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

Abstract

The invention discloses a communication and sensing integrated cooperative sensing method, device and equipment based on multi-point cooperation, is applied to a communication and sensing integrated system assisted by a multi-point cooperation technology, relates to the technical field of computer communication, and can solve the technical problem that the sensing performance is poor due to the fact that the sensing performance is ignored while the communication quality is met. Comprising the following steps: acquiring a base station set and a user set, for any target user in the user set, constructing a signal to interference plus noise ratio of the target user, and taking the maximum sum of the signal to interference plus noise ratios of all the target users as a target function; constructing a signal to interference plus noise ratio constraint, a sensing performance constraint, a power constraint and a base station number constraint according to the signal to interference plus noise ratio and the minimum value of the signal to interference plus noise ratio of the target user; and taking the target function, the signal to interference plus noise ratio constraint, the sensing performance constraint, the power constraint and the base station number constraint as a target model, and carrying out iterative solution on the target model to obtain a target optimal solution.

Description

Technical Field

[0001] The present invention relates to the field of computer communication technology, and in particular to a collaborative perception method, device and equipment based on multi-point collaboration. Background Art

[0002] The synaesthesia integration system aims to integrate the communication system and the radar system into one system to resolve the contradictions that arise during their development.

[0003] In communication, multi-point coordination technology enables multiple base stations to serve the same user, but in sensing, a single base station often senses a target. 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 has put forward higher requirements for communication quality.

[0004] Although more work has been done on the communication and perception of multi-base station coordinated service users, existing research mainly focuses on enhanced coordinated communication and single-base station perception or collaborative perception and single-base station communication, but is still insufficient in the dual collaboration of communication and perception. Therefore, there is an urgent need to study the integration of multi-base station cooperation and comprehensive perception. Summary of the invention

[0005] In view of this, the present invention provides a collaborative perception method, device and equipment based on multi-point collaboration, which can solve the technical problem of ignoring perception performance while focusing on meeting communication quality, resulting in poor perception performance.

[0006] According to a first aspect of the present invention, a collaborative perception method of synaesthesia based on multi-point collaboration is provided, which is applied to a synaesthesia system assisted by multi-point collaboration technology, and the method comprises:

[0007] Acquire a base station set and a user set, construct a signal to interference plus noise ratio of any target user in the user set, and take the maximum sum of the signal to interference plus noise ratios of all the target users as an objective function;

[0008] Acquire a minimum signal to interference plus noise ratio value of the target user, construct a signal to interference plus noise ratio constraint according to the signal to interference plus noise ratio and the minimum signal to interference plus noise ratio value, construct a coordinated base station cluster serving the target user in the base station set based on multi-point collaboration, calculate a detection probability based on the coordinated base station cluster, determine a minimum threshold value according to the detection probability, construct a perceived performance constraint according to the minimum threshold value, construct a power constraint, and construct a base station quantity constraint;

[0009] The objective function, the signal to interference and noise ratio constraint, the perception performance constraint, the power constraint and the base station quantity constraint are used as a target model, and the target model is iteratively solved to obtain the target optimal solution.

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

[0011] Acquire any one base station in the base station set and any one non-target user in the user set;

[0012] Constructing a first base station user association matrix between the base station and the target user, constructing a second base station user association matrix between 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 sent by the base station to the target user, constructing a second beamforming vector sent by the base station to the non-target user, and constructing a noise power of the target user;

[0013] Constructing a signal to interference plus 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 of the base station quantity constraint includes:

[0015] The first base station user association matrix is ​​constructed as 0 or 1.

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

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

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

[0019] Determining the presence of a target and the absence of a target according to the target reflection signal, respectively, a first accumulated signal and a second accumulated signal corresponding to each of the target users;

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

[0021] 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 according to the likelihood ratio;

[0022] The existence 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 existence detection probability and the false alarm probability.

[0023] Preferably, the calculating of the target reflected signal from the second base station to the target user and then to the first base station includes:

[0024] Constructing a path loss from the first base station to the target user and then to the second base station;

[0025] constructing a first base station receive steering vector and constructing a second base station transmit steering vector;

[0026] Construct a target reflection matrix from the second base station to the target user and then 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;

[0027] Constructing a third beamforming vector sent by the second base station to the target user;

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

[0029] Preferably, constructing the perceived performance constraint according to the minimum threshold value includes:

[0030] Constructing a third beamforming vector sent by the second base station to the target user;

[0031] constructing a perceived performance constraint based on the path loss, the transpose of the second base station transmit steering vector, the third beamforming vector, and the threshold minimum value;

[0032] The constructing of the base station quantity constraint includes:

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

[0034] Preferably, the constructing power constraint includes:

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

[0036] Preferably, the iteratively solving the target model to obtain the target optimal solution includes:

[0037] Setting a preset number of iterations, 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 an initial value of the first beamforming vector and an initial value of the second beamforming vector, and iteratively solving an optimal solution of the first base station user association matrix and an optimal solution of the second base station user association matrix by using a differential evolution algorithm;

[0039] Fixing an initial value of the first base station user association matrix and an initial value of the second base station user association matrix, and solving an optimal solution of the first beamforming vector and an optimal solution of the second beamforming vector by using fractional programming and semidefinite relaxation iteration;

[0040] An optimal solution of the first base station user association matrix, an optimal solution of the second base station user association matrix, an optimal solution of the first beamforming vector, and an optimal solution of the second beamforming vector are taken as target optimal solutions.

[0041] According to a second aspect of the present invention, a collaborative perception device based on multi-point collaboration is provided, the device comprising:

[0042] A first construction module is used to obtain a base station set and a user set, and for any target user in the user set, construct a signal to interference plus noise ratio of the target user, and take the maximum sum of the signal to interference plus noise ratios of all the target users as an objective function;

[0043] A second construction module is configured to obtain a minimum signal to interference plus noise ratio value of the target user, construct a signal to interference plus noise ratio constraint according to the signal to interference plus noise ratio and the minimum signal to interference plus noise ratio value, construct a coordinated base station cluster serving the target user in the base station set based on multi-point collaboration, calculate a detection probability based on the coordinated base station cluster, determine a minimum threshold value according to the detection probability, construct a perceived performance constraint according to the minimum threshold value, construct a power constraint, and construct a base station quantity constraint;

[0044] A solution module is used to take the objective function, the signal to interference and noise ratio constraint, the perception performance constraint, the power constraint and the base station quantity constraint as a target model, iteratively solve the target model, and obtain the target optimal solution.

[0045] According to a third aspect of the present application, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned collaborative perception method based on multi-point collaboration and synaesthesia is implemented.

[0046] According to the fourth aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the program, the above-mentioned collaborative perception method based on multi-point collaboration and synaesthesia is implemented.

[0047] By means of the above technical scheme, the present invention provides a collaborative perception method, device and equipment based on multi-point collaboration and synaesthesia. First, a base station set and a user set are obtained, and for any target user in the user set, a signal to interference and noise ratio of the target user is constructed, and the maximum sum of the signal to interference and noise ratios of all the target users is used as the objective function; then, the minimum signal to interference and noise ratio of the target user is obtained, and a signal to interference and noise ratio constraint is constructed according to the signal to interference and noise ratio and the minimum signal to interference and noise ratio. A coordinated base station cluster serving the target user in the base station set is constructed based on multi-point collaboration, a detection probability is calculated based on the coordinated base station cluster, a minimum threshold is determined according to the detection probability, and a perception performance constraint is constructed according to the minimum threshold, a power constraint is constructed, and a base station quantity constraint is constructed; finally, the objective function, the signal to interference and noise ratio constraint, the perception performance constraint, the power constraint and the base station quantity constraint are used as a target model, and the target model is iteratively solved to obtain the optimal solution of the target. Through the technical solution of the present invention, the maximum sum of signal to interference and noise ratio is used as the objective function and the signal to interference and noise ratio constraint to ensure the communication quality, and a coordinated base station cluster serving the target user in the base station set is constructed through multi-point collaboration. The detection probability is calculated based on the coordinated base station cluster, and the perception performance constraint is constructed based on the detection probability to ensure the perception performance. The target model constructed in this way can satisfy both the communication quality and the perception performance, and solves the problem of biased satisfaction of communication quality and neglect of perception performance leading to poor perception performance.

[0048] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0050] Figure 1 A schematic flow chart of a collaborative perception method based on multi-point collaboration and synaesthesia provided by an embodiment of the present invention is shown;

[0051] Figure 2A schematic flow chart of another collaborative perception method based on multi-point collaboration and synaesthesia provided by an embodiment of the present invention is shown;

[0052] Figure 3 A schematic diagram of the structure of a collaborative sensing device based on multi-point collaboration provided by an embodiment of the present invention is shown;

[0053] Figure 4 A schematic diagram of the structure of another collaborative sensing device based on multi-point collaboration provided by an embodiment of the present invention is shown;

[0054] Figure 5 A schematic diagram of a scenario of a synaesthesia system assisted by a multi-point collaboration technology provided by an embodiment of the present invention is shown;

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

[0056] Figure 7 The result of a Monte Carlo method for verifying the correctness of the probability of convergent cooperative sensing detection provided by an embodiment of the present invention is shown;

[0057] Figure 8 The result of a convergence experiment for verifying an objective function provided by an embodiment of the present invention is shown;

[0058] Fig. 9 The result of an experiment on the relationship between the sum of the signal to interference and noise ratios of all target users and the maximum transmission power of the base station provided by an embodiment of the present invention is shown;

[0059] Fig.10 The results of an experiment on the relationship between detection probability and the number of base station antennas provided by an embodiment of the present invention are shown;

[0060] Fig.11 The results of an experiment on the relationship between detection probability and minimum signal to interference noise ratio provided by an embodiment of the present invention are shown;

[0061] Fig.12 The results of an experiment on the relationship between the detection probability and the maximum transmission power of a base station provided by an embodiment of the present invention are shown;

[0062] Fig.13 The experimental results of the relationship between the detection probability and the minimum threshold value provided by an embodiment of the present invention are shown. DETAILED DESCRIPTION

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

[0064] This embodiment provides a collaborative perception method based on multi-point collaboration and synaesthesia integration, which is applied to a synaesthesia integration system assisted by multi-point collaboration technology, such as Figure 1 As shown, the method includes:

[0065] 101. Acquire a base station set and a user set, construct a signal to interference plus noise ratio of any target user in the user set, and take a maximum sum of the signal to interference plus noise ratios of all the target users as an objective function.

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

[0067] 103. Take the objective function, the signal to interference and noise ratio constraint, the perception performance constraint, the power constraint, and the base station quantity constraint as a target model, and iteratively solve the target model to obtain a target optimal solution.

[0068] Regarding the steps 101-103 of the embodiment, it should be noted that Figure 5 As shown in FIG. 1 , the multi-point coordinated technology-assisted interawareness system is composed of K base stations and U target users. The application of multi-point coordinated technology in the interawareness system means that multiple base stations are coordinated into a coordinated base station cluster to serve the same target user. Then the multi-point coordinated technology converts the co-channel interference into a useful superposition signal. On the one hand, the interference can be eliminated by coordinating the information in the shared link of multiple base stations to achieve the effect of improving the communication quality and ensure the communication performance of each target user. On the other hand, the coordinated base station cluster can perform cooperative perception to ensure the perception performance and realize the cooperative perception of multiple base stations. Specifically, all base stations send transmission messages to the target user at the same time under the control of the central controller. The target user can be deployed at any location in the cell where the two base stations are located. This method requires the channel state information and target user data information in the shared link between the base stations. Multiple base stations communicating with the same target user are coordinated into a cluster to realize multi-base station coordinated signal transmission, thereby converting traditional interference into a useful signal for the target user. The base station is both a provider of communication services and a receiver of perception signals. The target user is both a demander of communication services and a perception target.

[0069] Among them, the maximum sum of the signal to interference and noise ratio is used as the objective function and the signal to interference and noise ratio constraint is to ensure the 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 perception performance constraint is constructed based on the detection probability, so that the perception performance can be guaranteed; the power constraint and the base station number constraint are basic constraints for auxiliary solution, so compared with the prior art that only uses the signal to interference and noise ratio for related calculations, this embodiment also constructs the perception performance constraint through the detection probability, which can ensure the perception performance. 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 larger the detection probability, the better the base station perception performance. The minimum threshold value of the perception performance constraint is obtained based on the detection probability, so that the perception performance can be guaranteed to be at least greater than or equal to the minimum threshold value determined by the detection probability. The target optimal solution solved under such a perception performance constraint guarantees the perception performance.

[0070] The present invention provides a collaborative perception method, device and equipment based on multi-point collaboration. First, a base station set and a user set are obtained. For any target user in the user set, a signal to interference plus noise ratio of the target user is constructed, and the maximum sum of the signal to interference plus noise ratios of all the target users is used as an objective function; then, a minimum signal to interference plus noise ratio of the target user is obtained, a signal to interference plus noise ratio constraint is constructed according to the signal to interference plus noise ratio and the minimum signal to interference plus noise ratio, a coordinated base station cluster serving the target user in the base station set is constructed based on multi-point collaboration, a detection probability is calculated based on the coordinated base station cluster, a minimum threshold is determined according to the detection probability, a perception performance constraint is constructed according to the minimum threshold, a power constraint is constructed, and a base station quantity constraint is constructed; finally, the objective function, the signal to interference plus noise ratio constraint, the perception performance constraint, the power constraint and the base station quantity constraint are used as a target model, and the target model is iteratively solved to obtain the target optimal solution. Through the technical solution of the present invention, the maximum sum of signal to interference and noise ratio is used as the objective function and the signal to interference and noise ratio constraint to ensure the communication quality, and a coordinated base station cluster serving the target user in the base station set is constructed through multi-point collaboration. The detection probability is calculated based on the coordinated base station cluster, and the perception performance constraint is constructed based on the detection probability to ensure the perception performance. The target model constructed in this way can satisfy both the communication quality and the perception performance, and solves the problem of biased satisfaction of communication quality and neglect of perception performance leading to poor perception performance.

[0071] Further, as a refinement and expansion of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another collaborative perception method based on multi-point collaboration is provided, which is applied to the synaesthesia system assisted by multi-point collaboration technology, such as Figure 2 As shown, the method includes:

[0072] 201. Acquire a base station set and a user set, and construct a signal to interference and noise ratio of any target user in the user set.

[0073] For this embodiment, constructing the signal to interference plus noise ratio of the target user includes: 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 between the base station and the target user, constructing a second base station user association matrix between 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 sent by the base station to the target user, constructing a second beamforming vector sent by the base station to the non-target user, and constructing the noise power of the target user; constructing the signal to interference plus 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] Among them, the set and collection They represent the base station set and the user set respectively. The base station set is composed of multiple base stations, K represents the number of base stations, and the user set is composed of multiple target users. U represents the number of target users. k represents any base station, and u represents any target user. Both target users and non-target users are elements in the user set, but target users and non-target users are different elements. m represents any non-target user. And m is not equal to u.

[0075] Among them, the base station user association matrix is ​​represented by a binary variable. Specifically, the first base station user association matrix is ​​represented by α k,u Indicates that α k,u ∈{0,1}, if α k,u =1, it means the target user Associated with base station k, if α k,u =0, it means the target user is not associated with base station k. Similarly, the user association matrix of the second base station is represented by α k,m It means that if α k,m =1, it means non-target users and m is not equal to u, associated with base station k, if α k,m =0, it means non-target users And m is not equal to u and is not associated with base station k.

[0076] Each base station includes Nt ≥1 transmitting antenna and N r ≥ 1 receiving antenna, where the number of transmitting antennas and receiving antennas can be the same, N t =N r , no limitation is given here, and N t =N r For example, Fig.10 FIG. 1 shows the result of an experiment on the relationship between detection probability and the number of base station antennas provided by an embodiment of the present invention. From the base station To target users The channel vector, From the base station To target users The conjugate transpose of the channel vector . Indicates base station Send to target user The first beamforming vector of Indicates base station Send to non-target users The second beamforming vector of Indicates the target user Additive Gaussian white noise, Represents the noise power of the target user. u Indicates the signal-to-interference-to-noise ratio of the target user. The signal-to-interference-to-noise ratio refers to the ratio of the signal to the sum of interference and noise in the system. It is an important indicator for measuring the communication quality of the system and the larger the better.

[0077] Then the signal-to-interference-noise ratio of the target user is:

[0078]

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

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

[0081]

[0082] Wherein, ω, α represent target variables. The target optimal solution of step 208 of the embodiment refers to the target optimal solution of the cooperative sensing variable. 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 , the 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 a minimum signal to interference plus noise ratio value of the target user, and construct a signal to interference plus noise ratio constraint according to the signal to interference plus noise ratio and the minimum signal to interference plus noise ratio value.

[0084] For this embodiment, represents the minimum value of the signal to interference plus noise ratio. The signal to interference plus noise ratio constraint is:

[0085]

[0086] like Fig.11 As shown, a detection probability and signal to noise ratio minimum value are shown. Results of the relationship experiment.

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

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

[0089] For this embodiment, the calculation of the detection probability based on the coordinated base station cluster includes: obtaining the first base station and the 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 the first cumulative signal and the second cumulative signal corresponding to each target user when the target exists and the target does not exist according to the target reflection signal respectively; respectively calculating the first probability density function corresponding to the first cumulative signal and the second probability density function corresponding to the second cumulative signal, and calculating the likelihood ratio according to the first probability density function and the second probability density function; calculating 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 according to the likelihood ratio; calculating the existence detection probability when the target exists according to the first distribution mean and the first distribution variance, calculating the false alarm probability when the target does not exist according to the second distribution mean and the second distribution variance, and calculating the detection probability according to the existence 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, wherein the first base station and the second base station are both elements in the coordinated base station cluster, but the first base station is different from the second base station.

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

[0092] Among them, β s,i represents the path loss from the second base station to the target user and then to the first base station, with a r,s (θ s,u ) represents the first base station receiving the steering vector, and a t,i (θ i,u ) represents the second base station transmit steering vector, and ζ s,i Denotes the radar cross section, using The target reflection matrix from the second base station to the target user and then to the first base station is represented by w i,u represents the third beamforming vector sent by the second base station to the target user, H s,i w i,u Indicates the target reflection signal.

[0093]

[0094]

[0095] d and λ represent the antenna spacing and signal wavelength respectively, 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 an imaginary unit.

[0096]

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

[0098] For this embodiment, when it is determined that a target exists according to the target reflection signal, the first accumulated signal of each target user is:

[0099] Among them, Indicates that the target exists. When the target exists, the first cumulative signal of the target user includes the sum of noise and the cumulative signal accumulated by the target reflection signal. The target reflection signal is expressed as: The cumulative signal of the target user u is composed of the base station cluster serving it, and the cumulative signal accumulated by the target reflected signal is expressed as:

[0100] The noise is expressed in vector form as

[0101] The first probability density function corresponding to the first cumulative signal is expressed as:

[0102]

[0103] Similarly, when it is determined according to the target reflection signal that the target does not exist, the second accumulated signal of each target user is:

[0104] Among them, Indicates that the target does not exist. When the target does not exist, the second accumulated signal of the target user only includes noise.

[0105] The noise is expressed in vector form as

[0106] The second probability density function corresponding to the second cumulative signal is expressed as:

[0107]

[0108] For this embodiment, calculating the likelihood ratio according to the first probability density function and the second probability density function includes: if is true, according to the maximum likelihood principle, we have Using the likelihood ratio test method, with λ u To express the likelihood ratio, it can be expressed as:

[0109]

[0110] if is true, λ u Should be larger. So set the threshold η u If λ u >η u , then suppose True, otherwise assume 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: when the beam vector is known, should be a constant value, so the likelihood ratio λ u Simplified to λ′ u :

[0112]

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

[0114]

[0115]

[0116] For this embodiment, calculating the existence detection probability when the target exists according to the first distribution mean and the first distribution variance, calculating the false alarm probability when the target does not exist according to the second distribution mean and the second distribution variance, and calculating the detection probability according to the existence detection probability and the false alarm probability include: setting but:

[0117]

[0118] In the assumption Next, there is In the assumption Next, there is According to the corresponding relationship between Gaussian distribution and complex Gaussian distribution, for the assumption Then there is For the assumption Setting up the Lambda u The corresponding threshold is η′ u , therefore, when the target exists, the existence detection probability can be calculated as follows:

[0119]

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

[0121]

[0122] Will Substitute the existence detection probability Get the detection probability:

[0123]

[0124] Preferably, the correctness of the derivation of the detection probability needs to be verified, e.g. Figure 7 As shown, a result of a Monte Carlo verification of the correctness of the convergent collaborative sensing detection probability provided by an embodiment of the present invention is shown, and the Monte Carlo method is selected for verification, including: using 10,000 simulation experiments to calculate the number of times exceeding the threshold, and dividing the number of times exceeding the threshold by 10,000 to calculate the frequency of the simulation experiment meeting the threshold as the result of the simulation experiment; setting the false alarm probability and other parameters, and using the calculation result of the derived detection probability formula as the formula calculation result; comparing the above two results, if the difference is within a preset small interval, it is considered that the derivation of the detection probability formula is correct, otherwise, the derivation is incorrect.

[0125] 205. Construct a perceptual performance constraint according to the minimum threshold value.

[0126] For this embodiment, constructing the perceived performance constraint based on the minimum threshold value includes: constructing a third beamforming vector sent by the second base station to the target user; constructing the perceived performance constraint based on the path loss, the transpose of the second base station transmit steering vector, the third beamforming vector and the minimum threshold value.

[0127] The minimum threshold value is t u Indicates that the detection probability δ = preset constant * t u , therefore, t u It can be determined based on the probability of detection. Fig.13 As shown, an experimental result of the relationship between detection probability and minimum threshold value is shown, where the horizontal axis is t u .

[0128] Among them, Denote the transpose of the second base station's transmit steering vector, and use w i,u represents the third beamforming vector sent by the second base station to the target user, and β s,i represents the path loss from the first base station to the target user and then to the second base station, and the perceived performance constraint:

[0129]

[0130] It should be noted that the construction process of the perceived 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, and in the target model, except for the perceived performance constraints (objective function, signal to interference and noise ratio constraints, power constraints, and base station quantity constraints), the construction process is based on the base station k in the base station cluster.

[0131] 206. Obtain a maximum transmit 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 transmit power.

[0132] For this embodiment, Represents the maximum transmission power of the base station, with α k,u Denotes the user association matrix of the first base station, and w k,u represents the first beamforming vector, and the power constraint is expressed as:

[0133]

[0134] like Fig. 9 As shown, it shows the result of an experiment on the relationship between the sum of the signal to interference noise ratio of all target users and the maximum transmission power of the base station, where the horizontal axis is t u .

[0135] like Fig.12 As shown, it shows the result of an experiment on the relationship between the detection probability and the maximum transmission power of the base station, where the horizontal axis is t u .

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

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

[0138]

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

[0140]

[0141] 208. Take the objective function, the signal to interference and noise ratio constraint, the perception performance constraint, the power constraint, and the base station quantity constraint as a target model, and iteratively solve the target model to obtain a target optimal solution.

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

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] For this embodiment, the iterative solution of the target model to obtain the target optimal solution includes: setting a preset number of iterations, 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; 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 using a differential evolution algorithm; 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 using fractional programming and semidefinite 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] Among them, Figure 8 The result of a convergence experiment for verifying the objective function provided by an embodiment of the present invention is shown. Figure 8 CoMP in it stands for Coordinated Multiple Points, SDR is semidefinite relaxation, DE is 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, that is, 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 semidefinite relaxation to prove the correctness of the semidefinite relaxation in solving the optimal solution of the first beamforming vector and the optimal solution of the second beamforming vector, that is, the optimal solution of the first beamforming vector and the optimal solution of the second beamforming vector can be found.

[0151] Among them, by fixing the initial value of the first beamforming vector and the initial value 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] In order to find the optimal solution of the user association matrix of the first base station and the optimal solution of the user association matrix of the second base station, a differential evolution algorithm is used to solve it, such as Figure 6 The figure shows the steps of the differential evolution algorithm, which is a global optimization algorithm based on population. Compared with other algorithms, it can avoid falling into the local optimal solution while obtaining the 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, selecting 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.

[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 method, a 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 K×U elements, then the population can be expressed 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 of the base station user association matrix α. The base station user association matrix α is a matrix composed of 0 and 1, that is, its elements are integer values, so an integer-based encoding mechanism is designed. v can be represented as an integer vector of size K×U, which can be expressed as:

[0162]

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

[0164]

[0165] This means that the target user UE1 is coordinated by the base stations BS1 and BS3, while the base stations BS1 and BS2 coordinate the service of the target user UE2, and the target user UE3 is coordinated by the base stations BS2 and BS3.

[0166] According to the principle of 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 a ranking function and exponential function of the objective function value. The sum of the signal-to-noise ratios of all target users is calculated.

[0167] Defined as the fitness function, the fitness function can be expressed as:

[0168]

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

[0170] However, in order to obtain the sum of the signal to noise ratios of all target users, it is necessary to obtain the beamforming vector. Therefore, the initial value of the beamforming vector (including the initial value of the first beamforming vector and the initial value of the second beamforming vector) is used, and the corresponding sum of the signal to 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 vector can be achieved. 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 the traditional differential evolution algorithm, the differential evolution algorithm uses a vector difference strategy to achieve individual mutation. Under normal circumstances, the mutation operation of the differential evolution algorithm is to select three different individuals X in the current population. r1 , X r2 and X r3 , and then use the differences between these three individuals to generate a mutation vector. The calculation formula 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 the mutation factor, which is 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 crossover of the new vector according to the original solution vector to produce a new solution vector. This process involves the crossover operation of the first generation population and its variants. The purpose of the selection operation is to maintain the excellent individuals in the population and reduce the redundancy of the solution vector and the influence 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 selection, tournament selection, elite strategy, etc. As an implementation method, a combination of greedy selection and elite 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 the parents of the next generation population. Competitive selection does not require additional parameter settings and has better practicality and reliability. The elite strategy makes the individuals with the best fitness values ​​in the population not participate in the selection operation, but directly copy them 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 the local optimal solution too early. The elite strategy can maintain the global optimal solution while maintaining the diversity of the population to improve the search efficiency. At the same time, since the elite strategy retains excellent solution vectors, the algorithm's search process is more stable and converges faster.

[0174] As an implementation method, the population size is set to 50, that is, 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, elite strategy, etc., and a strategy combining greedy selection and elite strategy is adopted here.

[0175] Among them, for solving the optimal solution of the first beamforming vector and the optimal solution of the second beamforming vector: since the base station quantity 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 quantity constraint in the target model can be removed during the solution, and the target model is restated as:

[0176]

[0177]

[0178]

[0179]

[0180] However, since the objective function is a fraction, the signal-to-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 the quadratic transformation in fractional programming to transform the objective function in fractional form into the objective function form that conforms to the convex programming. Introduce the auxiliary variable y u , The auxiliary variable is a fractional form of one expression divided by another expression, and the objective function is converted into a polynomial form, that is, the sum or difference form of one expression and another expression.

[0181] Among them, the auxiliary variable y u The target user The signal received at:

[0182]

[0183] The first base station user association matrix is ​​α k,u The second base station user association matrix is ​​represented by α k,m express, Indicates that the slave base station The conjugate transpose of the channel vector to the target user u∈u, Indicates that the slave base station To non-target users The conjugate transpose of the channel vector, Indicates base station Send to target user The first beamforming vector of Indicates base station Send to non-target users The second beamforming vector, s k,u Indicates base station Send to target user The transmission signal, s k,m Indicates base station Send to non-target users The transmission signal, represents the additive white Gaussian noise at the target user, Represents the noise power.

[0184] The objective function can be restated as:

[0185]

[0186] A u and B u Respectively expressed as:

[0187]

[0188]

[0189] Indicates that from the first base station To target users The conjugate transpose of the channel vector, Indicates that from the third base station To non-target users The conjugate transpose of the channel vector, Indicates that from the first base station To non-target users The conjugate transpose of the channel vector, w s,u Indicates the first base station Send to target user The fourth beamforming vector, w f,m Indicates the third base station Send to non-target users The fifth beamforming vector, w s,m Indicates the first base station Send to non-target users The sixth beamforming vector of .

[0190] Then the target model can be restated as:

[0191]

[0192]

[0193]

[0194]

[0195] The above objective function is converted into convex programming. However, since the signal-to-interference-to-noise ratio constraint and the perception performance constraint do not conform to the convex programming constraint form, the target model is still non-convex. Therefore, semidefinite relaxation is used. Semidefinite relaxation can solve the non-convex situation due to quadratic constraints. The non-convex problem with quadratic constraints is relaxed into a semidefinite programming, which satisfies the constraint function form of convex programming.

[0196] Definition: W k,u =w k,u 、w k,u ≥0, (rank means rank), in, Indicates a t,i (θ i,u ) is the result of the conjugate operation. Indicates a t,i (θ i,u ) is the result after transposing, (a t,i (θ i,u ) represents the second base station transmits the steering vector).

[0197]

[0198]

[0199]

[0200]

[0201]

[0202] β s,i represents the path loss from the second base station to the target user and then to the first base station, W i,u represents the third beamforming vector sent by the second base station to the target user, tr() represents the trace, and the user association matrix of the first base station is α k,u Indicates that W k,u Indicates base station Send to target user The first beamforming vector of Indicates the maximum transmission power of the base station, h f,m Indicates that from the third base station To non-target users The channel vector, Indicates that from the third base station To non-target users The conjugate transpose of the channel vector, W f,m Indicates the third base station Send to non-target users The fifth beamforming vector, h s,m Indicates that from the first base station To non-target users The channel vector, Indicates that from the first base station To non-target users The conjugate transpose of the channel vector, W s,m Indicates the first base station Send to non-target users The sixth beamforming vector of represents the minimum signal-to-interference-noise ratio, h k,u From the base station To target users The channel vector, From the base station The conjugate transpose of the channel vector to the target user u∈u.

[0203] because

[0204]

[0205] is a rank 1 constraint. The existence of rank 1 causes the problem model to not be a convex programming. Therefore, after removing this constraint, the problem is transformed from a non-convex problem to a semidefinite programming. Removing the rank 1 constraint, we get an optimization problem that conforms to the convex programming and can then be solved by the convex programming solver.* represents the optimal solution to the optimization problem that conforms to convex programming (that is, removing the rank 1 constraint). However, ω * It may not satisfy the rank 1 constraint, so it may not be the optimal solution of the target model including the rank 1 constraint. It is necessary to use the Gaussian randomization method to * Perform eigenvalue decomposition to obtain the optimal solution of the target model including the rank 1 constraint. The decomposition result can be specifically expressed as:

[0206]

[0207] Σ i represents the diagonal matrix composed of eigenvalues, U i represents the matrix composed of eigenvectors, Denotes the conjugate transpose of the matrix composed of eigenvectors, let in, In the diagonal matrix composed of eigenvalues, the values ​​on the diagonal are taken as square roots. And I represents the identity matrix, r i Represents a random vector that follows a complex Gaussian distribution, randomly generating multiple r i 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 present invention provides a collaborative perception method, device and equipment based on multi-point collaboration. First, a base station set and a user set are obtained. For any target user in the user set, a signal to interference plus noise ratio of the target user is constructed, and the maximum sum of the signal to interference plus noise ratios of all the target users is used as an objective function; then, a minimum signal to interference plus noise ratio of the target user is obtained, a signal to interference plus noise ratio constraint is constructed according to the signal to interference plus noise ratio and the minimum signal to interference plus noise ratio, a coordinated base station cluster serving the target user in the base station set is constructed based on multi-point collaboration, a detection probability is calculated based on the coordinated base station cluster, a minimum threshold is determined according to the detection probability, a perception performance constraint is constructed according to the minimum threshold, a power constraint is constructed, and a base station quantity constraint is constructed; finally, the objective function, the signal to interference plus noise ratio constraint, the perception performance constraint, the power constraint and the base station quantity constraint are used as a target model, and the target model is iteratively solved to obtain the target optimal solution. Through the technical solution of the present invention, the maximum sum of signal to interference and noise ratio is used as the objective function and the signal to interference and noise ratio constraint to ensure the communication quality, and a coordinated base station cluster serving the target user in the base station set is constructed through multi-point collaboration. The detection probability is calculated based on the coordinated base station cluster, and the perception performance constraint is constructed based on the detection probability to ensure the perception performance. The target model constructed in this way can satisfy both the communication quality and the perception performance, and solves the problem of biased satisfaction of communication quality and neglect of perception performance leading to poor perception performance.

[0209] Further, as Figure 1 and Figure 2 The specific implementation of the method shown in the figure, the embodiment of the present invention provides a collaborative perception device based on multi-point collaboration, such as Figure 3 As shown, the device includes: a first building module 31, a second building module 32, and a solving module 33;

[0210] A first construction module 31 is used to obtain a base station set and a user set, and for any target user in the user set, construct a signal to interference plus noise ratio of the target user, and take the maximum sum of the signal to interference plus noise ratios of all the target users as an objective function;

[0211] A second construction module 32 is configured to obtain a minimum signal to interference plus noise ratio (SINR) value of the target user, construct a SINR constraint according to the SINR and the minimum SINR value, construct a coordinated base station cluster serving the target user in the base station set based on multi-point collaboration, calculate a detection probability based on the coordinated base station cluster, determine a minimum threshold value according to the detection probability, construct a perceived performance constraint according to the minimum threshold value, construct a power constraint, and construct a base station quantity constraint;

[0212] The solving module 33 is used to take the objective function, the signal to interference and noise ratio constraint, the perception performance constraint, the power constraint and the base station quantity constraint as a target model, iteratively solve the target model, and obtain the target optimal solution.

[0213] Correspondingly, in order to construct the signal to interference plus noise ratio of the target user, the first construction module 31 can be specifically used to obtain any base station in the base station set and any non-target user in 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 a 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; construct the signal to interference plus 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.

[0214] Correspondingly, in order to construct the base station quantity constraint, the second construction module 32 can be specifically used 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 construction module 32 can be specifically used to obtain 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 the target does not exist according to 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, and calculate the likelihood ratio according to 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 according to the likelihood ratio; calculate the existence detection probability when the target exists according to the first distribution mean and the first distribution variance, calculate the false alarm probability when the target does not exist according to the second distribution mean and the second distribution variance, and calculate the detection probability according to the existence 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 then to the first base station, the second construction module 32 can be specifically used to construct the path loss from the first base station to the target user and then to the second base station; construct the first base station receiving steering vector and construct the second base station transmitting steering vector; construct the target reflection matrix from the second base station to the target user and then to the first base station according to the path loss, radar cross section, the first base station receiving steering vector, and the second base station transmitting steering vector; construct a third beamforming vector sent by the second base station to the target user; multiply the target reflection matrix by the third beamforming vector to obtain the target reflection signal from the second base station to the target user and then to the first base station.

[0217] Correspondingly, in order to construct a perceived performance constraint based on the minimum threshold value, the second construction module 32 can be specifically used to construct a third beamforming vector sent by the second base station to the target user; and construct a perceived performance constraint based on the path loss, the transpose of the second base station transmit steering vector, the third beamforming vector and the minimum threshold value.

[0218] Accordingly, in order to construct the base station quantity constraint, the second constructing module 32 can be specifically used to construct the coordinated base station cluster with the quantity of all the base stations being greater than or equal to 1 and less than or equal to the number of the base stations in the base station set.

[0219] Correspondingly, in order to construct a power constraint, the second construction module 32 can be specifically used to obtain the maximum transmit power of the base station, and construct the power constraint according to the first base station user association matrix, the first beamforming vector and the maximum transmit power.

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

[0221] The setting unit 331 may be specifically configured to set a preset number of iterations, 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 solving unit 332 can be specifically used to fix the initial value of the first beamforming vector and the initial value of the second beamforming vector, and use the differential evolution algorithm to iteratively solve the optimal solution of the first base station user association matrix and the optimal solution of the second base station user association matrix; fix the initial value of the first base station user association matrix and the initial value of the second base station user association matrix, and use fractional programming and semidefinite relaxation to iteratively solve the optimal solution of the first beamforming vector and the optimal solution of the second beamforming vector; use 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.

[0223] It should be noted that for other corresponding descriptions of the functional units involved in the multi-point collaborative synaesthesia integrated collaborative sensing device provided in this embodiment, please refer to Figure 1 to Figure 2 The corresponding description will not be repeated here.

[0224] Based on the above Figure 1 to Figure 2 The method shown in the embodiment, accordingly, also provides a storage medium, which can be volatile or non-volatile, and stores a computer program, which is executed by a processor to implement the above-mentioned Figure 1 to Figure 2 The collaborative perception method based on multi-point collaboration and synaesthesia is shown.

[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 (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various implementation scenarios of the present invention.

[0226] Based on the above Figure 1 to Figure 2 The method shown and Figure 3 , Figure 4 In order to achieve the above-mentioned purpose, the present embodiment further 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 achieve the above-mentioned Figure 1 to Figure 2 The collaborative perception method based on multi-point collaboration and synaesthesia is shown.

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

[0228] Those skilled in the art will appreciate that the computer device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0229] The storage medium may also include an operating network communication module. The operating is a program that manages the hardware and software resources of the above-mentioned computer device, supporting the operation of the information processing program and other software and / or programs. The network communication module is used to communicate between the components inside the storage medium, and to communicate with other hardware and software in the information processing entity device.

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

[0231] The present invention provides a collaborative perception method, device and equipment based on multi-point collaboration. First, a base station set and a user set are obtained. For any target user in the user set, a signal to interference plus noise ratio of the target user is constructed, and the maximum sum of the signal to interference plus noise ratios of all the target users is used as an objective function; then, a minimum signal to interference plus noise ratio of the target user is obtained, a signal to interference plus noise ratio constraint is constructed according to the signal to interference plus noise ratio and the minimum signal to interference plus noise ratio, a coordinated base station cluster serving the target user in the base station set is constructed based on multi-point collaboration, a detection probability is calculated based on the coordinated base station cluster, a minimum threshold is determined according to the detection probability, a perception performance constraint is constructed according to the minimum threshold, a power constraint is constructed, and a base station quantity constraint is constructed; finally, the objective function, the signal to interference plus noise ratio constraint, the perception performance constraint, the power constraint and the base station quantity constraint are used as a target model, and the target model is iteratively solved to obtain the target optimal solution. Through the technical solution of the present invention, the maximum sum of signal to interference and noise ratio is used as the objective function and the signal to interference and noise ratio constraint to ensure the communication quality, and a coordinated base station cluster serving the target user in the base station set is constructed through multi-point collaboration. The detection probability is calculated based on the coordinated base station cluster, and the perception performance constraint is constructed based on the detection probability to ensure the perception performance. The target model constructed in this way can satisfy both the communication quality and the perception performance, and solves the problem of biased satisfaction of communication quality and neglect of perception performance leading to poor perception performance.

[0232] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present invention. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0233] The above serial numbers of the present invention are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of the present invention, but the present invention 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 present invention.

Claims

1. A collaborative perception method based on multi-point collaboration, characterized in that: A synaesthesia system assisted by multi-point collaboration technology is applied, and the method includes: Acquire a base station set and a user set, construct a signal to interference plus noise ratio of any target user in the user set, and take the maximum sum of the signal to interference plus noise ratios of all the target users as an objective function; Acquire a minimum signal to interference plus noise ratio value of the target user, construct a signal to interference plus noise ratio constraint according to the signal to interference plus noise ratio and the minimum signal to interference plus noise ratio value, construct a coordinated base station cluster serving the target user in the base station set based on multi-point collaboration, calculate a detection probability based on the coordinated base station cluster, determine a minimum threshold value according to the detection probability, construct a perceived performance constraint according to the minimum threshold value, construct a power constraint, and construct a base station quantity constraint; The objective function, the signal to interference and noise ratio constraint, the perception performance constraint, the power constraint and the base station quantity constraint are used as a target model, and the target model is iteratively solved to obtain the target optimal solution.

2. The method according to claim 1, characterized in that The constructing the signal to interference and noise ratio of the target user includes: Acquire 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 between the base station and the target user, constructing a second base station user association matrix between 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 sent by the base station to the target user, constructing a second beamforming vector sent by the base station to the non-target user, and constructing a noise power of the target user; Constructing a signal to interference plus 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; The constructing of the base station quantity constraint includes: The first base station user association matrix is ​​constructed as 0 or 1.

3. The method according to claim 1, characterized in that The calculating the detection probability based on the coordinated base station cluster includes: Acquire 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 then to the first base station; Determining the presence of a target and the absence of a target according to the target reflection signal, respectively, a first accumulated signal and a second accumulated signal corresponding to each of the target users; Respectively calculating a first probability density function corresponding to the first accumulated signal and a second probability density function corresponding to the second accumulated signal, and calculating a likelihood ratio according to 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 according to the likelihood ratio; The existence 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 existence detection probability and the false alarm probability.

4. The method according to claim 3, characterized in that The calculating of the target reflected signal from the second base station to the target user and then to the first base station includes: Constructing a path loss from the first base station to the target user and then to the second base station; constructing a first base station receive steering vector and constructing a second base station transmit steering vector; Construct a target reflection matrix from the second base station to the target user and then 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; Constructing a third beamforming vector sent by the second base station to the target user; The target reflection matrix is ​​multiplied by the third beamforming vector to obtain a target reflection signal from the second base station to the target user and then to the first base station.

5. The method according to claim 4, characterized in that The step of constructing a perceptual performance constraint according to the minimum threshold value includes: Constructing a third beamforming vector sent by the second base station to the target user; constructing a perceived performance constraint based on the path loss, the transpose of the second base station transmit steering vector, the third beamforming vector, and the threshold minimum value; The constructing of the base station quantity constraint includes: The number of all the base stations in the base station set that is constructed to coordinate the base station cluster is greater than or equal to 1 and less than or equal to the number of all the base stations in the base station set.

6. The method according to claim 2, characterized in that The constructing power constraint comprises: The maximum transmit power of the base station is obtained, and a power constraint is established according to the first base station user association matrix, the first beamforming vector, and the maximum transmit power.

7. The method according to claim 2, characterized in that The iteratively solving the target model to obtain the target optimal solution includes: Setting a preset number of iterations, 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; Fixing an initial value of the first beamforming vector and an initial value of the second beamforming vector, and iteratively solving an optimal solution of the first base station user association matrix and an optimal solution of the second base station user association matrix by using a differential evolution algorithm; Fixing an initial value of the first base station user association matrix and an initial value of the second base station user association matrix, and solving an optimal solution of the first beamforming vector and an optimal solution of the second beamforming vector by using fractional programming and semidefinite relaxation iteration; An optimal solution of the first base station user association matrix, an optimal solution of the second base station user association matrix, an optimal solution of the first beamforming vector, and an optimal solution of the second beamforming vector are taken as target optimal solutions.

8. A collaborative sensing device based on multi-point collaboration, characterized in that: The device comprises: A first construction module is used to obtain a base station set and a user set, and for any target user in the user set, construct a signal to interference plus noise ratio of the target user, and take the maximum sum of the signal to interference plus noise ratios of all the target users as an objective function; A second construction module is configured to obtain a minimum signal to interference plus noise ratio value of the target user, construct a signal to interference plus noise ratio constraint according to the signal to interference plus noise ratio and the minimum signal to interference plus noise ratio value, construct a coordinated base station cluster serving the target user in the base station set based on multi-point collaboration, calculate a detection probability based on the coordinated base station cluster, determine a minimum threshold value according to the detection probability, construct a perceived performance constraint according to the minimum threshold value, construct a power constraint, and construct a base station quantity constraint; A solution module is used to take the objective function, the signal to interference and noise ratio constraint, the perception performance constraint, the power constraint and the base station quantity constraint as a target model, iteratively solve the target model, and obtain the target optimal solution.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the collaborative perception method based on multi-point collaboration and synaesthesia as claimed in any one of claims 1 to 7 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the collaborative perception method based on multi-point collaboration and synaesthesia as claimed in any one of claims 1 to 7 is implemented.

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