Performance optimization method and apparatus, electronic device, and storage medium

Abstract

Embodiments of the present application disclose a performance optimization method and device, electronic equipment and storage medium. The method comprises: establishing an optimization problem with a sensing parameter as a target function and a communication parameter and a precoding matrix as constraint conditions; the communication parameter comprises a parameter of a communication receiving end receiving the communication sensing signal, and the sensing parameter comprises a parameter obtained according to a probe echo corresponding to the communication sensing signal; in the case that the communication parameter satisfies a first constraint condition and the precoding matrix satisfies a second constraint condition, an optimal solution of the target function is obtained; and based on the optimal solution of the target function, a target precoding matrix and a target communication parameter are obtained to realize performance optimization of a communication sensing system. The technical scheme of the embodiments of the present application realizes performance optimization of the communication sensing system.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more particularly to a performance optimization method, apparatus, electronic device, and storage medium. Background Technology

[0002] Future mobile communication systems, such as B5G or 6G systems, will possess sensing capabilities in addition to communication capabilities. Sensing capabilities refer to the ability of one or more devices to sense information such as the location, distance, and speed of target objects through the transmission and reception of wireless signals, or to detect, track, identify, and image target objects, events, or environments. Traditional communication sensing systems treat inter-user interference entirely as noise, limiting performance improvements. Rate Splitting Multiple Access (RSMA) technology, through the splitting of user messages and the non-orthogonal transmission of common messages decoded by multiple users and private messages decoded by corresponding users, achieves two extreme interference management strategies: soft bridging, harmonizing fully decoded interference, and treating interference as noise.

[0003] Applying RSMA to communication sensing systems allows for flexible management of interference; however, current research is still in its early stages, and RSMA-based communication sensing systems exhibit poor communication and sensing performance. Improving the performance of communication sensing systems is currently the primary challenge. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a performance optimization method, apparatus, electronic device, and storage medium, which optimize the performance of integrated sensing and communication.

[0005] In a first aspect, embodiments of the present invention provide a performance optimization method, the method comprising:

[0006] An optimization problem is established with sensing parameters as the objective function and communication parameters and a precoding matrix as constraints; the communication parameters include parameters of the communication receiver that receives the communication sensing signal, and the sensing parameters include parameters obtained based on the detection echo corresponding to the communication sensing signal;

[0007] The optimal solution of the objective function is obtained when the communication parameters satisfy the first constraint and the precoding matrix satisfies the second constraint.

[0008] Based on the optimal solution of the objective function, the target precoding matrix and target communication parameters are obtained to optimize the performance of the communication sensing system.

[0009] Secondly, embodiments of the present invention provide a performance optimization device, the device comprising:

[0010] The problem establishment module is used to establish an optimization problem with sensing parameters as the objective function and communication parameters and a precoding matrix as constraints; the communication parameters include parameters of the communication receiver that receives the communication sensing signal, and the sensing parameters include parameters obtained based on the detection echo corresponding to the communication sensing signal;

[0011] The optimal solution acquisition module is used to obtain the optimal solution of the objective function when the communication parameters satisfy the first constraint condition and the precoding matrix satisfies the second constraint condition.

[0012] The performance optimization module is used to obtain the target precoding matrix and target communication parameters based on the optimal solution of the objective function, so as to achieve performance optimization of the communication sensing system.

[0013] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising:

[0014] One or more processors;

[0015] Storage device for storing one or more programs.

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the performance optimization method as described in any of the embodiments of the present invention.

[0017] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, characterized in that, when the program is executed by a processor, it implements the performance optimization method as described in any of the embodiments of the present invention.

[0018] The technical solution of this invention establishes an optimization problem with sensing parameters as the objective function and communication parameters and a precoding matrix as constraints. Under the condition that the communication parameters satisfy a first constraint and the precoding matrix satisfies a second constraint, the optimal solution of the objective function is obtained. Based on the optimal solution of the objective function, the target precoding matrix and target power parameters are obtained, thereby optimizing the performance of the communication sensing system. This technical solution achieves the acquisition of the optimal target precoding matrix and target communication parameters, and then transmits communication sensing signals based on these parameters, improving the accuracy of communication sensing signal transmission and enhancing the performance of the communication sensing system. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] in:

[0021] Figure 1 This is a flowchart illustrating a performance optimization method in one embodiment;

[0022] Figure 2 This is a schematic diagram of the structure of a performance optimization device in one embodiment;

[0023] Figure 3 This is a schematic diagram of the structure of an electronic device in one embodiment. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Before describing the technical solutions of the embodiments of the present invention, the application scenarios of the embodiments of the present invention will be illustrated by example:

[0026] Future 6G systems are expected to integrate wireless transmission, sensing capabilities, and computing functions, supporting a wider range of new applications such as automated manufacturing, intelligent transportation, ubiquitous communication, and haptic communication. These applications require the combined capabilities of wireless sensing and data transmission, leading more researchers to explore integrated communication and sensing systems. Furthermore, the integration of wireless sensing and communication systems can reduce hardware costs, share the same spectrum, and alleviate the increasingly severe problem of spectrum resource scarcity.

[0027] Current research can be broadly categorized into three types: design centered on the sensing system, design centered on the communication system, and design of integrated communication-sensing systems. Among these, the design of communication-sensing systems (or integrated communication-sensing systems) is increasingly becoming a research hotspot. Especially with the development of Multiple-Input Multiple-Output (MIMO) technology in radar and communication fields, integrated communication-sensing systems based on multi-antenna technology can achieve better joint performance compared to traditional single-antenna systems. Most existing integrated communication-sensing systems under the MIMO architecture employ Space Division Multiple Access (SDMA) technology, resulting in relatively poor performance. This invention provides a performance optimization method. By establishing an optimization problem with sensing parameters as the objective function and communication parameters and a precoding matrix as preset conditions, the optimal solution is obtained. Based on the optimal solution, the target precoding matrix and target communication parameters are obtained to achieve performance optimization of the communication-sensing system.

[0028] In one embodiment of the present invention, a performance optimization method is provided. The method of the present invention is applicable to the situation of optimizing the performance of a communication sensing system. The method can be executed by a performance optimization device, which can be implemented in software and / or hardware.

[0029] like Figure 1 As shown, the performance optimization method of this invention specifically includes the following steps:

[0030] S110. Establish an optimization problem with sensing parameters as the objective function and communication parameters and precoding matrix as constraints.

[0031] The communication parameters include parameters of the communication receiving end that receives the communication sensing signal. These sensing parameters include parameters obtained based on the detection echo corresponding to the communication sensing signal. It should be understood that the detection echo is acquired through the radar receiving end. The precoding matrix is ​​used to precode the communication sensing signal, improving the performance of the signal received by the receiving end.

[0032] Specifically, an optimization problem is established with the sensing parameters as the objective function and the communication parameters and precoding matrix as constraints. This serves as preparation for solving the optimization problem and obtaining the target precoding matrix and target communication parameters.

[0033] S120. Under the condition that the communication parameters satisfy the first constraint and the precoding matrix satisfies the second constraint, the optimal solution of the objective function is obtained.

[0034] The first constraint can be a pre-set constraint on communication parameters. The second constraint can be a pre-set constraint on the precoding matrix. For example, communication parameters include the common communication rate of each communication receiver. The first constraint includes that the sum of the common communication rates of each communication receiver is not greater than the common rate of each communication receiver. The second constraint includes that the power obtained based on the precoding matrix is ​​not greater than a preset power threshold. The common communication rate refers to the common rate allocated to each communication receiver. The communication parameters of each communication receiver include the allocated common communication rate, common rate, private communication rate, and communication rate. The communication rate is the sum of the common communication rate and the private communication rate. The communication receiver refers to the user terminal that receives the communication sensing signal.

[0035] Specifically, the optimization problem is solved by finding the optimal solution to the objective function, provided that the communication parameters satisfy the first constraint and the precoding matrix satisfies the second constraint. This optimal solution is then used to determine the target precoding matrix and target communication parameters.

[0036] S130. Based on the optimal solution of the objective function, obtain the target precoding matrix and target communication parameters to optimize the performance of the communication sensing system.

[0037] Specifically, given the optimal solution to the objective function, the current precoding matrix is ​​determined as the target precoding matrix, and the current communication parameters are determined as the target communication parameters.

[0038] The technical solution of this invention establishes an optimization problem with sensing parameters as the objective function and communication parameters and a precoding matrix as constraints. Under the condition that the communication parameters satisfy a first constraint and the precoding matrix satisfies a second constraint, the optimal solution of the objective function is obtained. Given the optimal solution, the current precoding matrix is ​​determined as the target precoding matrix, and the current communication parameters are determined as the target communication parameters. This technical solution in this invention achieves the determination of the optimal target precoding matrix and target communication parameters, thereby improving the performance of the communication sensing system.

[0039] In another embodiment of the invention, the communication parameters and the perception parameters are obtained before establishing an optimization problem with the perception parameters as the objective function and the communication parameters and the precoding matrix as constraints.

[0040] Specifically, a target model for the communication sensing system is established, which includes a transmitter of the communication sensing signal, multiple communication receivers, and a radar receiver. Communication parameters for each communication receiver are acquired. When the radar receiver receives a detection echo corresponding to the communication sensing signal, the sensing parameters of the target response matrix are determined based on the detection echo.

[0041] For example, communication parameters include a common communication rate, and obtaining the common communication rate limit includes:

[0042] The data information stream at the sending end should be understood to include the communication sensing signal. The data information stream is as follows:

[0043]

[0044] in, For each communication receiver, the communication information stream For public information flow, A private information stream for each communication receiver. The sensing information stream for each communication receiver, and each data information stream includes the communication information stream and the sensing information stream.

[0045] The data information streams satisfy the condition of unit energy and are mutually independent. The communication sensing signal at the transmitting end can be represented as:

[0046] ,

[0047] Among them, k =1,2,……, K , This is the precoding matrix. p 1 indicates precoding of the public information flow. This represents the precoding of the private information stream at each communication receiver. This represents the precoding of the perceived information stream at each communication receiver. i =1, 2, ..., I . S This is the data transmission matrix.

[0048] The vector set of all transmitted signals received by the communication receivers can be represented as:

[0049] ,

[0050] in, For the channel, It follows a mean of 0 and a variance. The complex Gaussian distribution, N 1 represents noise generated during the communication process.

[0051] The signal-to-noise ratio of each communication receiver decoding the common information stream is:

[0052] ,

[0053] in, The variance of the second Gaussian noise. Indicates the communication receiving end kSignal-to-noise ratio for decoding public information streams. j =1,2,... K , i =1,2,... I .

[0054] The signal-to-noise ratio of each communication receiver decoding the private information stream is:

[0055] ,

[0056] in, Indicates the communication receiving end k The signal-to-noise ratio of decoding its own private information stream.

[0057] The common rate at which each communication receiver decodes the common information stream is:

[0058]

[0059] The private communication rate at which each communication receiver decodes the private information stream is:

[0060]

[0061] To ensure that all communication receivers can decode the common information stream, the common communication rate at which the common information stream can be transmitted is:

[0062] ,

[0063] r 1 represents the actual common communication rate of each communication receiver when sending information.

[0064] The public information flow contains partial information from each communication receiver, and the public communication rate that each communication receiver can occupy must satisfy the following:

[0065] ,

[0066] Ultimately, the communication rate of each receiving end includes a public communication rate component and a private communication rate component, expressed by the formula:

[0067]

[0068] Indicates the communication receiving end k The communication rate is equal to that of the communication receiver. k The sum of public and private communication rates.

[0069] The detection echo that the radar receiver can receive can be expressed as:

[0070]

[0071] in, G Let be the target response matrix. N 2 is a number with a mean of 0 and a variance of 0. The complex Gaussian distribution. N 2 represents the first noise during the radar process.

[0072] The sensing parameters include the Cramer-Rao bound. After obtaining the probe echo, the Cramer-Rao bound expression of the target response matrix is ​​obtained:

[0073] ,

[0074] in, , where is the covariance matrix of the communication sensing signal at the transmitting end. N 0 represents the number of antennas at the receiving end.

[0075] In another embodiment of the invention, the sensing parameters include Cramer-Rao bounds; the communication parameters include the communication rate of each communication receiver, which is used to receive the communication sensing signal; the step of establishing an optimization problem with the sensing parameters as the objective function and the communication parameters and the precoding matrix as constraints includes: calculating Cramer-Rao bounds based on the precoding matrix, Gaussian noise variance, covariance matrix of the communication sensing signal, information length of the communication sensing signal, and number of antennas at the communication receiver to generate the objective function; the constraints include: the communication rate of each communication receiver is not less than a preset communication rate threshold; the sum of the common communication rates of each communication receiver is not greater than the common rate of each communication receiver; and the transmission power obtained based on the precoding matrix is ​​not greater than a preset transmission power threshold, wherein the communication rate includes the common rate.

[0076] The communication rate at the receiving end includes both public and private communication rates.

[0077] In this embodiment of the invention, the Cramer-Rao bound is calculated based on the precoding matrix, Gaussian noise variance, covariance matrix of the communication sensing signal, information length of the communication sensing signal, and number of antennas at the communication receiver to generate an objective function. The communication rate of each communication receiver and the common communication rate are used as rate constraints, and the transmission power of the precoding matrix is ​​used as power constraints, thereby establishing the optimization problem.

[0078] In another embodiment of the invention, obtaining the optimal solution of the objective function when the communication parameters satisfy the first constraint condition and the precoding matrix satisfies the second constraint condition includes: obtaining the minimum value of the Cramer-Rao bound when the communication rate at each communication receiver is not less than a preset communication rate threshold, the sum of the common rates at each communication receiver is not greater than the common rate at each communication receiver, and the transmission power obtained by the precoding matrix is ​​not greater than a preset transmission power threshold, including:

[0079] The minimum value of the Cramer-Rao bound is obtained by the following formula:

[0080] ,

[0081] in, Let be the covariance matrix of the communication sensing signal. For the precoding matrix, X For communication sensing signals, S For data transmission matrix, L The length of information in the communication sensing signal. H Let c be the channel and c be the common communication rate allocation variable; c j For communication receiver j The allocated public communication rate, r 1,k The common rate for each communication receiver, r k For communication receiver k The allocated communication rate The variance of the first Gaussian noise is... r 11 To preset the communication rate threshold, P 0 represents the preset power threshold. E Indicates the expected value. s . t The dot (.) indicates a constraint. p 1 indicates precoding of the public information flow. This represents the precoding of the private information stream at each communication receiver. Precoding that represents the perceptual information flow. I = N 0- K -1, N 0 represents the number of antennas on the receiving end side of the communication receiver. K Indicates the number of communication receivers. P η represents the precoding matrix; η represents the set of communication receivers; F represents the Frobenius norm. Denotes the Frobenius norm of matrix P. This indicates that the total power corresponding to matrix P does not exceed a preset power threshold. The Frobenius norm is a way to measure the "size" or "energy" of a matrix. For an m×n matrix A, its Frobenius norm is defined as the square root of the sum of the squares of the absolute values ​​of all its elements.

[0082] In this embodiment of the invention, the objective function and constraints in the optimization problem are defined. Under the condition that the constraints are satisfied, the value of the objective function is obtained, which is then taken as the optimal solution for the objective function. The technical solution of this embodiment of the invention realizes the creation of an optimization problem.

[0083] In another embodiment of the invention, establishing an optimization problem with sensing parameters as the objective function and communication parameters and a precoding matrix as constraints includes: establishing an internal optimization problem with the sensing parameters as the internal objective function and the precoding matrix as the internal constraints; establishing an external optimization problem with the sensing parameters as the external objective function and a common communication rate as the external constraint; the internal problem includes an internal objective function and internal constraints, wherein establishing the internal optimization problem with the sensing parameters as the internal objective function and the precoding matrix as the internal constraints includes: calculating the Cramer-Rao bound based on the precoding matrix, Gaussian noise variance, covariance matrix of the communication sensing signal, information length in the communication sensing signal, and number of antennas at the communication receiver to generate the objective function; the internal constraints include: the communication rate of each communication receiver is large. The common communication rate is less than or equal to a preset communication rate threshold; the sum of the common communication rates of each communication receiver is less than or equal to the common rate of each communication receiver; the transmission power obtained based on the precoding matrix is ​​less than a preset transmission power threshold; the external problem includes an external objective function and external constraints, wherein establishing an external optimization problem with the communication rate as the external objective function and the common rate as the external constraint includes: an external function determined based on the communication rate and associated with the communication rate; the external constraints include: the common communication rate of each communication receiver is not less than zero; obtaining the target precoding matrix and target communication parameters based on the optimal solution of the objective function includes: obtaining the optimal solutions of the internal problem and the external problem respectively by solving the internal problem and the external problem; processing each optimal solution to obtain the target precoding matrix and the target common communication rate.

[0084] In this embodiment of the invention, the optimization problem is decomposed into internal and external problems. When solving the optimization problem, both internal and external problems can be solved simultaneously, reducing the difficulty of the solution. By optimizing sensing performance while ensuring communication performance, this method can improve the overall performance of the communication and sensing system.

[0085] In another embodiment of the invention, establishing an internal optimization problem with the sensing parameters as the internal objective function and the precoding matrix as the internal constraint includes: representing the internal objective function and the internal constraint by the following formula:

[0086]

[0087] The establishment of the external optimization problem, with the sensing parameters as the external objective function and the common rate as the external constraint, includes: expressing the external objective function and the external constraint by the following formulas:

[0088]

[0089] in, f ( c ) is an external function. c k The common communication rate for each communication receiver. k =1,2,... , c This represents a matrix consisting of the common communication rates of each communication receiver.

[0090] In this embodiment of the invention, internal and external problems are specifically defined by formulas to facilitate solving.

[0091] In another embodiment of the invention, the internal problem is further transformed into:

[0092]

[0093] in, As variables, , , , , , α The variables in the formula represent the internal problems that can be solved using the CVX toolbox in MATLAB, assuming the common communication rate is known. CVX is a convex optimization modeling system based on MATLAB. P 33 = p 2,1 - p 2,I This represents the precoding of all perceived information streams.

[0094] In another embodiment of the invention, the step of obtaining optimal solutions to the internal and external problems by solving the internal and external problems respectively, and processing each optimal solution to obtain the target precoding matrix and the target common communication rate, includes: iteratively solving the external problem using the particle swarm optimization algorithm; for each external solution obtained, substituting the external solution into the internal problem to obtain the internal solution of the internal problem; obtaining the optimal external solution when the external solution satisfies a preset condition; substituting the optimal external solution into the internal problem to obtain the optimal internal solution, thereby obtaining the target precoding matrix and the target common communication rate.

[0095] In this embodiment of the invention, the particle swarm optimization algorithm is used to iteratively solve the internal problem. After obtaining an internal solution for each internal problem, the internal solution is substituted into the external problem to obtain an external solution. If the external solution satisfies the constraints, the optimal external solution is obtained. With the optimal external solution obtained, the current communication rate is determined as the target common communication rate, and the current precoding matrix is ​​determined as the target precoding matrix. In this way, the optimization problem can be solved.

[0096] The specific process of the particle swarm optimization algorithm:

[0097] Define the size of the input particle swarm D The maximum number of iterations is J, the speedup factors are C1 and C2, and the local solution for each iteration is defined as... ,in, n =1,2,…,D, global solution is G The maximum speed of particle motion V Inertia weight W The ultimate goal is to find a convergent global solution through multiple iterations. G This global solution G This refers to the required common communication rate in the embodiments of the present invention. c The value of . The common communication rate c refers to the target common communication rate including all communication receivers. c j The matrix, j =1,2,… K In the following iterations, candidate common communication rates will be used. c11 Indicates the common communication rate to be processed. c .

[0098] Before iteration, based on the particle swarm size, in [ c min , c max The number of randomly initialized particle swarms within the range is D, with candidate common communication rates. c11 It should be understood that c minThis refers to the minimum preset public communication rate. c max This represents the maximum preset public communication rate, with candidate public communication rates being variables. c min =0, ,definition D Candidate common communication rates c11 for Xn , n =1,2,..., D Define the speed for each common communication rate. Vn =0; Initialize the local solution for each iteration. n =1,2,..., D This D indivual Xn Substitute into the internal problem and solve. D This internal issue, obtained D The solution to the internal problem is... Corresponding to each randomized candidate common communication rate c 11. Find the Cramer-Rao bounds and identify the smallest Cramer-Rao bound with the corresponding common communication rate. Assign this common communication rate as the global solution. G ,Right now At this time, the Cramero boundary is f ( G Then the iteration begins:

[0099] 1. Update the speed for each common communication rate. First, generate a speed based on each common communication rate. D Random weighting coefficients r 1 n , r 2 n ~ u , u Representing a uniform distribution following [0, 1], using The formula updates the rate for each common communication rate. After the update, it checks the speed; if the speed is not within the first speed range, [-] v min , v max Within this range, for the portion of the speed exceeding this range, the speed is used as the boundary value of the new first speed range to update the first speed range. Specifically, v min This represents the minimum value among the preset speeds. v max This indicates the maximum value among the preset speeds.

[0100] 2. Regarding D Public communication rate Xn Update via Perform an update. After the update is complete, if the public communication rate is not within the second speed range... c min , c max Within [the specified range], the portion of the speed exceeding the limit is used as the boundary value of the new second speed range to update the second speed range.

[0101] 3. The updated version D Public communication rate Xn Substitute into the internal problem and solve. D This internal issue, obtained D The solution to the internal problem is... .

[0102] 4. According to D indivual Update local solution ,like ,but .

[0103] 5. Based on D items Update the global solution G to obtain the minimum Craméroyal bound. That is, for each Make a judgment, if ,but This iteration is now complete. Number of iterations. .

[0104] The iteration ends when the number of iterations reaches the iteration threshold.

[0105] The optimal global solution is obtained through different iterative calculations. G , here G That is, the final required public communication rate. c The value of , the solution value of the corresponding internal problem. f ( G This is the Cramérod boundary, within the internal problem. P This is the optimal precoding matrix.

[0106] In another embodiment of the invention, after obtaining the target precoding matrix and the target communication parameters, the method further includes: transmitting a communication sensing signal using the target precoding matrix and the target communication parameters.

[0107] It should be noted that the establishment and solution of the optimization problem are based on the calculation of the communication sensing signal in the model of the communication sensing system. After obtaining the target precoding matrix and target communication parameters, in real-world scenarios, the precoding matrix and communication rate together determine the performance of the communication sensing system. Therefore, the communication sensing signal is determined based on the target common communication rate, and the communication sensing signal is transmitted based on the target precoding matrix.

[0108] In this embodiment of the invention, the communication information stream in the communication sensing signal can be split into a public information stream and a private information stream by using a target common communication rate. The communication sensing signal is then transmitted using a target precoding matrix. The technical solution of this embodiment of the invention achieves the determination of the target precoding matrix and the target common communication rate, thereby optimizing the acquisition and transmission of the communication sensing signal and improving the performance of the communication sensing system.

[0109] In another embodiment of the invention, a performance optimization device is provided. This device can execute the performance optimization method provided in any embodiment of the invention, and possesses the corresponding functional modules and beneficial effects of the method. For example... Figure 2 As shown, the device includes: a problem establishment module 410, an optimal solution acquisition module 420, and a performance optimization module 430, wherein:

[0110] Problem establishment module 410 is used to establish an optimization problem with sensing parameters as the objective function and communication parameters and precoding matrix as constraints; the communication parameters include parameters of the communication receiving end that receives the communication sensing signal, and the sensing parameters include parameters obtained based on the detection echo corresponding to the communication sensing signal; optimal solution acquisition module 420 is used to obtain the optimal solution of the objective function when the communication parameters satisfy the first constraint and the precoding matrix satisfies the second constraint; performance optimization module 430 is used to obtain the target precoding matrix and target communication parameters based on the optimal solution of the objective function, so as to achieve performance optimization of the communication sensing system.

[0111] Preferably, in this embodiment of the invention, the sensing parameters include the Cramer-Rao boundary; the communication parameters include the communication rate of each communication receiver, the communication receiver being used to receive communication sensing signals;

[0112] The problem establishment module 410 is also used for:

[0113] The Cramer-Rao bound is calculated based on the precoding matrix, Gaussian noise variance, covariance matrix of the communication sensing signal, information length of the communication sensing signal, and number of antennas at the communication receiver to generate the objective function.

[0114] The constraints include: the communication rate of each communication receiver is not less than a preset communication rate threshold; the sum of the common communication rates of each communication receiver is not greater than the common rate of each communication receiver; and the transmission power obtained based on the precoding matrix is ​​not greater than a preset transmission power threshold, wherein the communication rate includes the common rate.

[0115] Preferably, in this embodiment of the invention, the optimal solution acquisition module 420 is further configured to:

[0116] The minimum value of the Cramer-Rao bound is obtained when the communication rate at each of the communication receivers is not less than a preset communication rate threshold, the sum of the common communication rates at each of the communication receivers is not greater than the common rate at each of the communication receivers, and the transmission power obtained by the precoding matrix is ​​not greater than a preset transmission power threshold. This includes:

[0117] The minimum value of the Cramer-Rao bound is obtained by the following formula:

[0118] ,

[0119] in, Let be the covariance matrix of the communication sensing signal. For the precoding matrix, X For communication sensing signals, S For data transmission matrix, L The length of information in the communication sensing signal. H For the channel, c j For communication receiver j The allocated public communication rate, r 1,k The common rate for each communication receiver, r k For communication receiver k The allocated communication rate The variance of the first Gaussian noise is... r 11 To preset the communication rate threshold, P 0 represents the preset power threshold. E Indicates the expected value. s . t The dot (.) indicates a constraint. p 1 indicates precoding of the public information flow. This represents the precoding of the private information stream at each communication receiver. Precoding that represents the perceptual information flow. I = N 0- K -1, N0 represents the number of antennas on the receiving end side of the communication receiver. K Indicates the number of communication receivers. P This represents the precoding matrix.

[0120] Preferably, in this embodiment of the invention, the problem establishment module 410 is further configured to:

[0121] An internal optimization problem is established with the sensing parameters as the internal objective function and the precoding matrix as the internal constraint; an external optimization problem is established with the sensing parameters as the external objective function and the common communication rate as the external constraint; the internal problem includes the internal objective function and the internal constraint, wherein establishing the internal optimization problem with the sensing parameters as the internal objective function and the precoding matrix as the internal constraint includes: calculating the Cramer-Rao bound based on the precoding matrix, the Gaussian noise variance, the covariance matrix of the communication sensing signal, the information length in the communication sensing signal, and the number of antennas at the communication receiver to generate the objective function. The internal constraints include: the communication rate of each communication receiver is greater than or equal to a preset communication rate threshold; the sum of the common communication rates of each communication receiver is less than or equal to the common rate of each communication receiver; and the transmission power obtained based on the precoding matrix is ​​less than a preset transmission power threshold. The external problem includes an external objective function and external constraints, wherein establishing an external optimization problem with the communication rate as the external objective function and the common rate as the external constraint includes: an external function determined based on the communication rate and associated with the communication rate; the external constraints include: the common communication rate of each communication receiver is not less than zero; obtaining the target precoding matrix and target communication parameters based on the optimal solution of the objective function includes: obtaining the optimal solutions of the internal problem and the external problem respectively by solving the internal problem and the external problem; and processing each optimal solution to obtain the target precoding matrix and the target common communication rate.

[0122] Preferably, in this embodiment of the invention, the problem establishment module 410 is further configured to:

[0123] The internal objective function and internal constraints are expressed by the following formulas:

[0124]

[0125] The establishment of the external optimization problem with the sensing parameters as the external objective function and the common rate as the external constraint includes:

[0126] The external objective function and external constraints are expressed by the following formulas:

[0127]

[0128] in, f ( c ) is an external function. c k The common communication rate for each communication receiver. k =1,2,... , c This represents a matrix consisting of the common communication rates of each communication receiver.

[0129] Preferably, in this embodiment of the invention, the problem establishment module 410 is further configured to:

[0130] By iteratively solving the external problem using the particle swarm optimization algorithm, each time an external solution to the external problem is obtained, the external solution is substituted into the internal problem to obtain an internal solution to the internal problem. If the external solution satisfies a preset condition, the optimal external solution is obtained. The optimal external solution is then substituted into the internal problem to obtain the optimal internal solution, thereby obtaining the target precoding matrix and the target common communication rate.

[0131] Preferably, in this embodiment of the invention, the device further includes:

[0132] The signal transmission module is used to transmit communication sensing signals using the target precoding matrix and target communication parameters.

[0133] The technical solution of this invention establishes an optimization problem with sensing parameters as the objective function and communication parameters and a precoding matrix as constraints. Under the condition that the communication parameters satisfy a first constraint and the precoding matrix satisfies a second constraint, the optimal solution of the objective function is obtained. Based on the optimal solution of the objective function, the target precoding matrix and target power parameters are obtained, thereby optimizing the performance of the communication sensing system. This technical solution achieves the acquisition of the optimal target precoding matrix and target communication parameters, and then transmits communication sensing signals based on these parameters, improving the accuracy of communication sensing signal transmission and enhancing the performance of the communication sensing system.

[0134] It is worth noting that the modules included in the above-mentioned device are divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional module are only for easy differentiation and are not used to limit the protection scope of the embodiments of the present invention.

[0135] In another embodiment of the invention, an electronic device is provided. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Figure 3 A block diagram is shown of an exemplary electronic device 50 suitable for implementing embodiments of the present invention. Figure 3The electronic device 50 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0136] like Figure 3 As shown, the electronic device 50 is represented in the form of a general-purpose computing device. The components of the electronic device 50 may include, but are not limited to: one or more processors or processing units 501, system memory 502, and bus 503 connecting different system components (including system memory 502 and processing unit 501).

[0137] Bus 503 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0138] Electronic device 50 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 50, including volatile and non-volatile media, removable and non-removable media.

[0139] System memory 502 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 504 and / or cache memory 505. Electronic device 50 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 506 may be used to read and write non-removable, non-volatile magnetic media (… Figure 3 Not shown; usually referred to as a "hard drive"). Although Figure 3 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 503 via one or more data media interfaces. Memory 502 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0140] A program / utility 508 having a set (at least one) of program modules 507 may be stored, for example, in memory 502. Such program modules 507 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 507 typically perform the functions and / or methods described in the embodiments of the present invention.

[0141] Electronic device 50 can also communicate with one or more external devices 509 (e.g., keyboard, pointing device, display 510, etc.), and with one or more devices that enable a user to interact with electronic device 50, and / or with any device that enables electronic device 50 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 511. Furthermore, electronic device 50 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 512. As shown, network adapter 512 communicates with other modules of electronic device 50 via bus 503. It should be understood that, although... Figure 3 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 50, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0142] The processing unit 501 executes various functional applications and data processing by running programs stored in the system memory 502, such as implementing the performance optimization method provided in the embodiments of the present invention.

[0143] In another embodiment of the invention, a storage medium containing computer-executable instructions is provided, which, when executed by a computer processor, are used to perform a performance optimization method, the method comprising:

[0144] An optimization problem is established with sensing parameters as the objective function and communication parameters and a precoding matrix as constraints. The communication parameters include parameters of the communication receiver receiving the communication sensing signal, and the sensing parameters include parameters obtained from the probe echoes corresponding to the communication sensing signal. The optimal solution of the objective function is obtained when the communication parameters satisfy a first constraint and the precoding matrix satisfies a second constraint. Based on the optimal solution of the objective function, the target precoding matrix and target communication parameters are obtained to optimize the performance of the communication sensing system.

[0145] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0146] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0147] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0148] Computer program code for performing the operations of embodiments of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider). The above disclosure is merely a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. Therefore, equivalent variations made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A performance optimization method, characterized in that, include: An optimization problem is established with sensing parameters as the objective function and communication parameters and precoding matrix as constraints. The communication parameters include parameters of the communication receiving end that receives the communication sensing signal, and the sensing parameters include parameters obtained based on the detection echo corresponding to the communication sensing signal; The sensing parameters include the Cramer-Rao boundary; the communication parameters include the communication rate of each communication receiver, which is used to receive communication sensing signals. The optimization problem established, with sensing parameters as the objective function and communication parameters and precoding matrix as constraints, includes: The Cramer-Rao bound is calculated based on the precoding matrix, Gaussian noise variance, covariance matrix of the communication sensing signal, information length of the communication sensing signal, and number of antennas at the communication receiver to generate the objective function. The optimal solution of the objective function is obtained when the communication parameters satisfy the first constraint and the precoding matrix satisfies the second constraint; wherein the first constraint is: the communication rate of each communication receiver is not less than a preset communication rate threshold; the sum of the common communication rates of each communication receiver is not greater than the common rate of each communication receiver; the second constraint is that the transmission power obtained based on the precoding matrix is ​​not greater than a preset transmission power threshold. Based on the optimal solution of the objective function, the target precoding matrix and target communication parameters are obtained to optimize the performance of the communication sensing system.

2. The method according to claim 1, characterized in that, The step of obtaining the optimal solution of the objective function when the communication parameters satisfy the first constraint and the precoding matrix satisfies the second constraint includes: The minimum value of the Cramer-Rao bound is obtained when the communication rate at each of the communication receivers is not less than a preset communication rate threshold, the sum of the common communication rates at each of the communication receivers is not greater than the common rate at each of the communication receivers, and the transmission power obtained by the precoding matrix is ​​not greater than a preset transmission power threshold. This includes: The minimum value of the Cramer-Rao bound is obtained by the following formula: ， in, Let be the covariance matrix of the communication sensing signal. For the precoding matrix, X For communication sensing signals, S For data transmission matrix, L The length of information in the communication sensing signal. H Let c be the channel and c be the common communication rate allocation variable; c j For communication receiver j The allocated public communication rate, r 1,k The common rate for each communication receiver, r k For communication receiver k The allocated communication rate The variance of the first Gaussian noise is... r 11 To preset the communication rate threshold, P 0 represents the preset power threshold. E Indicates the expected value. s . t The dot (.) indicates a constraint. p 1 indicates precoding of the public information flow. This represents the precoding of the private information stream at each communication receiver. Precoding that represents the perceptual information flow. I = N 0- K -1, N 0 represents the number of antennas on the receiving end side of the communication receiver. K Indicates the number of communication receivers. P η represents the precoding matrix; η represents the set of communication receivers; F represents the Frobenius norm. Denotes the Frobenius norm of matrix P. This indicates that the total power corresponding to matrix P does not exceed a preset power threshold. .

3. The method according to claim 2, characterized in that, The optimization problem established, with sensing parameters as the objective function and communication parameters and precoding matrix as constraints, includes: An internal optimization problem is established with the perception parameters as the internal objective function and the precoding matrix as the internal constraint. An external optimization problem is established with the sensing parameters as the external objective function and the common communication rate as the external constraint. The internal problem includes an internal objective function and internal constraints, wherein establishing an internal optimization problem with the perceptual parameters as the internal objective function and the precoding matrix as the internal constraints includes: The Cramer-Rao bound is calculated based on the precoding matrix, Gaussian noise variance, covariance matrix of the communication-sensed signal, information length in the communication-sensed signal, and number of antennas at the communication receiver to generate the objective function. The internal constraints include: The communication rate of each communication receiver is greater than or equal to a preset communication rate threshold; The sum of the common communication rates of each communication receiver is less than or equal to the common rate of each communication receiver; The transmission power obtained based on the precoding matrix is ​​less than a preset transmission power threshold; The external problem includes an external objective function and external constraints. Specifically, establishing an external optimization problem with communication rate as the external objective function and common rate as the external constraints includes: An external function associated with the communication rate, determined based on the communication rate; The external constraints include: The common communication rate of each communication receiver is not less than zero; The process of obtaining the target precoding matrix and target communication parameters based on the optimal solution of the objective function includes: By solving the internal and external problems, the optimal solutions to the internal and external problems are obtained respectively. Each optimal solution is processed to obtain the target precoding matrix and the target common communication rate.

4. The method according to claim 3, characterized in that, The establishment of the internal optimization problem, with the sensing parameters as the internal objective function and the precoding matrix as the internal constraint, includes: The internal objective function and internal constraints are expressed by the following formulas: The establishment of the external optimization problem with the sensing parameters as the external objective function and the common rate as the external constraint includes: The external objective function and external constraints are expressed by the following formulas: in, f ( c ) is an external function. c k The common communication rate for each communication receiver. k =1,2,... , c This represents a matrix consisting of the common communication rates of each communication receiver.

5. The method according to claim 4, characterized in that, The optimal solutions to the internal and external problems are obtained by solving the internal and external problems respectively. Each optimal solution is processed to obtain the target precoding matrix and the target common communication rate, including: By iteratively solving the external problem using the particle swarm optimization algorithm, each time an external solution to the external problem is obtained, the external solution is substituted into the internal problem to obtain an internal solution to the internal problem. If the external solution satisfies a preset condition, the optimal external solution is obtained. The optimal external solution is then substituted into the internal problem to obtain the optimal internal solution, thereby obtaining the target precoding matrix and the target common communication rate.

6. The method according to claim 1, characterized in that, After obtaining the target precoding matrix and target communication parameters, the method further includes: Communication sensing signals are transmitted using the target precoding matrix and target communication parameters.

7. A performance optimization device, characterized in that, include: The problem-setting module is used to set up optimization problems with perception parameters as the objective function and communication parameters and precoding matrices as constraints. The communication parameters include parameters of the communication receiving end that receives the communication sensing signal, and the sensing parameters include parameters obtained based on the detection echo corresponding to the communication sensing signal; wherein, the sensing parameters include the Cramer-Rao boundary; the communication parameters include the communication rate of each communication receiving end, and the communication receiving end is used to receive the communication sensing signal; The optimization problem established, with sensing parameters as the objective function and communication parameters and precoding matrix as constraints, includes: The Cramer-Rao bound is calculated based on the precoding matrix, Gaussian noise variance, covariance matrix of the communication sensing signal, information length of the communication sensing signal, and number of antennas at the communication receiver to generate the objective function. The optimal solution acquisition module is used to obtain the optimal solution of the objective function when the communication parameters satisfy a first constraint and the precoding matrix satisfies a second constraint; wherein the first constraint is: the communication rate of each communication receiver is not less than a preset communication rate threshold; the sum of the common communication rates of each communication receiver is not greater than the common rate of each communication receiver; the second constraint is that the transmission power obtained based on the precoding matrix is ​​not greater than a preset transmission power threshold; The performance optimization module is used to obtain the target precoding matrix and target communication parameters based on the optimal solution of the objective function, so as to achieve performance optimization of the communication sensing system.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the performance optimization method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the performance optimization method as described in any one of claims 1-6.

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