Cooperative sensing method, device and equipment based on cooperative sensing and multi-point cooperation

Abstract

The application discloses a kind of based on the cooperation of multiple points and the integrated cooperation sensing method, device and equipment of sensing, applied to the integrated communication and sensing system assisted by multiple point cooperation technology, it is related to computer communication technical field, it can solve the technical problem that the sensing performance is poor caused by the communication quality is satisfied and the sensing performance is ignored, including: obtaining base station set and user set, for any one target user in user set, the signal-to-interference-and-noise ratio of target user is constructed, the sum of the signal-to-interference-and-noise ratio of all target users is maximum as target function;According to signal-to-interference-and-noise ratio and the minimum value of the signal-to-interference-and-noise ratio of target user, signal-to-interference-and-noise ratio constraint is constructed, sensing performance constraint is constructed, power constraint and base station quantity constraint are constructed;Target function, signal-to-interference-and-noise ratio constraint, sensing performance constraint, power constraint and base station quantity constraint are used as target model, target model is iteratively solved, and target optimal solution is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer communication technology, and in particular to a multi-point cooperation-based integrated communication and sensing cooperation sensing method, device and equipment. BACKGROUND

[0002] The integrated communication and sensing system aims to integrate the communication system and the radar system into one system to solve the contradictions between them in the development process.

[0003] In communication, multi-point cooperation technology enables multiple base stations to serve the same user, but in sensing, a single base station senses a target more often. This may increase the problems caused by short sensing distance and lack of sensing resources. At the same time, the next generation of wireless communication systems have higher requirements for communication quality.

[0004] Although more work has been done on multi-base station coordinated service users in communication and sensing, existing research mainly focuses on enhancing coordinated communication and single-base station sensing or coordinated sensing and single-base station communication, and there is not enough research on communication and sensing dual cooperation, so it is urgent to study multi-base station cooperation and integrated sensing. SUMMARY

[0005] Therefore, the present application provides a multi-point cooperation-based integrated communication and sensing cooperation sensing method, device and equipment, which can solve the technical problem of poor sensing performance caused by focusing on communication quality and ignoring sensing performance.

[0006] According to a first aspect of the present application, a multi-point cooperation-based integrated communication and sensing cooperation sensing method is provided, which is applied to a multi-point cooperation technology-assisted integrated communication and sensing system, and the method comprises:

[0007] Obtaining a base station set and a user set, for any target user in the user set, constructing a signal-to-interference-and-noise ratio of the target user, and taking the sum of the signal-to-interference-and-noise ratios of all target users as a target function;

[0008] Obtaining a minimum value of the signal-to-interference-and-noise ratio of the target user, constructing a signal-to-interference-and-noise ratio constraint according to the signal-to-interference-and-noise ratio and the minimum value of the signal-to-interference-and-noise ratio, constructing a coordinated base station cluster serving the target user in the base station set based on multi-point cooperation, calculating a detection probability based on the coordinated base station cluster, determining a threshold minimum value according to the detection probability, constructing a sensing performance constraint according to the threshold minimum value, constructing a power constraint, and constructing a base station number constraint;

[0009] Taking the target function, the signal-to-interference-and-noise ratio constraint, the sensing performance constraint, the power constraint and the base station number constraint as a target model, iteratively solving the target model to obtain a target optimal solution.

[0010] Preferably, the constructing the signal-to-interference-and-noise ratio of the target user comprises:

[0011] obtaining any one of the base stations in the base station set and any one of the users in the user set which is not the target user;

[0012] constructing a first base station-user association matrix of the base stations and the target user, constructing a second base station-user association matrix of the base stations and the non-target user, constructing a conjugate transpose of a channel vector from the base stations to the target user, constructing a first beamforming vector of the base stations sent to the target user, constructing a second beamforming vector of the base stations sent to the non-target user, and constructing a noise power of the target user;

[0013] constructing the signal-to-interference-and-noise ratio of the target user according to all the base stations, all the non-target users, the first base station-user association matrix, the second base station-user association matrix, the conjugate transpose of the channel vector, the first beamforming vector, the second beamforming vector, and the noise power;

[0014] the constructing the base station number constraint comprises:

[0015] constructing the first base station-user association matrix to be 0 or 1.

[0016] Preferably, the calculating the detection probability based on the coordinated base station cluster comprises:

[0017] obtaining a first base station and a second base station in the coordinated base station cluster;

[0018] calculating a target reflected signal from the second base station to the target user and to the first base station;

[0019] respectively determining a first cumulative signal and a second cumulative signal corresponding to each of the target users according to the target reflected signal when the target exists and when the target does not exist;

[0020] respectively calculating a first probability density function corresponding to the first cumulative signal and a second probability density function corresponding to the second cumulative signal, and calculating a likelihood ratio according to the first probability density function and the second probability density function;

[0021] calculating a first distribution mean and a first distribution variance when the target exists and a second distribution mean and a second distribution variance when the target does not exist according to the likelihood ratio;

[0022] According to the first distribution mean and the first distribution variance, a presence detection probability when the target exists is calculated, according to the second distribution mean and the second distribution variance, a false alarm probability when the target does not exist is calculated, and according to the presence detection probability and the false alarm probability, a detection probability is calculated.

[0023] Preferably, the calculation of the target reflection signal from the second base station to the target user and to the first base station comprises:

[0024] The path loss from the first base station to the target user and to the second base station is constructed;

[0025] The first base station receiving steering vector is constructed, and the second base station transmitting steering vector is constructed;

[0026] According to the path loss, the radar cross section, the first base station receiving steering vector, and the second base station transmitting steering vector, a target reflection matrix from the second base station to the target user and to the first base station is constructed;

[0027] The third beamforming vector transmitted by the second base station to the target user is constructed;

[0028] The target reflection matrix is multiplied by the third beamforming vector to obtain the target reflection signal from the second base station to the target user and to the first base station.

[0029] Preferably, the construction of the perception performance constraint according to the threshold minimum value comprises:

[0030] The third beamforming vector transmitted by the second base station to the target user is constructed;

[0031] According to the path loss, the transpose of the second base station transmitting steering vector, the third beamforming vector, and the threshold minimum value, a perception performance constraint is constructed;

[0032] The construction of the base station number constraint comprises:

[0033] The number of the coordinated base station cluster is constructed to be greater than or equal to 1 and less than or equal to the number of all base stations in the base station set.

[0034] Preferably, the construction of the power constraint comprises:

[0035] The maximum transmission power of the base station is obtained, and according to the first base station user association matrix, the first beamforming vector, and the maximum transmission power, a power constraint is constructed.

[0036] Preferably, the iterative solving of the target model to obtain a target optimal solution comprises:

[0037] setting a preset iteration number, an initial value of the first base station-user association matrix, an initial value of the second base station-user association matrix, an initial value of the first beamforming vector, and an initial value of the second beamforming vector;

[0038] fixing the initial value of the first beamforming vector and the initial value of the second beamforming vector, and iteratively solving the optimal solution of the first base station-user association matrix and the optimal solution of the second base station-user association matrix by using a differential evolution algorithm;

[0039] fixing the initial value of the first base station-user association matrix and the initial value of the second base station-user association matrix, and iteratively solving the optimal solution of the first beamforming vector and the optimal solution of the second beamforming vector by using fractional programming and semi-definite relaxation;

[0040] taking the optimal solution of the first base station-user association matrix, the optimal solution of the second base station-user association matrix, the optimal solution of the first beamforming vector, and the optimal solution of the second beamforming vector as a target optimal solution.

[0041] According to a second aspect of the present application, a cooperative sensing device based on multi-point cooperation is provided, and the device comprises:

[0042] a first constructing module, configured to acquire a base station set and a user set, for any target user in the user set, construct a signal-to-interference-and-noise ratio (SINR) of the target user, and take a sum of the SINRs of all the target users as a target function;

[0043] a second constructing module, configured to acquire a minimum value of the SINR of the target user, construct an SINR constraint according to the SINR and the minimum value of the SINR, construct a coordinated base station cluster serving the target user in the base station set based on multi-point cooperation, calculate a detection probability based on the coordinated base station cluster, determine a threshold minimum value according to the detection probability, construct a sensing performance constraint according to the threshold minimum value, construct a power constraint, and construct a base station number constraint;

[0044] a solving module, configured to take the target function, the SINR constraint, the sensing performance constraint, the power constraint, and the base station number constraint as a target model, iteratively solve the target model, and obtain a target optimal solution.

[0045] According to a third aspect of the present application, a storage medium having a computer program stored thereon is provided, and the computer program is executed by a processor to implement the cooperative sensing method based on multi-point cooperation.

[0046] According to the fourth aspect of the present application, a computer device is provided, comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, and the processor implements the above-mentioned cooperative sensing method based on multi-point cooperation when executing the program.

[0047] By the above technical solution, the present application provides a cooperative sensing method, device and equipment based on multi-point cooperation, which first acquires a base station set and a user set, constructs a signal-to-interference-and-noise ratio of any target user in the user set, takes the maximum sum of the signal-to-interference-and-noise ratios of all target users as a target function, then acquires a minimum value of the signal-to-interference-and-noise ratio of the target user, constructs a signal-to-interference-and-noise ratio constraint according to the signal-to-interference-and-noise ratio and the minimum value of the signal-to-interference-and-noise ratio, constructs a coordinated base station cluster serving the target user in the base station set based on multi-point cooperation, calculates a detection probability based on the coordinated base station cluster, determines a threshold minimum value according to the detection probability, constructs a sensing performance constraint according to the threshold minimum value, constructs a power constraint, and constructs a base station number constraint, and finally takes the target function, the signal-to-interference-and-noise ratio constraint, the sensing performance constraint, the power constraint, and the base station number constraint as a target model, iteratively solves the target model, and obtains a target optimal solution. By the technical solution of the present application, the maximum sum of the signal-to-interference-and-noise ratios is used as the target function and the signal-to-interference-and-noise ratio constraint, the communication quality is guaranteed, the coordinated base station cluster serving the target user in the base station set is constructed based on multi-point cooperation, the detection probability is calculated based on the coordinated base station cluster, the sensing performance constraint is constructed based on the detection probability, and the sensing performance is guaranteed, so that the target model constructed can meet both the communication quality and the sensing performance, and the problem of poor sensing performance caused by the bias of meeting the communication quality and ignoring the sensing performance is solved.

[0048] The above description is only a summary of the technical solution of the present application, in order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present application, and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0050] Figure 1 A flowchart of a cooperative sensing method based on multi-point cooperation provided by the embodiment of the present application is shown;

[0051] Figure 2A flow diagram of another multi-point cooperation based integrated sensing and communication cooperative sensing method provided by the embodiment of the present application is shown;

[0052] Figure 3 A structure diagram of a multi-point cooperation based integrated sensing and communication cooperative sensing device provided by the embodiment of the present application is shown;

[0053] Figure 4 A structure diagram of another multi-point cooperation based integrated sensing and communication cooperative sensing device provided by the embodiment of the present application is shown;

[0054] Figure 5 A scene diagram of a multi-point cooperation technology assisted integrated sensing and communication system provided by the embodiment of the present application is shown;

[0055] Figure 6 A structure diagram of a differential evolution algorithm provided by the embodiment of the present application is shown;

[0056] Figure 7 A result of a Monte Carlo verification convergence cooperative sensing detection probability correctness provided by the embodiment of the present application is shown;

[0057] Figure 8 A result of a convergence experiment of a verification target function provided by the embodiment of the present application is shown;

[0058] Figure 9 A result of an experiment of a sum of signal-to-interference-and-noise ratios of all target users and a maximum transmission power of a base station provided by the embodiment of the present application is shown;

[0059] Figure 10 A result of an experiment of a detection probability and a number of base station antennas provided by the embodiment of the present application is shown;

[0060] Figure 11 A result of an experiment of a detection probability and a minimum value of a signal-to-interference-and-noise ratio provided by the embodiment of the present application is shown;

[0061] Figure 12 A result of an experiment of a detection probability and a maximum transmission power of a base station provided by the embodiment of the present application is shown;

[0062] Figure 13 A result of an experiment of a detection probability and a minimum value of a threshold provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0063] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0064] This embodiment provides a collaborative sensing method based on multi-point cooperation, applicable to a synesthetic system assisted by multi-point cooperation technology, such as... Figure 1 As shown, the method includes:

[0065] 101. Obtain the base station set and the user set. For any target user in the user set, construct the signal-to-interference-plus-noise ratio (SINR) of the target user. Maximize the sum of the SINR of all target users as the objective function.

[0066] 102. Obtain the minimum signal-to-interference-plus-noise ratio (SINR) of the target user, construct an SINR constraint based on the SINR and the minimum SINR, construct a coordinated base station cluster serving the target user in the base station set based on multi-point cooperation, calculate the detection probability based on the coordinated base station cluster, determine the minimum threshold based on the detection probability, construct a perception performance constraint, a power constraint, and a base station quantity constraint based on the minimum threshold.

[0067] 103. Using the objective function, the signal-to-interference-plus-noise ratio constraint, the sensing performance constraint, the power constraint, and the base station number constraint as the objective model, iteratively solve the objective model to obtain the optimal objective solution.

[0068] Regarding steps 101-103 of the embodiment, it should be noted that, as Figure 5 As shown, the multi-point cooperation technology-assisted sensing system consists of K base stations and U target users. Applying multi-point cooperation technology in this system means that multiple base stations are coordinated into a coordinated base station cluster to serve the same target user. This technology transforms co-channel interference into useful superimposed signals. On one hand, it improves communication quality by coordinating multiple base stations to share information in the link, ensuring the communication performance of each target user. On the other hand, the coordinated base station cluster can perform cooperative sensing to guarantee sensing performance, achieving cooperative sensing by multiple base stations. Specifically, under the control of the central controller, all base stations simultaneously send transmission messages to the target user. The target user can be deployed arbitrarily at any location within the cell where two base stations are located. This method requires base stations to share channel state information and target user data information in the link, coordinating multiple base stations communicating with the same target user into a cluster to achieve multi-base station cooperative signal transmission. This transforms traditional interference into a useful signal for the target user. The base station acts as both a provider of communication services and a receiver of sensing signals, while the target user is both a demander of communication services and a sensing target.

[0069] The sum of the signal-to-interference-and-noise ratios is used as a target function, and the signal-to-interference-and-noise ratio constraint is used to ensure communication quality; through the multi-point cooperation technology, multiple base stations are coordinated into a coordinated base station cluster to serve the same target user, the detection probability is calculated based on the coordinated base station cluster, and the sensing performance constraint is constructed based on the detection probability, so that the sensing performance can be ensured; the power constraint and the base station number constraint are basic constraints for auxiliary solving, so that compared with the prior art which only uses the signal-to-interference-and-noise ratio for related calculation, the embodiment further constructs the sensing performance constraint based on the detection probability, so that the sensing performance can be ensured, and the reason is that the detection probability refers to the probability that all base stations in the coordinated base station cluster serving the same target user detect the reflected signal from the same target user; the greater the detection probability, the better the sensing performance of the base station; and the threshold minimum value of the sensing performance constraint is obtained based on the detection probability, so that the sensing performance is ensured to be greater than or equal to the threshold minimum value determined by the detection probability; and the target optimal solution solved under the sensing performance constraint ensures the sensing performance.

[0070] The application provides a multi-point cooperation-based sensing and communication integrated cooperative sensing method, device and equipment. First, a base station set and a user set are acquired, and for any target user in the user set, a signal-to-interference-and-noise ratio of the target user is constructed, and the sum of the signal-to-interference-and-noise ratios of all target users is used as a target function. Then, a minimum value of the signal-to-interference-and-noise ratio of the target user is acquired, a signal-to-interference-and-noise ratio constraint is constructed according to the signal-to-interference-and-noise ratio and the minimum value of the signal-to-interference-and-noise ratio, a coordinated base station cluster serving the target user is constructed in the base station set based on multi-point cooperation, a detection probability is calculated based on the coordinated base station cluster, a threshold minimum value is determined according to the detection probability, a sensing performance constraint is constructed according to the threshold minimum value, a power constraint is constructed, and a base station number constraint is constructed. Finally, the target function, the signal-to-interference-and-noise ratio constraint, the sensing performance constraint, the power constraint and the base station number constraint are used as a target model, and the target model is iteratively solved to obtain a target optimal solution. Through the technical solution of the application, the sum of the signal-to-interference-and-noise ratios is used as a target function and a signal-to-interference-and-noise ratio constraint to ensure communication quality, a coordinated base station cluster serving the target user is constructed in the base station set based on multi-point cooperation, a detection probability is calculated based on the coordinated base station cluster, a sensing performance constraint is constructed based on the detection probability to ensure the sensing performance, and the target model constructed in this way can meet both the communication quality and the sensing performance, thereby solving the problem that the sensing performance is poor due to the fact that the communication quality is biased to be met and the sensing performance is ignored.

[0071] Further, as a refinement and expansion of the above embodiment, in order to completely describe the specific implementation process in the embodiment, another multi-point cooperation-based sensing and communication integrated cooperative sensing method is provided, which is applied to a sensing and communication integrated system assisted by multi-point cooperation technology, as shown in Figure 2 The method comprises the following steps.

[0072] 201、acquiring a base station set and a user set, for any one target user in the user set, constructing a signal-to-interference-and-noise ratio of the target user.

[0073] For the embodiment, the constructing the signal-to-interference-and-noise ratio of the target user comprises: acquiring any one base station in the base station set and any one non-target user in the user set; constructing a first base station-user association matrix of the base station and the target user, constructing a second base station-user association matrix of the base station and the non-target user, constructing a conjugate transpose of a channel vector from the base station to the target user, constructing a first beamforming vector of the base station sent to the target user, constructing a second beamforming vector of the base station sent to the non-target user, and constructing a noise power of the target user; and constructing the signal-to-interference-and-noise ratio of the target user according to all the base stations, all the non-target users, the first base station-user association matrix, the second base station-user association matrix, the conjugate transpose of the channel vector, the first beamforming vector, the second beamforming vector, and the noise power.

[0074] wherein the base station set and the user set are represented by sets and respectively, the base station set is composed of multiple base stations, K represents the number of base stations, the user set is composed of multiple target users, U represents the number of target users, k represents any one base station, u represents any one target user, the target user and the non-target user are both elements in the user set, but the target user and the non-target user are different elements, and m represents any one non-target user. and m is not equal to u.

[0075] wherein a binary variable is used to represent the base station-user association matrix, specifically, the first base station-user association matrix is represented by k,u , α k,u ∈{0,1}, if α k,u =1, it indicates that the target user is associated with the base station k, and if α k,u =0, it indicates that the target user is not associated with the base station k. Similarly, the second base station-user association matrix is represented by k,m , if α k,m =1, it indicates that the non-target user and m is not equal to u is associated with the base station k, and if α k,m =0, it indicates that the non-target user and m is not equal to u is not associated with the base station k.

[0076] wherein each base station includes Nt ≥1 transmitting antenna and N r ≥1 receiving antenna, wherein the number of transmitting antennas and receiving antennas can be the same, N t =N r Without making any specific restrictions, we will use N below. t =N r For example, such as Figure 10 The figure shows the experimental results of the relationship between detection probability and the number of base station antennas provided by an embodiment of the present invention. Indicates from base station To target users The channel vector, Indicates from base station To target users The conjugate transpose of the channel vector. Indicates base station Send to target user The first beamforming vector, using Indicates base station Send to non-target users The second beamforming vector. Using... Indicates target user Additive white Gaussian noise, Represents the noise power of the target user. (Using γ) u This indicates the signal-to-interference-plus-noise ratio (SIR / NNR) for the target user. SIR / NNR is the ratio of the signal to the sum of interference and noise in a system; it is an important indicator of system communication quality, and a higher SIR / NNR is better.

[0077] The signal-to-interference-plus-noise ratio (SIR) for the target user is:

[0078]

[0079] 202. Take the maximum sum of the signal-to-interference-plus-noise ratios of all the target users as the objective function.

[0080] For this embodiment, the objective function is:

[0081]

[0082] Where ω and α represent target variables, the target optimal solution in step 208 of the embodiment refers to the target optimal solution of the cooperative sensing variables. Specifically, ω represents the beamforming vector, and α represents the base station user association matrix, corresponding to the first beamforming vector w in step 201 of the embodiment. k,u Second beamforming vector w k,m The first base station user association matrix α k,u The second base station user association matrix α k,j .

[0083] 203. Obtain the minimum signal-to-interference-plus-noise ratio (SINR) of the target user, and construct an SINR constraint based on the SINR and the minimum SINR.

[0084] For this embodiment, using This represents the minimum signal-to-interference-plus-noise ratio (SINR), and the SINR constraint is:

[0085]

[0086] like Figure 11 As shown, a detection probability and minimum signal-to-interference-plus-noise ratio are illustrated. The results of the relationship experiment.

[0087] 204. Construct a coordinated base station cluster in the base station set that serves the target user based on multi-point cooperation, calculate the detection probability based on the coordinated base station cluster, and determine the minimum threshold value based on the detection probability.

[0088] Among them, serving the target user The coordinated base station cluster refers to all base stations, Δ k,u =1 base station, using Indicates target user The corresponding coordinated base station cluster.

[0089] In this embodiment, the step of calculating the detection probability based on the coordinated base station cluster includes: acquiring a first base station and a second base station in the coordinated base station cluster; calculating the target reflection signal from the second base station to the target user and then to the first base station; determining, based on the target reflection signal, a first cumulative signal and a second cumulative signal corresponding to each target user when the target exists and when the target does not exist; calculating, based on the first cumulative signal, a first probability density function and a second cumulative signal, a second probability density function, and calculating a likelihood ratio based on the first probability density function and the second probability density function; calculating, based on the likelihood ratio, a first distribution mean and a first distribution variance when the target exists, and a second distribution mean and a second distribution variance when the target does not exist; calculating, based on the first distribution mean and the first distribution variance, the presence detection probability when the target exists; calculating, based on the second distribution mean and the second distribution variance, the false alarm probability when the target does not exist; and calculating the detection probability based on the presence detection probability and the false alarm probability.

[0090] Here, s represents the first base station in the coordinated base station cluster, and i represents the second base station in the coordinated base station cluster. Both the first base station and the second base station are elements in the coordinated base station cluster, but the first base station and the second base station are different.

[0091] For the embodiment, the calculation of the target reflection signal from the second base station to the target user and to the first base station comprises: constructing a path loss from the first base station to the target user and to the second base station; constructing a first base station receiving steering vector and constructing a second base station transmitting steering vector; constructing a target reflection matrix from the second base station to the target user and to the first base station according to the path loss, radar cross section, the first base station receiving steering vector, the second base station transmitting steering vector; constructing a third beamforming vector sent by the second base station to the target user; multiplying the target reflection matrix and the third beamforming vector to obtain the target reflection signal from the second base station to the target user and to the first base station.

[0092] wherein, β s,i represents the path loss from the second base station to the target user and to the first base station, a r,s (θ s,u ) represents the first base station receiving steering vector, a t,i (θ i,u ) represents the second base station transmitting steering vector, ζ s,i represents the radar cross section, and represents the target reflection matrix from the second base station to the target user and to the first base station, w i,u represents the third beamforming vector sent by the second base station to the target user, and H s,i w i,u represents the target reflection signal.

[0093]

[0094]

[0095] d and λ respectively represent the antenna spacing and the signal wavelength, θ s,u represents the angle of the target user relative to the first base station, θ i,u represents the angle of the target user relative to the second base station, and j represents the imaginary unit.

[0096]

[0097] d s,u and d i,u respectively represent the distance of the target user u to the first base station s and the second base station i, d ref is the reference distance, and κ is the path loss at the reference distance d ref .

[0098] For the embodiment, when the target exists is determined according to the target reflection signal, the first cumulative signal of each target user is:

[0099] wherein, Ytargetrepresents the target presence, and the first accumulated signal of the target user comprises the sum of the noise and the accumulated signal accumulated by the target reflected signal, the target reflected signal is represented as: The accumulated signal of the target user u is composed of the serving base station cluster for the target user, and the accumulated signal accumulated by the target reflected signal is represented as:

[0100] The noise is represented in a vector form as

[0101] The first probability density function corresponding to the first accumulated signal is represented as:

[0102]

[0103] Similarly, according to the target reflected signal, the second accumulated signal of each of the target users is determined when the target presence is false:

[0104] wherein, Ytargetrepresents the target presence, and the first accumulated signal of the target user comprises the sum of the noise and the accumulated signal accumulated by the target reflected signal, the target reflected signal is represented as:

[0105] The noise is represented in a vector form as

[0106] The second probability density function corresponding to the second accumulated signal is represented as:

[0107]

[0108] For the embodiment, the likelihood ratio is calculated according to the first probability density function and the second probability density function, comprising: if is true, according to the maximum likelihood principle, there is The method of using the likelihood ratio test is represented as: u The likelihood ratio can be represented as:

[0109]

[0110] If is true, the λ u should be larger. Therefore, a threshold η u is set. If the λ u > η u , it is assumed that is true, otherwise it is assumed that is true.

[0111] ​​For this embodiment, the first distribution mean and the first distribution variance when the target exists, and the second distribution mean and the second distribution variance when the target does not exist are calculated according to the likelihood ratio, including: The likelihood ratio λ should be a constant value, so the likelihood ratio λ is simplified as λ' u u :

[0112]

[0113] Re represents a real part operation, and let The mean and variance of t' when the target exists and when the target does not exist are as follows: u

[0114]

[0115]

[0116] For this embodiment, the presence detection probability when the target exists is calculated according to the first distribution mean and the first distribution variance, the false alarm probability when the target does not exist is calculated according to the second distribution mean and the second distribution variance, and the detection probability is calculated according to the presence detection probability and the false alarm probability, including: let Then:

[0117]

[0118] Under the assumption that Then Under the assumption that Then According to the corresponding relationship between the Gaussian distribution and the complex Gaussian distribution, under the assumption that Then Under the assumption that Set λ u The corresponding threshold is η' u Therefore, when the target exists, the presence detection probability can be calculated as follows:

[0119]

[0120] The false alarm probability is defined as the probability of incorrectly detecting a signal when no target is present, and should be as small as possible. The false alarm probability can be calculated as follows:

[0121]

[0122] Substituting into the presence detection probability to obtain the detection probability:

[0123] ​​

[0124] Preferably, the correctness of the detection probability derivation needs to be verified, such as... Figure 7 The illustration shows the results of a Monte Carlo verification of the correctness of convergent collaborative sensing detection probability provided by an embodiment of the present invention. The verification uses the Monte Carlo method, which includes: conducting 10,000 simulation experiments to calculate the number of times a threshold is exceeded; dividing the number of times the threshold is exceeded by 10,000 to calculate the frequency of the simulation experiments satisfying the threshold; setting the false alarm probability and other parameters; using the result calculated using the derived detection probability formula as the formula calculation result; comparing the two results; if the difference is within a preset small interval, the derivation of the detection probability formula is considered correct; otherwise, the derivation is incorrect.

[0125] 205. Construct a perception performance constraint based on the minimum value of the threshold.

[0126] In this embodiment, constructing the perception performance constraint based on the minimum threshold includes: constructing a third beamforming vector transmitted from the second base station to the target user; and constructing the perception performance constraint based on the path loss, the transpose of the second base station's transmit steering vector, the third beamforming vector, and the minimum threshold.

[0127] Wherein, the minimum threshold is represented by t. u This means that δ in the detection probability is equal to a preset constant * t. u Therefore, t u It can be determined based on the detection probability. For example... Figure 13 As shown, experimental results illustrating the relationship between detection probability and minimum threshold are presented, where the horizontal axis represents t. u .

[0128] Among them, using The transpose of the transmission steering vector of the second base station is represented by w. i,u The third beamforming vector transmitted from the second base station to the target user is represented by β. s,i This represents the path loss from the first base station to the target user and then to the second base station, and the perception performance constraints.

[0129]

[0130] It should be noted that the construction process of the perception performance constraints in steps 204 and 205 of the embodiment is based on coordinating the first base station s and the second base station i in the base station cluster. In the target model, apart from the perception performance constraints (objective function, signal-to-interference-plus-noise ratio constraints, power constraints, and base station number constraints), the construction process is based on the base station k in the base station cluster.

[0131] 206. Obtain the maximum transmit power of the base station, and construct a power constraint based on the first base station user association matrix, the first beamforming vector, and the maximum transmit power.

[0132] For this embodiment, using The maximum transmit power of the base station is represented by α. k,u Let w represent the user association matrix of the first base station. k,u The first beamforming vector is represented by the power constraint:

[0133]

[0134] like Figure 9 As shown, the results of an experiment are presented showing the relationship between the sum of the signal-to-interference-plus-noise ratios (SIRs) of all target users and the maximum transmit power of the base station, where the horizontal axis represents t. u .

[0135] like Figure 12 As shown, the results of an experiment on the relationship between detection probability and the maximum transmit power of a base station are presented, where the horizontal axis represents t. u .

[0136] 207. Establish constraints on the number of base stations.

[0137] The constraint on the number of base stations includes: constructing the user association matrix of the first base station as 0 or 1.

[0138]

[0139] The constraint on the number of base stations to be constructed includes: the number of all base stations in the coordinated base station cluster that is greater than or equal to 1 and less than or equal to the number of base stations in the base station set.

[0140]

[0141] 208. Using the objective function, the signal-to-interference-plus-noise ratio constraint, the sensing performance constraint, the power constraint, and the base station number constraint as the objective model, iteratively solve the objective model to obtain the optimal objective solution.

[0142] For this embodiment, the target model is:

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] For the embodiment, the iterative solving of the target model to obtain the target optimal solution comprises: setting a preset iteration number, initial values of the first base station user association matrix, initial values of the second base station user association matrix, initial values of the first beamforming vector and initial values of the second beamforming vector; fixing the initial values of the first beamforming vector and the initial values of the second beamforming vector, and iteratively solving the optimal solution of the first base station user association matrix and the optimal solution of the second base station user association matrix by using a differential evolution algorithm; fixing the initial values of the first base station user association matrix and the initial values of the second base station user association matrix, and iteratively solving the optimal solution of the first beamforming vector and the optimal solution of the second beamforming vector by using fractional programming and semi-definite relaxation; and taking the optimal solution of the first base station user association matrix, the optimal solution of the second base station user association matrix, the optimal solution of the first beamforming vector and the optimal solution of the second beamforming vector as the target optimal solution.

[0150] Wherein, as Figure 8 The results of the convergence experiment of the target function are shown, Figure 8 CoMP in the table is Coordinated Multiple Points, SDR is semi-definite relaxation, DE is a differential evolution algorithm, NEU is an enumeration algorithm used for comparison with the differential evolution algorithm to prove the correctness of the differential evolution algorithm in solving the optimal solution of the first base station user association matrix and the optimal solution of the second base station user association matrix, i.e. the optimal solution of the first base station user association matrix and the optimal solution of the second base station user association matrix can be found, and ZF is a zero-forcing algorithm used for comparison with semi-definite relaxation to prove the correctness of semi-definite relaxation in solving the optimal solution of the first beamforming vector and the optimal solution of the second beamforming vector, i.e. the optimal solution of the first beamforming vector and the optimal solution of the second beamforming vector can be found.

[0151] Wherein, by fixing the initial values of the first beamforming vector and the initial values of the second beamforming vector, the first base station user association matrix and the second base station user association matrix are optimized and solved, and the target model is expressed as:

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158] To solve the optimal solution of the first base station-user association matrix and the optimal solution of the second base station-user association matrix, a differential evolution algorithm is used to solve, as shown in the following formula (1), the steps of the differential evolution algorithm. The differential evolution algorithm is a global optimization algorithm based on population. Compared with other algorithms, it can avoid falling into a local optimal solution while obtaining a global optimal solution, and is very suitable for solving optimization problems with a large number of discrete variables. The main steps of the differential evolution algorithm include initializing the population, mutation operation, crossover operation, calculating the value function, selection operation, and outputting the optimal individual. The differential evolution algorithm needs to set the population size, fitness function, mutation factor, crossover probability, and appropriate selection strategy. Figure 6

[0159] Population initialization is a very important step in the differential evolution algorithm. Common global initialization methods include random initialization, Latin hypercube sampling, global sampling, etc. As an implementation, the random initialization method is used to ensure the diversity of the population. Specifically, the population P is initialized with V individuals, each of which consists of KxU elements. The population can be represented as:

[0160] P = [P1, P2, ···, P v , ···, P V-1 , P V ]

[0161] P v is not only the vth individual in the population, but also a potential solution to the base station-user association matrix α. The base station-user association matrix α is a matrix composed of 0 and 1, i.e., its elements are integer values, so an integer-based encoding mechanism is designed. P v , the vth individual in the population, can be represented as an integer vector of size KxU, which can be represented as:

[0162]

[0163] represents whether the target user u is associated with the base station k. If is 1, it means that the target user u is associated with the base station k; if is 0, it means that the target user u is not associated with the base station k. For example, when the individual is represented as

[0164]

[0165] ​At this time, it means that the target user UE1 is served by the base stations BSI and BS3, while the target user UE2 is served by the base stations BSI and BS2, and the target user UE3 is served by the base stations BS2 and BS3.

[0166] According to the principle of the differential evolution algorithm, a fitness function is defined to evaluate each individual in the population. For discrete optimization problems, the fitness function can be the value of the objective function, or the sorting function and the exponential function of the value of the objective function. The sum of the signal-to-interference-and-noise ratios of all target users is defined as the fitness function, which can be expressed as:

[0167] The fitness function is defined as the sum of the signal-to-interference-and-noise ratios of all target users, which can be expressed as:

[0168]

[0169] P v is a potential solution of the base station-user association matrix a.

[0170] However, in order to obtain the sum of the signal-to-interference-and-noise ratios of all target users, it is necessary to obtain the beamforming vectors. Therefore, the initial values of the beamforming vectors (including the initial values of the first beamforming vectors and the initial values of the second beamforming vectors) are used, and the sum of the corresponding signal-to-interference-and-noise ratios of all target users is obtained. By using the fitness function, the joint optimization of the base station-user association matrix and the beamforming vectors can be realized. According to the fitness, suitable individuals are selected to participate in the population evolution process. After selecting individuals from the initial population, mutation and crossover operations are performed to produce new offspring. The purpose of mutation is to increase the diversity of individuals. Unlike traditional differential evolution algorithms, the differential evolution algorithm uses a vector difference strategy to realize individual mutation. In normal cases, the mutation operation of the differential evolution algorithm selects three different individuals X r1 , X r2 and X r3 in the current population, and then uses the difference between the three individuals to generate a mutation vector, which can be expressed as:

[0171] V v (g+1)=X r1 (g)+F(X r2 (g)-X r3 (g)),v≠r1≠r2≠r3,

[0172] F is a mutation factor used to control the size and direction of the mutation vector, usually F∈[0,1].

[0173] In the differential evolution algorithm, the crossover operation refers to the crossing of new vectors according to the original solution vectors to generate a new solution vector. This process involves the first generation population and its variants performing crossover operations. The purpose of selection operation is to maintain excellent individuals in the population and reduce the redundancy of solution vectors to reduce the impact of low-quality solutions, thereby improving the convergence speed and search efficiency of the differential evolution algorithm. Common selection operation methods include greedy selection, roulette wheel selection, tournament selection, elitist strategy, etc. As an implementation, the combination of greedy selection and elitist strategy is adopted. Greedy selection: competitive selection refers to pairing and comparing solution vectors in the population, and selecting solution vectors with higher fitness values as parents of the next generation population. Competitive selection does not require additional parameter settings and has better practicality and reliability. The elitist strategy makes the individual with the best fitness value in the population not participate in the selection operation, but is directly copied to the next generation population. This not only ensures that the fitness value of the population will not decrease, but also prevents the search process from falling into a local optimal solution too early. The elitist strategy can maintain the global optimal solution while maintaining the diversity of the population, thereby improving the search efficiency. At the same time, since the elitist strategy retains excellent solution vectors, the search process of the algorithm is more stable and the convergence speed is faster.

[0174] As an implementation, the population size is set to 50, i.e. there are 50 individuals in the population; the initial mutation factor is 0.4, and the crossover probability is set to 0.1; common selection operation methods include greedy selection, roulette selection, tournament selection, elitist strategy, etc. Here, the strategy of combining greedy selection and elitist strategy is adopted.

[0175] Among them, for solving the optimal solution of the first beam forming vector and the optimal solution of the second beam forming vector: since the base station number constraint is only related to the first base station user association matrix and the second base station user association matrix, and the initial value of the first base station user association matrix and the initial value of the second base station user association matrix are fixed, the base station number constraint in the target model can be removed when solving, and the target model is re-expressed as:

[0176]

[0177]

[0178]

[0179]

[0180] However, since the objective function is fractional, the signal-to-interference-and-noise ratio constraint and the perception performance constraint do not conform to the convex programming constraint form, and the above problem is still non-convex. Consider using quadratic transformation in fractional programming to convert the fractional form of the objective function into the form of the objective function in convex programming. Introduce auxiliary variable y u , The auxiliary variable is in the form of a fraction of one expression divided by another expression, which converts the objective function into a polynomial form, i.e., a sum or difference of one expression and another expression.

[0181] where the auxiliary variable y u refers to a signal received at the target user :

[0182]

[0183] The first base station user association matrix is denoted by a k,u , and the second base station user association matrix is denoted by a k,m , denotes the conjugate transpose of the channel vector from the base station to the target user u e u, denotes the conjugate transpose of the channel vector from the base station to the non-target user , denotes the first beamforming vector transmitted by the base station to the target user , denotes the second beamforming vector transmitted by the base station to the non-target user , k,u denotes the transmission signal transmitted by the base station to the target user , k,m denotes the transmission signal transmitted by the base station to the non-target user , denotes the additive white Gaussian noise at the target user, denotes the noise power.

[0184] The objective function can be re-expressed as:

[0185]

[0186] A u and B u are respectively expressed as:

[0187]

[0188]

[0189] denotes the conjugate transpose of the channel vector from the first base station to the target user , denotes the conjugate transpose of the channel vector from the third base station to the non-target user the conjugate transpose of the channel vector from the first base station to the non-target user , w the conjugate transpose of the channel vector from the first base station s,u to the target user , w the fourth beamforming vector sent by the first base station f,m to the non-target user , w the fifth beamforming vector sent by the third base station s,m to the non-target user , w the sixth beamforming vector sent by the first base station

[0190] The target model can be re-expressed as:

[0191]

[0192]

[0193]

[0194]

[0195] The above objective function is converted into a convex programming, but since the signal-to-interference-and-noise ratio constraint and the sensing performance constraint do not conform to the constraint form of the convex programming, the target model is still non-convex. Therefore, semi-definite relaxation is used. Semi-definite relaxation can solve the non-convex problem caused by the quadratic constraint. The non-convex problem with quadratic constraint is relaxed into a semi-definite programming, which satisfies the constraint function form of the convex programming.

[0196] Definition: W k,u = w k,u , w k,u ≥ 0, (rank represents rank), wherein, represents the result of the conjugate operation on a t,i (θ i,u ), represents the result of the transposition on a t,i (θ i,u ), and (a t,i (θ i,u ) represents the second base station transmit steering vector).

[0197]

[0198]

[0199]

[0200]

[0201]

[0202] β s,i denotes the path loss from the second base station to the target user to the first base station, W i,u denotes the third beamforming vector transmitted by the second base station to the target user, tr() denotes the trace, the first base station user association matrix is denoted by α k,u denotes, W k,u denotes the base station transmits to the target user the first beamforming vector, denotes the maximum transmit power of the base station, h f,m denotes the channel vector from the third base station to the non-target user , denotes the conjugate transpose of the channel vector from the third base station to the non-target user , f,m denotes the fifth beamforming vector transmitted by the third base station to the non-target user , s,m denotes the channel vector from the first base station to the non-target user , denotes the conjugate transpose of the channel vector from the first base station to the non-target user , s,m denotes the sixth beamforming vector transmitted by the first base station to the non-target user , denotes the minimum signal-to-noise ratio, h k,u denotes the channel vector from the base station to the target user , denotes the conjugate transpose of the channel vector from the base station to the target user u∈u.

[0203] Since

[0204]

[0205] is a rank 1 constraint, the existence of rank 1 causes the problem model to be a non-convex programming, so after removing the constraint, the problem changes from a non-convex problem to a semi-definite programming, removes the rank 1 constraint, and obtains an optimization problem that meets the convex programming Then it can be solved by a convex programming solver.* denotes the optimal solution of the optimization problem that fits the convex program (i.e., without the rank 1 constraint). However, ω * may not satisfy the rank 1 constraint, and thus may not be the optimal solution of the objective model described above that includes the rank 1 constraint. It is necessary to further perform eigenvalue decomposition on ω * to obtain the optimal solution of the objective model described above that includes the rank 1 constraint. The decomposition result can be specifically expressed as:

[0206]

[0207] Σ i denotes a diagonal matrix composed of eigenvalues, U i denotes a matrix composed of eigenvectors, denotes the conjugate transpose of the matrix composed of eigenvectors, and let wherein, denotes the square root of the values on the diagonal in the diagonal matrix composed of eigenvalues, and I represents a unit matrix, r i denotes a random vector subject to a complex Gaussian distribution, a plurality of r i and the value of the first beamforming vector that maximizes the objective function is selected as the optimal solution of the first beamforming vector, and the value of the second beamforming vector is selected as the optimal solution of the second beamforming vector.

[0208] The application provides a cooperative sensing method, device and equipment based on multi-point cooperation and integrated sensing. Firstly, a base station set and a user set are acquired, and for any target user in the user set, a signal-to-interference-and-noise ratio of the target user is constructed, and a maximum sum of the signal-to-interference-and-noise ratios of all the target users is taken as a target function. Then, a minimum value of the signal-to-interference-and-noise ratio of the target user is acquired, a signal-to-interference-and-noise ratio constraint is constructed according to the signal-to-interference-and-noise ratio and the minimum value, a coordinated base station cluster serving the target user in the base station set is constructed based on multi-point cooperation, a detection probability is calculated based on the coordinated base station cluster, a threshold minimum value is determined according to the detection probability, a sensing performance constraint is constructed according to the threshold minimum value, a power constraint is constructed, and a base station number constraint is constructed. Finally, the target function, the signal-to-interference-and-noise ratio constraint, the sensing performance constraint, the power constraint and the base station number constraint are taken as a target model, and the target model is iteratively solved to obtain a target optimal solution. By the technical scheme of the application, the maximum sum of the signal-to-interference-and-noise ratios is taken as the target function and the signal-to-interference-and-noise ratio constraint, the communication quality is ensured, the coordinated base station cluster serving the target user in the base station set is constructed based on multi-point cooperation, the detection probability is calculated based on the coordinated base station cluster, the sensing performance constraint is constructed based on the detection probability, and the sensing performance is ensured. Therefore, the target model constructed can meet both the communication quality and the sensing performance, and the problem that the sensing performance is poor due to the bias of meeting the communication quality and ignoring the sensing performance is solved.

[0209] Further, as a specific implementation of the method shown in Figure 1 and Figure 2 , the application provides a cooperative sensing device based on multi-point cooperation and integrated sensing, which, as shown in Figure 3 , comprises a first construction module 31, a second construction module 32 and a solving module 33.

[0210] The first construction module 31 is used for acquiring a base station set and a user set, constructing a signal-to-interference-and-noise ratio of any target user in the user set, and taking a maximum sum of the signal-to-interference-and-noise ratios of all the target users as a target function.

[0211] The second construction module 32 is used for acquiring a minimum value of the signal-to-interference-and-noise ratio of the target user, constructing a signal-to-interference-and-noise ratio constraint according to the signal-to-interference-and-noise ratio and the minimum value, constructing a coordinated base station cluster serving the target user in the base station set based on multi-point cooperation, calculating a detection probability based on the coordinated base station cluster, determining a threshold minimum value according to the detection probability, constructing a sensing performance constraint according to the threshold minimum value, constructing a power constraint and constructing a base station number constraint.

[0212] The solving module 33 is configured to take the target function, the SINR constraint, the perception performance constraint, the power constraint and the base station number constraint as a target model, and iteratively solve the target model to obtain a target optimal solution.

[0213] Correspondingly, in order to construct the SINR of the target user, the first constructing module 31 is specifically configured to obtain any one of the base stations in the base station set and any one of the users in the user set which is a non-target user; construct a first base station-user association matrix of the base station and the target user, construct a second base station-user association matrix of the base station and the non-target user, construct a conjugate transpose of a channel vector from the base station to the target user, construct a first beamforming vector of the base station sent to the target user, construct a second beamforming vector of the base station sent to the non-target user, and construct a noise power of the target user; and construct the SINR of the target user according to all the base stations, all the non-target users, the first base station-user association matrix, the second base station-user association matrix, the conjugate transpose of the channel vector, the first beamforming vector, the second beamforming vector and the noise power.

[0214] Correspondingly, in order to construct the base station number constraint, the second constructing module 32 is specifically configured to construct the first base station-user association matrix as 0 or 1.

[0215] Correspondingly, in order to calculate the detection probability based on the coordinated base station cluster, the second constructing module 32 is specifically configured to obtain a first base station and a second base station in the coordinated base station cluster; calculate a target reflected signal from the second base station to the target user and to the first base station; determine a first cumulative signal and a second cumulative signal corresponding to each of the target users according to the target reflected signal when the target exists and when the target does not exist, respectively; calculate a first probability density function corresponding to the first cumulative signal and a second probability density function corresponding to the second cumulative signal, respectively, calculate a likelihood ratio according to the first probability density function and the second probability density function; calculate a first distribution mean and a first distribution variance when the target exists and a second distribution mean and a second distribution variance when the target does not exist according to the likelihood ratio; calculate a presence detection probability when the target exists according to the first distribution mean and the first distribution variance, calculate a false alarm probability when the target does not exist according to the second distribution mean and the second distribution variance, and calculate a detection probability according to the presence detection probability and the false alarm probability.

[0216] Correspondingly, in order to calculate the target reflection signal from the second base station to the target user and to the first base station, the second constructing module 32 is specifically configured to construct a path loss from the first base station to the target user and to the second base station; construct a first base station receiving steering vector and a second base station transmitting steering vector; construct a target reflection matrix from the second base station to the target user and to the first base station according to the path loss, the radar cross section, the first base station receiving steering vector, and the second base station transmitting steering vector; and construct a third beamforming vector sent by the second base station to the target user; and multiply the target reflection matrix and the third beamforming vector to obtain the target reflection signal from the second base station to the target user and to the first base station.

[0217] Correspondingly, in order to construct the perception performance constraint according to the threshold minimum value, the second constructing module 32 is specifically configured to construct a third beamforming vector sent by the second base station to the target user; and construct a perception performance constraint according to the path loss, a transpose of the second base station transmitting steering vector, the third beamforming vector, and the threshold minimum value.

[0218] Correspondingly, in order to construct the base station number constraint, the second constructing module 32 is specifically configured to construct the coordinated base station cluster to be greater than or equal to 1 and less than or equal to a number of all base stations in the base station set.

[0219] Correspondingly, in order to construct the power constraint, the second constructing module 32 is specifically configured to obtain a maximum transmitting power of the base station, and construct a power constraint according to the first base station user association matrix, the first beamforming vector, and the maximum transmitting power.

[0220] Correspondingly, in order to iteratively solve the target model to obtain a target optimal solution, the solving module 33 includes a setting unit 331 and a solving unit 332.

[0221] The setting unit 331 is specifically configured to set a preset iteration number, an initial value of the first base station user association matrix, an initial value of the second base station user association matrix, an initial value of the first beamforming vector, and an initial value of the second beamforming vector.

[0222] The solution unit 332 can be specifically used to fix the initial values ​​of the first beamforming vector and the second beamforming vector, and use the differential evolution algorithm to iteratively solve for the optimal solutions of the first base station user association matrix and the second base station user association matrix; fix the initial values ​​of the first base station user association matrix and the second base station user association matrix, and use fractional programming and semidefinite relaxation to iteratively solve for the optimal solutions of the first beamforming vector and the second beamforming vector; and take the optimal solutions of the first base station user association matrix, the second base station user association matrix, the first beamforming vector, and the second beamforming vector as the target optimal solutions.

[0223] It should be noted that other corresponding descriptions of the functional units involved in the multi-point collaborative sensing device based on synesthetic integration provided in this embodiment can be found in [reference needed]. Figures 1-2 The corresponding description will not be repeated here.

[0224] Based on the above, Figures 1-2 Accordingly, this embodiment also provides a storage medium, which may be volatile or non-volatile, storing a computer program that, when executed by a processor, implements the above-described method. Figures 1-2 The illustrated collaborative sensing method is based on multi-point collaboration and integrated synesthesia.

[0225] Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present invention.

[0226] Based on the above, Figures 1-2 The method shown and Figure 3 , Figure 4 To achieve the above objectives, this embodiment also provides a computer device, which includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the above-described virtual device embodiment. Figures 1-2 The illustrated collaborative sensing method is based on multi-point collaboration and integrated synesthesia.

[0227] Optionally, the computer device can further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, and the like. The user interface can include a display screen, an input unit such as a keyboard, and the like. Optionally, the user interface can further include a USB interface, a card reader interface, and the like. The network interface can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and the like.

[0228] Those skilled in the art can understand that the computer device structure provided by the embodiment does not constitute a limitation on the entity device, and can include more or fewer components, or combine certain components, or different component arrangements.

[0229] The storage medium can further include an operating network communication module. The operation is a program for managing the hardware and software resources of the computer device, supporting the running of the information processing program and other software and / or programs. The network communication module is used for communication between the components in the storage medium, and communication with other hardware and software in the information processing entity device.

[0230] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware.

[0231] The application provides a cooperative sensing method, device and equipment based on multi-point cooperation, which comprises the following steps: firstly, a base station set and a user set are acquired; for any target user in the user set, a signal-to-interference-and-noise ratio (SINR) of the target user is constructed, and a sum of the SINRs of all the target users is taken as a target function; then, a minimum value of the SINR of the target user is acquired, a SINR constraint is constructed according to the SINR and the minimum value of the SINR, a coordinated base station cluster serving the target user in the base station set is constructed based on multi-point cooperation, a detection probability is calculated based on the coordinated base station cluster, a threshold minimum value is determined according to the detection probability, a sensing performance constraint is constructed according to the threshold minimum value, a power constraint is constructed, and a base station number constraint is constructed; finally, the target function, the SINR constraint, the sensing performance constraint, the power constraint and the base station number constraint are taken as a target model, the target model is iteratively solved, and a target optimal solution is obtained. Through the technical scheme of the application, the sum of the SINRs is taken as the target function and the SINR constraint, the communication quality is guaranteed, the coordinated base station cluster serving the target user in the base station set is constructed based on multi-point cooperation, the detection probability is calculated based on the coordinated base station cluster, the sensing performance constraint is constructed based on the detection probability, and the sensing performance is guaranteed, so that the target model constructed can meet both the communication quality and the sensing performance, and the problem that the sensing performance is poor due to the fact that the sensing performance is ignored while the communication quality is satisfied is solved.

[0232] Those skilled in the art can understand that the modules or flows in the drawings are not necessarily required for implementing the application. Those skilled in the art can understand that the modules in the devices in the implementation scenarios can be distributed in the devices in the implementation scenarios according to the description of the implementation scenarios, or can be changed and located in one or more devices different from the implementation scenarios. The modules in the above implementation scenarios can be combined into one module, or can be further split into a plurality of sub-modules.

[0233] The above serial numbers of the application are only for description, and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only some specific implementation scenarios of the application, but the application is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the application.

Claims

1. A collaborative sensing method based on multi-point collaboration and synesthetic integration, characterized in that, The method for a synesthetic system assisted by multi-point collaborative technology includes: Obtain the base station set and the user set. For any target user in the user set, construct the signal-to-interference-plus-noise ratio (SINR) of the target user. Maximize the sum of the SINR of all target users as the objective function. Obtain the minimum signal-to-interference-plus-noise ratio (SINR) of the target user, construct an SINR constraint based on the SINR and the minimum SINR, construct a coordinated base station cluster serving the target user in the base station set based on multi-point cooperation, calculate the detection probability based on the coordinated base station cluster, determine the minimum threshold based on the detection probability, construct a perception performance constraint, a power constraint, and a base station quantity constraint based on the minimum threshold. The objective function, the signal-to-interference-plus-noise ratio constraint, the sensing performance constraint, the power constraint, and the base station number constraint are used as the objective model. The objective model is iteratively solved to obtain the optimal objective solution. The calculation of the detection probability based on the coordinated base station cluster includes: Obtain the first base station and the second base station in the coordinated base station cluster; Calculate the target reflected signal from the second base station to the target user and then to the first base station; When the presence or absence of a target is determined based on the target reflection signal, the first and second accumulated signals corresponding to each target user are obtained. Calculate the first probability density function corresponding to the first accumulated signal and the second probability density function corresponding to the second accumulated signal respectively, and calculate the likelihood ratio based on the first probability density function and the second probability density function; The mean and variance of the first distribution when the target exists, and the mean and variance of the second distribution when the target does not exist, are calculated based on the likelihood ratio. The presence detection probability is calculated based on the first distribution mean and the first distribution variance; the false alarm probability is calculated based on the second distribution mean and the second distribution variance; and the detection probability is calculated based on the presence detection probability and the false alarm probability. The detection probability is ,in, It is the false alarm probability. It is the variance of the noise. = , This represents the number of transmitting antennas, where t is the number of transmitting antennas. It is the radar cross-section, and s is the first base station. It is the second base station. The target user, It is the coordinated base station cluster corresponding to the target user. It is the path loss from the second base station to the target user and then to the first base station. It is the conjugate transpose of the transmission steering vector of the second base station. It is the third beamforming vector sent by the second base station to the target user; The step of determining the minimum threshold value based on the detection probability includes: based on the detection probability in... Determine the minimum threshold value.

2. The method according to claim 1, characterized in that, The construction of the signal-to-interference-plus-noise ratio (SIR) for the target user includes: Obtain any one base station from the base station set and any one non-target user from the user set; Construct a first base station user association matrix between the base station and the target user, construct a second base station user association matrix between the base station and the non-target user, construct the conjugate transpose of the channel vector from the base station to the target user, construct a first beamforming vector sent by the base station to the target user, construct a second beamforming vector sent by the base station to the non-target user, and construct the noise power of the target user; The signal-to-interference-plus-noise ratio (SIR) of the target user is constructed based on all the base stations, all the non-target users, the first base station user association matrix, the second base station user association matrix, the conjugate transpose of the channel vector, the first beamforming vector, the second beamforming vector, and the noise power. The constraints on the number of base stations to be constructed include: The user association matrix of the first base station is constructed as 0 or 1.

3. The method according to claim 1, characterized in that, The calculation of the target reflected signal from the second base station to the target user and then back to the first base station includes: Construct the path loss from the first base station to the target user and then to the second base station; Construct the receiving guidance vector of the first base station and the transmitting guidance vector of the second base station; Based on the path loss, radar cross-section, first base station receiving steering vector, and second base station transmitting steering vector, a target reflection matrix is ​​constructed from the second base station to the target user and then to the first base station. Construct the third beamforming vector transmitted from the second base station to the target user; Multiplying the target reflection matrix by the third beamforming vector yields the target reflection signal from the second base station to the target user and then to the first base station.

4. The method according to claim 3, characterized in that, The step of constructing the perception performance constraint based on the minimum value of the threshold includes: Construct the third beamforming vector transmitted from the second base station to the target user; The sensing performance constraints are constructed based on the path loss, the transpose of the second base station transmit steering vector, the third beamforming vector, and the minimum threshold value. The constraints on the number of base stations to be constructed include: The number of base stations that constitute the coordinated base station cluster is greater than or equal to 1 and less than or equal to the number of base stations in the base station set.

5. The method according to claim 2, characterized in that, The power constraint for construction includes: Obtain the maximum transmit power of the base station, and construct a power constraint based on the first base station user association matrix, the first beamforming vector, and the maximum transmit power.

6. The method according to claim 2, characterized in that, The iterative solution of the target model to obtain the optimal solution includes: Set the preset number of iterations, the initial value of the first base station user association matrix, the initial value of the second base station user association matrix, the initial value of the first beamforming vector, and the initial value of the second beamforming vector; With the initial values ​​of the first beamforming vector and the second beamforming vector fixed, the optimal solutions of the first base station user association matrix and the second base station user association matrix are iteratively solved using the differential evolution algorithm. With the initial values ​​of the first base station user association matrix and the second base station user association matrix fixed, the optimal solutions of the first beamforming vector and the second beamforming vector are solved by fractional programming and semidefinite relaxation iteration. The optimal solution is determined by taking the optimal solution of the first base station user association matrix, the optimal solution of the second base station user association matrix, the optimal solution of the first beamforming vector, and the optimal solution of the second beamforming vector.

7. A collaborative sensing device based on multi-point cooperation and integrated synesthesia, characterized in that, The device includes: The first construction module is used to obtain a set of base stations and a set of users, construct the signal-to-interference-plus-noise ratio (SINR) of any target user in the user set, and take the maximum sum of the SINR of all target users as the objective function. The second construction module is used to obtain the minimum signal-to-interference-plus-noise ratio (SINR) of the target user, construct an SINR constraint based on the SINR and the minimum SINR, construct a coordinated base station cluster serving the target user in the base station set based on multi-point cooperation, calculate the detection probability based on the coordinated base station cluster, determine the minimum threshold based on the detection probability, construct a perception performance constraint, a power constraint, and a base station quantity constraint based on the minimum threshold. The solution module is used to take the objective function, the signal-to-interference-plus-noise ratio constraint, the sensing performance constraint, the power constraint, and the base station number constraint as the objective model, and iteratively solve the objective model to obtain the optimal solution. The second construction module is specifically used to acquire the first base station and the second base station in the coordinated base station cluster; calculate the target reflection signal from the second base station to the target user and then to the first base station; determine the first cumulative signal and the second cumulative signal corresponding to each target user when the target exists and when the target does not exist, respectively, based on the target reflection signal; calculate the first probability density function corresponding to the first cumulative signal and the second probability density function corresponding to the second cumulative signal, respectively, and calculate the likelihood ratio based on the first probability density function and the second probability density function; calculate the first distribution mean and the first distribution variance when the target exists, and the second distribution mean and the second distribution variance when the target does not exist, based on the likelihood ratio; calculate the presence detection probability when the target exists based on the first distribution mean and the first distribution variance; calculate the false alarm probability when the target does not exist based on the second distribution mean and the second distribution variance; and calculate the detection probability based on the presence detection probability and the false alarm probability. The detection probability is ,in, It is the false alarm probability. It is the variance of the noise. = , This represents the number of transmitting antennas, where t is the number of transmitting antennas. It is the radar cross-section, and s is the first base station. It is the second base station. The target user, It is the coordinated base station cluster corresponding to the target user. It is the path loss from the second base station to the target user and then to the first base station. It is the conjugate transpose of the transmission steering vector of the second base station. It is the third beamforming vector sent by the second base station to the target user; The second construction module is specifically used to determine the detection probability based on... Determine the minimum threshold value.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the collaborative sensing method based on multi-point cooperation and integrated synesthesia as described in any one of claims 1 to 6.

9. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the collaborative sensing method based on multi-point cooperation and integrated synesthesia as described in any one of claims 1 to 6.

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