entire system transmission cycle. Indicates the duration of synesthetic integration. This represents the signal-to-noise ratio of the detected target. This represents the channel between the pure communication base station and the detection target. This indicates the transmit beamforming of a pure communication base station. This represents the probability that the target actually exists.

[0029] In some embodiments, step 3 involves constructing an optimization function with the objective of maximizing the system and achievable capacity. Specifically:

[0030]

[0031] in, This indicates the maximum transmission power of each sensing base station. Indicates the duration of the entire system transmission cycle. This represents the minimum threshold for the probability of collaborative detection. Indicates the probability of collaborative detection. and These represent omnidirectional intelligent reflective surfaces. The The square of the reflection and refraction amplitudes of each element, Indicates an omnidirectional intelligent reflective surface The Phase shift of reflection or refraction of an element.

[0032] In some embodiments, step 4 provides the transmit beamforming for the sensing base station. And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface Under the premise of [the above], the optimization problem of the time allocation strategy is transformed into a convex optimization problem, and solved by a convex optimization algorithm.

[0033] In some embodiments, step 5 includes the following steps:

[0034] Step 5-1: Given a time allocation strategy Under the premise of this, the first-order Taylor estimation method, logarithmic transformation method, and quadratic transformation method are used to transform the logarithmic optimization function, square root optimization function, and fractional optimization function in the objective function into homogeneous optimization functions, which facilitates the solution of the problem. This is then used to construct the transmit beamforming for the sensing base station. And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface Joint optimization function :

[0035]

[0036] in, Represents auxiliary variables. Represents an auxiliary vector. , , Indicates the first The result obtained in the next iteration The value, , , ; , , Indicates the first The result obtained in the next iteration The value;

[0037] Step 5-2: Based on the objective function In the In the next iteration, update according to the following formula :

[0038]

[0039] Step 5-3: Update by solving the following subproblems The constraints are ,

[0040]

[0041] in, ; ;

[0042] Step 5-4: According to , Solve for the reflection and refraction coefficient matrices of the omnidirectional intelligent reflective surface. .

[0043] In some embodiments, step 5-4 includes the following steps:

[0044] Step 5-4-1: Update by solving the following expression problem The constraints are ,

[0045]

[0046] in, ; , ;

[0047] Step 5-4-2: Solve the following equation using the phase rotation method. (Update problem) The constraints are and 6,

[0048]

[0049] in, , , ;

[0050] Step 5-4-3: Solve the following subproblem using the particle swarm optimization method, and update... The constraints are 4,

[0051]

[0052] in, , , ;

[0053] Step 5-4-4: According to , The reflection and refraction coefficient matrices of the omnidirectional intelligent reflective surface are obtained. .

[0054] In some embodiments, step 6 repeatedly executes steps 4 to 5, and the iteration terminates when the condition is the first iteration. The iteration and the The difference in achievable capacity of the system obtained in each iteration is less than .

[0055] The beneficial effects of this invention are as follows: Addressing the limitation of the limited coverage of communication and sensing signals of a single sensing base station, this invention utilizes an omnidirectional intelligent reflective surface to assist multiple sensing base stations in collaboratively sensing targets outside the coverage area of ​​the communication signal within the integrated sensing time, while simultaneously providing communication services to multiple communication users. Furthermore, based on the collaborative sensing results of multiple sensing base stations, it assists a pure communication base station in providing communication services to the detected target within the pure communication time. Through the allocation strategy of integrated sensing and pure communication time, the beamforming of the sensing base station's transmission, and the joint optimization of the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface, a significant improvement in the combined achievable throughput of communication users and detected targets is achieved in the heterogeneous collaborative integrated sensing system. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the process of the present invention.

[0057] Figure 2 This is a schematic diagram illustrating the application of the present invention. Detailed Implementation

[0058] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading the present invention, any modifications of the present invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0059] This invention discloses a capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflector. In a heterogeneous cooperative sensing integrated system composed of multiple sensing base stations and one single-function communication base station, an optimization function is constructed with the goal of maximizing the achievable capacity of the sum of communication users and detection targets. A convex optimization method is used to derive the allocation scheme of sensing integration time and pure communication time throughout the entire transmission cycle. The first-order Taylor estimation method, logarithmic transformation method, and quadratic transform method are used to derive the design schemes for the transmitting beamforming of the sensing base station and the reflection and refraction coefficient matrix of the omnidirectional intelligent reflector. After iterative optimization, the optimal solutions for the time allocation scheme, the transmitting beamforming design scheme, and the reflection and refraction coefficient matrix design scheme are obtained, thereby maximizing the achievable capacity of the sum of communication users and detection targets in the heterogeneous cooperative sensing system.

[0060] like Figure 1-2 The method for capacity optimization of a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface includes the following specific steps:

[0061] Step 1: Determine the local detection probability and local false alarm probability of each integrated sensing base station, and determine the following based on the local detection probability. The probability of collaborative detection among integrated sensor base stations;

[0062] Step 2: Determine the sum of achievable capacities for M communication users within the integrated sensing cycle. Based on the detection probabilities obtained in Step 1, determine the achievable capacity of the detection target within the pure communication cycle, thereby determining the capacity within the entire transmission cycle. The sum of the reachable capacities of all communication users and detection targets is the system's reachable capacity.

[0063] Step 3: Based on the time of synesthesia integration And pure communication time allocation strategy, transmission beamforming of sensing base stations and the reflection coefficient matrix of the omnidirectional intelligent reflective surface and refractive index matrix Construct an optimization objective function with the goal of maximizing the system and achievable capacity. ;

[0064] Step 4: Based on the optimization objective function , for synesthetic integration time Optimize the allocation strategy for pure communication time;

[0065] Step 5: Based on the optimization objective function And the optimized synesthetic timing strategy Constructing a joint optimization function Beamforming for the transmission of the sensing base station And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface. Perform joint optimization;

[0066] Step 6: Repeat steps 4 and 5 until the preset convergence condition is met, thus obtaining the synesthetic timing strategy that maximizes the system's achievable capacity. Beamforming for transmission from a sensor base station And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface This maximizes the system and achievable capacity of communication users and detection targets in heterogeneous collaborative sensing systems.

[0067] Step 1 specifically includes:

[0068] Step 1-1: Select any integrated sensing base station The obtained local detection probability and local false alarm probability ;

[0069] Step 1-2: Determine based on the local detection probability Collaborative detection probability of a single integrated sensing base station The following formula represents it:

[0070]

[0071] In some embodiments, step 1-1 is represented by the following formula for any integrated sensing base station. The obtained local detection probability and local false alarm probability :

[0072]

[0073] in, Indicates the duration of synesthetic integration. Indicates the sampling frequency. This indicates the detection threshold, determined by the local false alarm probability. Expected value Calculated, i.e. , , Indicates a sensor base station and omnidirectional intelligent reflective surface The channel between, Indicates an omnidirectional intelligent reflective surface The channel between the target and the detection target Indicates a sensor base station The transmit beamforming matrix, Indicates a sensor base station Receiver noise, It is a complementary cumulative distribution function.

[0074] Step 2 specifically includes:

[0075] Step 2-1: Determine the integration cycle of induction and communication The sum of achievable capacity for each communication user And the achievable capacity of target detection within a pure communication cycle. ;

[0076] Step 2-2: Based on the parameters determined in Step 2-1, determine the parameters for the entire transmission cycle using the following formula. The system and achievable capacity for individual communication users and detection targets. :

[0077]

[0078] In step 2-1, the following formula represents the period of the induction integration cycle: The sum of achievable capacity for each communication user

[0079]

[0080] in, Indicates the time allocation strategy. Indicates communication user Signal-to-interference-to-noise ratio, Indicates an omnidirectional intelligent reflective surface With communication users The channel between, yes The Columns represent sensor base stations To communication users Transmit beamforming, This represents the reflection coefficient matrix of the omnidirectional intelligent reflective surface. Indicates a sensor base station and omnidirectional intelligent reflective surface The channel between, Indicates communication user The received noise power;

[0081] The achievable capacity of a target detection within a pure communication cycle is expressed by the following formula. :

[0082]

[0083] in, Indicates the duration of the entire system transmission cycle. Indicates the duration of synesthetic integration. This represents the signal-to-noise ratio of the detected target. This represents the channel between the pure communication base station and the detection target. This indicates the transmit beamforming of a pure communication base station. This represents the probability that the target actually exists.

[0084] In step 3, an optimization function is constructed with the objective of maximizing the system and achievable capacity. Specifically:

[0085]

[0086] in, This indicates the maximum transmission power of each sensing base station. Indicates the duration of the entire system transmission cycle. This represents the minimum threshold for the probability of collaborative detection. Indicates the probability of collaborative detection. and These represent omnidirectional intelligent reflective surfaces. The The square of the reflection and refraction amplitudes of each element, Indicates an omnidirectional intelligent reflective surface The Phase shift of reflection or refraction of an element.

[0087] Step 4 provides the transmit beamforming for the sensing base station. And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface Under the premise of [the above], the optimization problem of the time allocation strategy is transformed into a convex optimization problem, and solved by a convex optimization algorithm.

[0088] Step 5 includes the following steps:

[0089] Step 5-1: Given a time allocation strategy Under the premise of this, the first-order Taylor estimation method, logarithmic transformation method, and quadratic transformation method are used to transform the logarithmic optimization function, square root optimization function, and fractional optimization function in the objective function into homogeneous optimization functions, which facilitates the solution of the problem. This is then used to construct the transmit beamforming for the sensing base station. And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface Joint optimization function :

[0090]

[0091] in, Represents auxiliary variables. Represents an auxiliary vector. , , Indicates the first The result obtained in the next iteration The value, , , ; , , Indicates the first The result obtained in the next iteration The value;

[0092] Step 5-2: Based on the objective function In the In the next iteration, update according to the following formula :

[0093]

[0094] Step 5-3: Update by solving the following subproblems The constraints are ,

[0095]

[0096] in, ; ;

[0097] Step 5-4: According to , Solve for the reflection and refraction coefficient matrices of the omnidirectional intelligent reflective surface. .

[0098] In some embodiments, step 5-4 includes the following steps:

[0099] Step 5-4-1: Update by solving the following expression problem The constraints are ,

[0100]

[0101] in, ; , ;

[0102] Step 5-4-2: Solve the following equation using the phase rotation method. (Update problem) The constraints are and 6,

[0103]

[0104] in, , , ;

[0105] Step 5-4-3: Solve the following subproblem using the particle swarm optimization method, and update... The constraints are 4,

[0106]

[0107] in, , , ;

[0108] Step 5-4-4: According to , The reflection and refraction coefficient matrices of the omnidirectional intelligent reflective surface are obtained. .

[0109] Step 6 repeats steps 4 and 5 in a loop, with the iteration termination condition being the first iteration. The iteration and the The difference in achievable capacity of the system obtained in each iteration is less than .

[0110] With the assistance of omnidirectional intelligent reflective surfaces and multi-base station collaborative sensing results, this invention proposes a joint design scheme for time allocation, transmission beamforming, and reflection and refraction coefficient matrices. This scheme can provide flexible full-space sensing service coverage for communication users and sensing targets located in different spatial areas in real-world scenarios where different types of base stations coexist, thereby effectively improving the overall communication performance of the heterogeneous collaborative sensing integrated system.

[0111] This invention provides a concept and method for capacity optimization of a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface, characterized in that, Includes the following steps: Step 1: Determine the local detection probability and local false alarm probability of each integrated sensing base station, and determine the following based on the local detection probability. The probability of collaborative detection among integrated sensor base stations; Step 2: Determine the sum of achievable capacities for M communication users within the integrated sensing cycle. Based on the detection probabilities obtained in Step 1, determine the achievable capacity of the detection target within the pure communication cycle, thereby determining the capacity within the entire transmission cycle. The sum of the reachable capacities of all communication users and detection targets is the system's reachable capacity. Step 3: Based on the time of synesthesia integration And pure communication time allocation strategy, transmission beamforming of sensing base stations and the reflection coefficient matrix of the omnidirectional intelligent reflective surface and refractive index matrix Construct an optimization objective function with the goal of maximizing the system and achievable capacity. ; Step 4: Based on the optimization objective function , for synesthetic integration time Optimize the allocation strategy for pure communication time; Step 5: Based on the optimization objective function And the optimized synesthetic timing strategy Constructing a joint optimization function Beamforming for the transmission of the sensing base station And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface. Perform joint optimization; Step 6: Repeat steps 4 and 5 until the preset convergence condition is met, thus obtaining the synesthetic timing strategy that maximizes the system's achievable capacity. Beamforming for transmission from a sensor base station And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface This maximizes the system and achievable capacity of communication users and detection targets in heterogeneous collaborative sensing systems.

2. The capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface according to claim 1, characterized in that, Step 1 specifically includes: Step 1-1: Select any integrated sensing base station The obtained local detection probability and local false alarm probability ; Step 1-2: Determine based on the local detection probability Collaborative detection probability of a single integrated sensing base station The following formula represents it: 。 3. The capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface according to claim 2, characterized in that, In step 1-1, each of the following formulas represents an integrated sensing base station. The obtained local detection probability and local false alarm probability : in, Indicates the duration of synesthesia integration. Indicates the sampling frequency. This indicates the detection threshold, determined by the local false alarm probability. Expected value Calculated, i.e. , , Indicates a sensor base station and omnidirectional intelligent reflective surface The channel between, Indicates an omnidirectional intelligent reflective surface The channel between the target and the detection target Indicates a sensor base station The transmit beamforming matrix, Indicates a sensor base station Receiver noise, It is a complementary cumulative distribution function.

4. The capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface according to claim 1, characterized in that, Step 2 specifically includes: Step 2-1: Determine the integration cycle of induction and communication The sum of achievable capacity for each communication user And the achievable capacity of target detection within a pure communication cycle. ; Step 2-2: Based on the parameters determined in Step 2-1, determine the parameters for the entire transmission cycle using the following formula. The system and achievable capacity for individual communication users and detection targets. :

5. The capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface according to claim 4, characterized in that, In step 2-1, the following formula represents the period of the induction integration cycle: The sum of achievable capacity for each communication user in, Indicates time allocation strategy, Indicates communication user Signal-to-interference-to-noise ratio, Indicates an omnidirectional intelligent reflective surface With communication users The channel between, yes The Columns represent the communication base stations. To communication users Transmit beamforming, This represents the reflection coefficient matrix of the omnidirectional intelligent reflective surface. Indicates a sensor base station and omnidirectional intelligent reflective surface The channel between, Indicates communication user The received noise power; The achievable capacity of a target detection within a pure communication cycle is expressed by the following formula. : in, Indicates the duration of the entire system transmission cycle. Indicates the duration of synesthesia integration. This represents the signal-to-noise ratio of the detected target. This represents the channel between the pure communication base station and the detection target. This indicates the transmit beamforming of a pure communication base station. This represents the probability that the target actually exists.

6. The capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface according to claim 5, characterized in that, In step 3, an optimization function is constructed with the objective of maximizing the system and achievable capacity. Specifically: in, This indicates the maximum transmission power of each sensing base station. Indicates the duration of the entire system transmission cycle. This represents the minimum threshold for the probability of collaborative detection. Indicates the probability of collaborative detection. and These represent omnidirectional intelligent reflective surfaces. The The square of the reflection and refraction amplitudes of each element, Indicates an omnidirectional intelligent reflective surface The Phase shift of reflection or refraction of an element.

7. The capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface according to claim 6, characterized in that, Step 4 provides the transmit beamforming for the sensing base station. And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface Under the premise of [the above], the optimization problem of the time allocation strategy is transformed into a convex optimization problem, and solved by a convex optimization algorithm.

8. The capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface according to claim 6 or 7, characterized in that, Step 5 includes the following steps: Step 5-1: Given a time allocation strategy Under the premise of this, the first-order Taylor estimation method, logarithmic transformation method, and quadratic transformation method are used to transform the logarithmic optimization function, square root optimization function, and fractional optimization function in the objective function into homogeneous optimization functions, which facilitates the solution of the problem. This is then used to construct the transmit beamforming for the sensing base station. And the reflection and refraction coefficient matrix of the omnidirectional intelligent reflective surface Joint optimization function : in, Represents auxiliary variables. Represents an auxiliary vector. , , Indicates the first The result obtained in the next iteration The value, , , ; , , Indicates the first The result obtained in the next iteration The value; Step 5-2: Based on the objective function In the In the next iteration, update according to the following formula : Step 5-3: Update by solving the following subproblems The constraints are , in, ; ; Step 5-4: According to , Solve for the reflection and refraction coefficient matrices of the omnidirectional intelligent reflective surface. .

9. The capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface according to claim 8, characterized in that, Step 5-4 includes the following steps: Step 5-4-1: Update by solving the following expression problem The constraints are , in, ; , ; Step 5-4-2: Solve the following equation using the phase rotation method. (Update problem) The constraints are and 6, in, , , ; Step 5-4-3: Solve the following subproblem using the particle swarm optimization method, and update... The constraints are 4, in, , , ; Step 5-4-4: According to , The reflection and refraction coefficient matrices of the omnidirectional intelligent reflective surface are obtained. .

10. The capacity optimization method for a heterogeneous cooperative sensing system assisted by an omnidirectional intelligent reflective surface according to claim 1, characterized in that, Step 6 repeats steps 4 and 5 in a loop, with the iteration termination condition being the first iteration. The iteration and the The difference in achievable capacity of the system obtained in each iteration is less than .

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