A precoding method and device for a cell-free massive MIMO communication and sensing integrated system

Abstract

The application discloses a precoding method and device of a cell-free massive MIMO integrated system, wherein an access point provides data services to a communication user through a downlink of the cell-free massive MIMO integrated system, and meanwhile, tracks a point-like sensing target from a reflection echo of the communication user. The method comprises the following steps: calculating a downlink reachable rate of the communication user and a signal-to-noise ratio of the reflection echo, constructing a problem function by taking the downlink reachable rate of the communication user as a maximum target and taking the signal-to-noise ratio of the reflection echo as a constraint, and solving the constructed problem function to obtain a precoding scheme of the system. The application can greatly improve the communication performance of the system and show the trade-off relationship between the communication performance and the sensing performance under the premise of not introducing additional system energy consumption and guaranteeing the sensing performance, so as to enhance the utilization efficiency of wireless spectrum resources and expand and improve the overall performance of the system.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of wireless communication, and particularly relates to a precoding method and device for a cell-free massive MIMO (Multiple-Input Multiple-Output) communication and sensing integrated system. BACKGROUND

[0002] The cell-free massive MIMO technology can use densely deployed access points to eliminate the cell boundary restriction and effectively alleviate the serious inter-cell interference in the traditional massive MIMO system. The communication and sensing integrated technology aims to realize the sensing function by using the "zero addition" wireless communication, that is, based on the wireless communication, as few as possible or no sensing module is added to realize the traditional positioning and tracking sensing functions, and the overall performance of the system is comprehensively expanded and improved.

[0003] However, the communication and sensing are integrated in the same hardware platform and share the frequency spectrum resources. Considering the limited power of the access point, there is a trade-off between the communication and sensing functions, and in certain cases, the two are extremely opposite, which seriously affects the system performance.

[0004] The existing cell-free massive MIMO communication and sensing integrated research has not deeply involved the mutual influence between the communication and sensing modules; at the same time, under the premise of power constraint, it is difficult to guarantee the sensing performance. SUMMARY

[0005] To solve the problems in the prior art, the present application provides a precoding method and device for a cell-free massive MIMO communication and sensing integrated system, which can greatly improve the communication performance of the system and show the trade-off relationship between the communication and sensing performance without introducing additional system energy consumption and guaranteeing the sensing performance, so as to enhance the utilization efficiency of wireless frequency spectrum resources and expand and improve the overall performance of the system.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0007] In a first aspect, a precoding method for a cell-free massive MIMO communication and sensing integrated system is provided. An access point provides data services to a communication user through the downlink of the cell-free massive MIMO communication and sensing integrated system, and simultaneously tracks a point-like sensing target from the reflection echo thereof. The method comprises: calculating the downlink reachable rate of the communication user and the signal-to-noise ratio of the reflection echo; constructing a problem function with the maximum downlink rate of the communication user as the target and the signal-to-noise ratio of the reflection echo as the constraint; and solving the constructed problem function to obtain a precoding scheme of the system.

[0008] Furthermore, the non-cellular massive MIMO sensing integrated system includes: a number of access points, a number of communication users, and a point-like sensing target evenly distributed in a defined plane; wherein, all access points are equipped with a set number of antennas, and all communication users are single-antenna devices.

[0009] Furthermore, the access point provides data services to communication users through the downlink of the non-cellular massive MIMO sensing integrated system, including:

[0010] No. The access point and the first The wireless channel between communication users is the Rayleigh channel. , is represented as:

[0011] ,

[0012] Among them, subscript Indicates a communication system. Indicates the first The access point and the first Large-scale fading coefficients among individual communication users Indicates the first The access point and the first Small-scale fading vectors between communication users.

[0013] Furthermore, the access point tracks point-like sensing targets from reflected echoes, including:

[0014] No. The wireless channel between each access point and the point-like sensing target is a direct link, and the target response matrix is... Represented as:

[0015] ,

[0016] Among them, subscript Indicates a sensing system, superscript This represents the conjugate transpose operation. It is the reflection coefficient, which includes the effects of path loss and the target's radar cross-section. and These are the point-like sensing targets relative to the first... The first access point and the first The azimuth angle of each access point and They are the first The first access point transmitting antenna and the first The steering vector of the receiving antenna at each access point.

[0017] Furthermore, the constructed problem function is as follows:

[0018] ,

[0019] wherein, represents the precoding vector corresponding to the th access point for the th communication user, represents the downlink sum-rate of the communication users, and respectively represent the actual power and the maximum power constraint of the th access point, and respectively represent the signal-to-noise ratio of the reflected echo and the minimum threshold value for guaranteeing the sensing performance.

[0020] Further, solving the constructed problem function comprises:

[0021] The constructed problem function is a sum-rate maximization problem (P1), by introducing , , three variables, the sum-rate maximization problem (P1) is equivalently transformed into a weighted sum mean square error minimization problem (P2);

[0022] ,

[0023] wherein, represents the trace operation, represents the determinant;

[0024] The weighted sum mean square error minimization problem (P2) is a convex optimization problem for , , respectively, and is solved by using an alternating iterative optimization method, i.e., fixing , , and obtaining the optimal precoding scheme by further solving a problem (P3), wherein the superscript represents an optimal solution, and the problem (P3) is expressed as:

[0025] .

[0026] Further, the alternating iterative optimization method comprises:

[0027] initializing the precoding , wherein the superscript represents the number of iterations, to satisfy the system power constraint, setting an iteration termination error , and iterating the variable .

[0028] Step one, according to the precoding , sequentially calculate , two variables, backhaul the objective function of problem (P3) and let ; define , where the superscript represents the transpose operation, is an auxiliary variable, define , is the first row of the identity matrix , and by the semi-definite relaxation algorithm, problem (P3) is reconstructed as problem (P4) to meet the definition of convex constraints, problem (P4) is expressed as:

[0029] ,

[0030] wherein, , , , , ;

[0031] Step two, using the CVX toolbox in Matlab software, solve problem (P4), and get the precoding by decomposition , at the same time, calculate the downlink and rate of the communication user ;

[0032] Loop step one and step two until , get the optimal precoding of the system.

[0033] The second aspect provides a precoding device of a cell-free massive MIMO integrated communication and sensing system. An access point provides data services to a communication user through the downlink of the cell-free massive MIMO integrated communication and sensing system, and simultaneously tracks a point-like sensing target from the reflected echo of the communication user. The device comprises: a function construction module configured to calculate the downlink reachable rate of the communication user and the signal-to-noise ratio of the reflected echo, construct a problem function with the maximum downlink and rate of the communication user as the target and the signal-to-noise ratio of the reflected echo as the constraint; and a solution module configured to solve the constructed problem function to obtain a precoding scheme of the system.

[0034] The third aspect provides a computer program product comprising computer programs / instructions, which, when executed by a processor, implement the steps of the precoding method of the cell-free massive MIMO integrated communication and sensing system according to the first aspect.

[0035] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention constructs a cellular-free massive MIMO communication and sensing integrated system, in which access points and communication users are uniformly distributed in a plane, and point-like sensing targets exist within the system; the access points provide data services to communication users through the downlink, while simultaneously tracking the point-like sensing targets from their reflected echoes, and calculating the downlink achievable rate of the communication users and the signal-to-noise ratio of the reflected echoes; with the goal of maximizing the downlink speed and rate of the communication users, and with the signal-to-noise ratio of the reflected echoes as a constraint, a problem function is constructed to obtain the optimal precoding design scheme for the system; it can significantly improve the trade-off between the system's communication performance and the ability to demonstrate communication and sensing performance without introducing additional system energy consumption and ensuring sensing performance; for the first time in a cellular-free massive MIMO communication and sensing integrated system, precoding is optimized to enhance the efficiency of wireless spectrum resource utilization and comprehensively expand and improve the overall system performance. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the main implementation process of a precoding method for a non-cellular massive MIMO integrated sensing system provided in an embodiment of the present invention;

[0037] Figure 2 In this embodiment of the invention, the downlink speed varies with the number of access points. M A graph showing the changes;

[0038] Figure 3 This represents the trade-off between communication performance and sensing performance in this embodiment of the invention.

[0039] Figure 4 Different numbers of antennas in embodiments of the present invention N The corresponding beam pattern. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0041] Example 1

[0042] A precoding method for a non-cellular massive MIMO sensing system is disclosed, wherein an access point provides data services to a communication user through the downlink of the non-cellular massive MIMO sensing system, and simultaneously tracks point-like sensing targets from the reflected echoes. The method includes: calculating the downlink achievable rate of the communication user and the signal-to-noise ratio (SNR) of the reflected echoes; constructing a problem function with the maximization of the downlink rate of the communication user as the objective and the SNR of the reflected echoes as a constraint; and solving the constructed problem function to obtain the precoding scheme of the system.

[0043] The main implementation process of the precoding method of the cell-free massive MIMO communication and sensing integrated system is as shown in the following Figure 1

[0044] S1: Construct a cell-free massive MIMO communication and sensing integrated system, which includes access points, communication users and a point-like sensing target;

[0045] In the embodiment of the present application, the constructed cell-free massive MIMO communication and sensing integrated system includes: M access points and K users, which are uniformly distributed in a plane; and a point-like sensing target; wherein all access points are equipped with N antennas, and all communication users are single-antenna devices.

[0046] For example, there is a cell-free massive MIMO communication and sensing integrated system, which includes access points, users are uniformly distributed in a square plane area with a side length of 1 kilometer, and a point-like sensing target exists in the system; wherein all access points are equipped with N antennas, and all communication users are single-antenna devices; wherein the transmit power of the access point is or It should be noted that, for the convenience of later simulation, one access point can be selected to be at the center of the square area, and the point-like sensing target is at the 0° direction of the access point.

[0047] S2: The access points provide data services to the communication users through the downlink, and track the point-like sensing target from the reflection echo thereof;

[0048] In the embodiment of the present application, the access points provide data services to the communication users through the downlink, including:

[0049] The wireless channel between the access point and the communication user is a Rayleigh channel, which is expressed as:

[0050] ,

[0051] wherein the subscript represents the communication system, represents the large-scale fading coefficient between the access point and the communication user, represents the small-scale fading vector between the access point and the communication user.

[0052] ​It should be noted that the large-scale fading is modeled as

[0053] ,

[0054] wherein, is a lognormal random variable with a mean =8db, is the distance between the th communication user and the th access point, =3.8 is the path loss exponent.

[0055] In the embodiment of the present application, the access point tracks the point-like sensing target from the reflection echo, including:

[0056] The wireless channel between the th access point and the point-like sensing target is a direct link, and the target response matrix is expressed as:

[0057] ,

[0058] wherein, the subscript represents the sensing system, the superscript represents the conjugate transpose operation, is the reflection coefficient, including the influence of the path loss and the target radar cross section, and are the azimuth angles of the point-like sensing target relative to the th access point and the th access point, respectively, and are the steering vectors of the th access point transmitting antenna and the th access point receiving antenna, respectively.

[0059] It should be noted that the access point preferably adopts a uniform linear array with a half-wavelength interval, and the steering vector is expressed as:

[0060] ,

[0061] wherein, the superscript T represents the transpose operation.

[0062] S3: calculating the downlink reachable rate of the communication user and the signal-to-noise ratio of the reflection echo, constructing a problem function with the maximum downlink rate of the communication user as the target and the signal-to-noise ratio of the reflection echo as the constraint;

[0063] In the embodiment of the present application, the downlink reachable rate of the communication user is calculated, including:

[0064] theM integrated signal transmitted by the access point is modeled as:

[0065] ,

[0066] wherein, is the transmit precoding matrix of the th access point, is the th column of , is the data symbol vector transmitted by the access point to the communication user, satisfying , and the data symbols between different communication users are mutually independent.

[0067] Thus, the received signal of the th communication user is expressed as:

[0068] ,

[0069] wherein, is the Gaussian white noise received by the th communication user.

[0070] The downlink achievable rate of the communication user can be calculated, expressed as:

[0071] ,

[0072] wherein, can be expressed as:

[0073] ,

[0074] In the embodiment of the present application, the signal-to-noise ratio of the reflected echo is calculated, comprising:

[0075] A radar receiver is installed at each AP to preliminarily process the reflected echo signal, and the output of the radar receiver can be expressed as:

[0076] ,

[0077] wherein, is the Gaussian white noise received by the th access point, is the receiver vector of the th AP, used to maximize the signal-to-noise ratio of the reflected echo signal, and thus the optimal can be calculated as:

[0078] ,

[0079] All APs transmit the output signal of the radar receiver to the CPU through a lossless backhaul link, which is expressed as:

[0080] ,

[0081] Therefore, the signal-to-noise ratio of the reflected echo can be calculated, which is expressed as:

[0082] ,

[0083] wherein, denotes the trace operation, and the standard deviation is 0.1.

[0084] In the embodiment of the present application, the problem function is constructed with the maximization of the sum rate of the communication users as the target and the signal-to-noise ratio of the reflected echo as the constraint, including: finding the optimal precoding design method with the maximization of the sum rate of the communication users as the target, guaranteeing the strength of the reflected echo signal and considering the system power constraint, and constructing the problem function (P1), which is expressed as:

[0085] ,

[0086] wherein, denotes the precoding vector corresponding to the kth communication user of the ith access point, denotes the downlink sum rate of the communication user, denotes the actual power of the ith access point, and denotes the maximum power constraint of the ith access point, and denote the signal-to-noise ratio of the reflected echo and the minimum threshold value guaranteeing the sensing performance, respectively. S4: The problem function is solved by the weighted least mean square error algorithm and the CVX toolbox in the Matlab software, and the optimal design method of the system precoding is restored by the Gaussian randomization method. In the embodiment of the present application, the problem function is solved by the weighted least mean square error algorithm and the CVX toolbox in the Matlab software, and the optimal design method of the system precoding is restored by the Gaussian randomization method, including:

[0087] By the weighted least mean square error algorithm, three variables are introduced,

[0088] , ,

[0089] the original sum rate maximization problem (P1) is equivalently converted into a weighted sum mean square error minimization problem (P2);

[0090] ​​​Specifically, , , Three variables are expressed as:

[0091] ,

[0092] ,

[0093] ,

[0094] In the formula, , ;

[0095] Specifically, the weighted sum of squared error minimization problem (P2) is expressed as:

[0096] ,

[0097] It should be noted that the weighted sum of squared error minimization problem (P2) is a convex optimization problem for , , Therefore, the alternating iterative optimization method can be used to solve it, that is, fixing , , the optimal precoding scheme is obtained by further solving problem (P3), which is expressed as:

[0098] ,

[0099] Specifically, the alternating iterative optimization method includes:

[0100] Initialize the precoding , where the superscript represents the number of iterations to meet the system power constraint; set the iteration termination error ; the iteration variable ;

[0101] Specifically, the iteration termination error can be 10 -3 or customized according to actual needs.

[0102] Step one, according to the precoding , in turn, calculate , Two variables, back to the expression Objective function of problem (P3). To further simplify the expression, define , where the superscript represents the transpose operation, is an auxiliary variable, , unit array The Okay, and using a semidefinite relaxation algorithm, the problem (P3) is reconstructed into problem (P4) to satisfy the definition of convex constraints, expressed as:

[0103] ,

[0104] in, , , , , ;

[0105] Step two: Use the CVX toolbox in Matlab software to solve problem (P4), and decompose it using Gaussian randomization. Get precoding Specifically, it can be stated as follows: First, for a positive semi-definite matrix... Perform eigenvalue decomposition to obtain Next, randomly generate... A group of Gaussian random vectors with standard normal distribution Construct the corresponding Select the one that maximizes the objective function of problem (P4). Finally, through normalization Auxiliary variables Take the front N The item is precoded And calculate the downlink speed of communication users. Repeat steps one and two until... So far, the optimal precoding of the system has been obtained. .

[0106] It should be noted that the optimized precoding scheme can effectively improve the downlink speed of the system; and during the optimization process, by adjusting the minimum threshold value of sensing performance, the trade-off between communication and sensing performance in the cellular-free massive MIMO communication and sensing integrated system can be intuitively demonstrated.

[0107] Figure 2 , Figure 3 and Figure 4 The downlink speed and data rate as a function of the number of access points are respectively determined by the precoding design method for a cellular massive MIMO communication and sensing integrated system. M The graph showing the changes, the trade-off between communication and sensing performance, and the number of antennas. N The corresponding beam pattern.

[0108] exist Figure 2 The accuracy and convergence of the results of this invention were verified in the experiment. Different numbers of access points were used...M The downlink sum rate of the system is compared with the downlink sum rate of the system under the constraint of the downlink sum rate of the system, and it can be seen from the figure that the downlink sum rate of the system can be effectively increased in the iteration process, and the convergence of the proposed weighted least mean square error algorithm is also ensured. In addition, it can also be found that with the increase of the number of access points in the region, the downlink sum rate of the system also increases, because increasing the number of access points can enhance the array gain of the system and improve the quality of the received signal of the communication user; the spatial degrees of freedom of the system also increase accordingly, and the system can transmit more data streams at the same time to realize spatial multiplexing gain. Therefore, under the condition that the perception constraint is unchanged, increasing the number of access points can effectively realize the synergistic effect of multiple gain mechanisms, improve the signal-to-noise ratio of the transmitted signal under the same bandwidth condition, thereby improving the channel capacity of the system and supporting higher communication data transmission rate.

[0109] In Figure 3 , the trade-off relationship between the downlink sum rate of the communication user with different number of communication users and the perception signal-to-noise ratio constraint is clearly shown. The results show that the system performance compromise boundary can be divided into three parts of communication constraint, double constraint and perception constraint by and Two boundary points. On the left side of the boundary point , the system is under the communication constraint, and the performance is mainly limited by the communication ability, at this time, the downlink sum rate of the system reaches the maximum , and the perception signal-to-noise ratio is relatively low, which is suitable for the scene with high requirement on communication rate and relatively low requirement on perception accuracy, such as high-speed data transmission. Between the boundary points and , the system is under the double constraint, and is simultaneously constrained by communication and perception. At this time, the downlink sum rate and the perception signal-to-noise ratio are at an intermediate level, and the system needs to trade off between the two, and the system resources need to be reasonably allocated between communication and perception tasks to achieve a balance between the two, which is suitable for the scene with certain requirements on communication and perception, such as unmanned aerial vehicle communication and perception integrated system, which needs to ensure the performance of data transmission and target detection at the same time; on the right side of the boundary point , the system is under the perception constraint, and the performance of the system is mainly limited by the perception ability, at this time, the perception signal-to-noise ratio reaches the maximum , and the downlink sum rate is relatively low, which is suitable for the scene with high requirement on perception accuracy and relatively low requirement on communication rate, such as high-precision radar detection. With the number of communication users K =5, the transmit power of the access point Taking the case of 20dB as an example, the boundary points of the system performance tradeoff are (8.64, 15) and (7.35, 18). It is worth noting that the boundary points that divide the system performance tradeoff boundary into three parts cannot be calculated using a fixed formula.

[0110] exist Figure 4 The image shows optimized beammaps for access points with different numbers of antennas. This invention will... The optimized beam pattern for each access point is defined as follows: ,in S3 provides the first method designed to maximize the perceived signal-to-noise ratio. The optimal receiver vector for each AP. Figure 4 The indicator shows the azimuth angle of the main beam at the target being sensed. From a radar perspective, beam pattern performance improves as the number of AP antennas increases. Specifically, the maximum side peak ratio decreases with increasing radar antenna count, and the main beamwidth also narrows. This is because more antennas provide higher array gain, allowing the beam to be pointed more precisely at the target, thus improving beam directivity and gain, as well as the system's spatial resolution, resulting in a narrower and stronger main lobe in the beam pattern.

[0111] Example 2

[0112] Based on the precoding method for a cellular-free massive MIMO sensing integrated system described in Embodiment 1, this embodiment provides a precoding device for a cellular-free massive MIMO sensing integrated system. The access point provides data services to communication users through the downlink of the cellular-free massive MIMO sensing integrated system, while simultaneously tracking point-like sensing targets from their reflected echoes. The device includes: a function construction module for calculating the downlink achievable rate of the communication user and the signal-to-noise ratio (SNR) of the reflected echo, constructing a problem function with the goal of maximizing the downlink rate of the communication user and the SNR of the reflected echo as a constraint; and a solution module for solving the constructed problem function to obtain the precoding scheme of the system.

[0113] Example 3

[0114] Based on the precoding method for a non-cellular massive MIMO sensor integrated system described in Embodiment 1, this embodiment provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the precoding method for the non-cellular massive MIMO sensor integrated system described in Embodiment 1.

[0115] Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, the methods can be tangibly embodied in a machine-readable storage medium having stored thereon instructions that can be used to program a computer to perform any of the methods. The software implementation can be initialized by loading and executing a set of instructions arranged to perform one of the methods into the computer's memory. Alternatively, hard-wired circuitry can be used in place of, or in combination with, software instructions. Thus, the

[0116] The present application is described in relation to flow diagrams and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It is understood that each block of the flow diagrams and / or block diagrams, and combinations of blocks in the flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more functions specified in one or more of the flow diagram or block diagram block or blocks. Figure 1 means for performing one or more of the functions specified in one or more of the flow diagram or block diagram block or blocks.

[0117] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in one or more of the flow diagram or block diagram block or blocks. Figure 1 means for performing one or more of the functions specified in one or more of the flow diagram or block diagram block or blocks.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in one or more of the flow diagram or block diagram block or blocks. Figure 1 means for performing one or more of the functions specified in one or more of the flow diagram or block diagram block or blocks.

[0119] The embodiments of the present application described above are merely illustrative and not limiting. Numerous modifications and adaptations will be apparent to those skilled in the art without departing from the spirit and scope of the present application.

Claims

1. A precoding method for a cellular-free massive MIMO integrated sensing system, characterized in that, The access point provides data services to communication users via the downlink of a non-cellular massive MIMO sensing integrated system, while simultaneously tracking point-like sensing targets from their reflected echoes; the method includes: Calculate the downlink achievable rate and signal-to-noise ratio of the reflected echo for the communication user. With the goal of maximizing the downlink speed and rate of the communication user and constrained by the signal-to-noise ratio of the reflected echo, construct a problem function. Solve the constructed problem function to obtain the precoding scheme of the system; The constructed problem function is as follows: ， in, Indicates the first The access point corresponds to the first... Precoded vectors for each communication user Indicates the downlink speed and rate of communication users. and They represent the first Actual power and maximum power constraints for each access point and These represent the signal-to-noise ratio of the reflected echo and the minimum threshold value to ensure sensing performance, respectively. Solving the constructed problem function includes: The constructed problem function is a rate maximization problem P1, which is solved using a weighted minimum mean square error algorithm. , , With three variables, the rate maximization problem P1 is equivalently transformed into the weighted sum mean square error minimization problem P2. ， in, This represents the trace operation. This indicates finding the determinant; The weighted and mean squared error minimization problem P2 is respectively for , , Both are convex optimization problems, solved using an alternating iterative optimization method, i.e., fixing... , The optimal precoding scheme is obtained by further solving problem P3. superscript To represent the optimal solution, problem P3 is represented as: 。 2. The precoding method for a cellular-free massive MIMO integrated sensing system according to claim 1, characterized in that, The non-cellular massive MIMO sensing integrated system includes: several access points uniformly distributed in a defined plane, several communication users, and a point-like sensing target; wherein, all access points are equipped with a set number of antennas, and all communication users are single-antenna devices.

3. The precoding method for a cellular-free massive MIMO integrated sensing system according to claim 2, characterized in that, The access point provides data services to communication users through the downlink of the non-cellular massive MIMO integrated sensing system, including: No. The access point and the first The wireless channel between communication users is the Rayleigh channel. , is represented as: ， Among them, subscript Indicates a communication system. Indicates the first The access point and the first Large-scale fading coefficients among individual communication users Indicates the first The access point and the first Small-scale fading vectors between communication users.

4. The precoding method for a cellular-free massive MIMO integrated sensing system according to claim 3, characterized in that, The access point tracks point-like sensing targets from reflected echoes, including: No. The wireless channel between each access point and the point-like sensing target is a direct link, and the target response matrix is... Represented as: ， Among them, subscript Indicates a sensing system, superscript This represents the conjugate transpose operation. It is the reflection coefficient, which includes the effects of path loss and the target's radar cross-section. and These are the point-like sensing targets relative to the first... The first access point and the first The azimuth angle of each access point and They are the first The first access point transmitting antenna and the first The steering vector of the receiving antenna at each access point.

5. The precoding method for a cellular-free massive MIMO integrated sensing system according to claim 4, characterized in that, The solution is obtained using an alternating iterative optimization method, including: Initialize precoding superscript The iteration count is used to satisfy the system power constraint, and the iteration termination error is set. Iteration variables ; Step 1, based on precoding Calculate in sequence , Two variables, return The expression is given by the objective function of problem P3, and let... ;definition superscript This indicates the transpose operation. Define as an auxiliary variable , unit array The Okay, and using a semidefinite relaxation algorithm, problem P3 is reconstructed into problem P4 to satisfy the definition of convex constraints. Problem P4 is expressed as: ， in, , , , , ; Step two: Using the CVX toolbox in Matlab software, solve problem P4, and decompose it. Get precoding Simultaneously, calculate the downlink speed and rate of communication users. ; Repeat steps one and two until... So far, the optimal precoding of the system has been obtained. .

6. A precoding device for a cellular-free massive MIMO integrated sensing system, characterized in that, The access point provides data services to communication users via the downlink of a non-cellular massive MIMO sensing integrated system, while simultaneously tracking point-like sensing targets from their reflected echoes; the device includes: The function construction module is used to calculate the downlink achievable rate and signal-to-noise ratio of the reflected echo for the communication user. The problem function is constructed with the downlink speed and rate maximization as the objective and the signal-to-noise ratio of the reflected echo as the constraint. The solver module is used to solve the constructed problem function to obtain the precoding scheme of the system; The constructed problem function is as follows: ， in, Indicates the first The access point corresponds to the first... Precoded vectors for each communication user Indicates the downlink speed and rate of communication users. and They represent the first Actual power and maximum power constraints for each access point and These represent the signal-to-noise ratio of the reflected echo and the minimum threshold value to ensure sensing performance, respectively. Solving the constructed problem function includes: The constructed problem function is a rate maximization problem P1, which is solved using a weighted minimum mean square error algorithm. , , With three variables, the rate maximization problem P1 is equivalently transformed into the weighted sum mean square error minimization problem P2. ， in, This represents the trace operation. This indicates finding the determinant; The weighted and mean squared error minimization problem P2 is respectively for , , Both are convex optimization problems, solved using an alternating iterative optimization method, i.e., fixing... , The optimal precoding scheme is obtained by further solving problem P3. superscript To represent the optimal solution, problem P3 is represented as: 。 7. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the precoding method for the non-cellular massive MIMO inductive system as described in any one of claims 1 to 5.

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