Precoding method and device for non-cellular large-scale MIMO (Multiple Input Multiple Output) general-sensing integrated system

Abstract

The invention discloses a pre-coding method and device for a cellular-free large-scale MIMO (Multiple Input Multiple Output) sensing integrated system, and the method comprises the steps: an access point provides a data service for a communication user through a downlink of the cellular-free large-scale MIMO sensing integrated system, and tracks a point-like sensing target from a reflection echo; the method comprises the following steps: calculating a downlink reachable rate of a communication user and a signal-to-noise ratio of a reflection echo, and constructing a problem function by taking maximization of the downlink sum rate of the communication user as a 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. According to the invention, on the premise of not introducing extra system energy consumption and guaranteeing the sensing performance, the trade-off relationship between the communication performance of the system and the display of the communication sensing performance can be greatly improved, so that the utilization efficiency of wireless spectrum resources is enhanced, and the integrity of the system is expanded and improved.

Description

Technical Field

[0001] The present invention 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 Art

[0002] The cell-free massive MIMO technology can use densely deployed access points to eliminate cell boundary limitations and effectively mitigate severe inter-cell interference in traditional massive MIMO systems. The communication and sensing integration technology aims to achieve sensing functions using "zero-added" wireless communication, that is, based on wireless communication, adding as few or no sensing modules as possible to achieve traditional sensing functions such as positioning and tracking, and comprehensively expand and enhance the overall performance of the system.

[0003] However, since communication and sensing are integrated on the same hardware platform and share spectrum resources, considering the limited power of real access points, there is a trade-off between communication and sensing functions, and in certain situations, the two show extreme opposition, seriously affecting the system performance.

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

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

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows: In the first aspect, a precoding method for a cell-free massive MIMO communication and sensing integrated system is provided. The access point provides data services to communication users through the downlink of the cell-free massive MIMO communication and sensing integrated system, and simultaneously tracks a point-like sensing target from its reflected echo; the method includes: calculating the downlink achievable rate of the communication user and the signal-to-noise ratio of the reflected echo, constructing a problem function with the maximization of the downlink sum rate of the communication user as the target and the signal-to-noise ratio of the reflected echo as the constraint; and solving the constructed problem function to obtain the precoding scheme of the system.

[0007] Furthermore, the cell-free massive MIMO integrated communication and sensing system includes: a number of access points evenly distributed in a set plane, a number of communication users, and a point-like sensing target; among them, all access points are equipped with a set number of antennas, and all communication users are single-antenna devices.

[0008] Furthermore, the access points provide data services to the communication users through the downlink of the cell-free massive MIMO integrated communication and sensing system, including: The th access point and the th communication user have a Rayleigh channel , expressed as: , where the subscript represents the communication system, represents the large-scale fading coefficient between the th access point and the th communication user, represents the small-scale fading vector between the th access point and the th communication user.

[0009] Furthermore, the access points track the point-like sensing target from the reflected echo, including: 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: , where the subscript represents the sensing system, the superscript represents the conjugate transpose operation, is the reflection coefficient, including the influence of path loss and the target radar cross section, and are respectively the azimuth angles of the point-like sensing target relative to the th access point and the th access point, and are respectively the steering vectors of the transmitting antenna of the th access point and the receiving antenna of the th access point.

[0010] Furthermore, the constructed problem function is: , where represents the precoding vector of the th access point corresponding to the th communication user, Denotes the downlink sum rate of communication users, and respectively denote the actual power and the maximum power constraint of the th access point, and respectively denote the signal-to-noise ratio of the reflected echo and the minimum threshold for ensuring the sensing performance.

[0011] Furthermore, solving the constructed problem function includes: The constructed problem function is the sum rate maximization problem (P1). By using the weighted minimum mean square error algorithm, introduce , , Three variables, and equivalently transform the sum rate maximization problem (P1) into the weighted sum mean square error minimization problem (P2); , wherein, Denotes the trace operation, Denotes the determinant; The weighted sum mean square error minimization problem (P2) is a convex optimization problem with respect to , , respectively. Solve it by using the method of alternating iteration optimization, that is, fix , , and obtain the optimal precoding scheme by further solving the problem (P3), where the superscript represents the optimal solution. The problem (P3) is expressed as: .

[0012] Furthermore, using the method of alternating iteration optimization to solve includes: 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 ; Step 1, according to the precoding , calculate , two variables in sequence, substitute the expression into the objective function of the problem (P3), and let ; Define , where the superscript represents the transpose operation, is an auxiliary variable, define , is the identity matrix The th row, and through the semi - definite relaxation algorithm, reconstruct problem (P3) into problem (P4) to satisfy the definition of convex constraints. Problem (P4) is expressed as: , where , , , , ; Step 2: Use the CVX toolbox in Matlab software to solve problem (P4), and obtain the precoding by decomposing . Meanwhile, calculate the downlink sum rate of the communication users; Loop through Step 1 and Step 2 until is reached, and obtain the optimal precoding of the system.

[0013] In a second aspect, a precoding device for a cell - free massive MIMO communication and sensing integrated system is provided. The access point provides data services to communication users through the downlink of the cell - free massive MIMO communication and sensing integrated system, and simultaneously tracks a point - like sensing target from its reflected echo. The device includes: a function construction module for calculating the downlink achievable rate of the communication users and the signal - to - noise ratio of the reflected echo, constructing a problem function with the maximization of the downlink sum rate of the communication users as the objective and the signal - to - noise ratio of the reflected echo as the constraint; a solution module for solving the constructed problem function to obtain the precoding scheme of the system.

[0014] In a third aspect, a computer program product is provided, including computer programs / instructions which, when executed by a processor, implement the steps of the precoding method for the cell - free massive MIMO communication and sensing integrated system described in the first aspect.

[0015] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention constructs a cell-free massive MIMO communication and sensing integrated system, where access points and communication users are evenly distributed in a plane, and there are point-like sensing targets in the system. The access points provide data services to communication users through the downlink, and at the same time track the point-like sensing targets from their reflected echoes, and calculate the downlink achievable rate of the communication users and the signal-to-noise ratio of the reflected echoes. Taking the maximization of the downlink sum rate of the communication users as the goal and the signal-to-noise ratio of the reflected echoes as the constraint, a problem function is constructed, and an optimal precoding design scheme for the system is obtained. Without introducing additional system energy consumption and ensuring sensing performance, the communication performance of the system can be significantly improved, and the trade-off relationship between communication and sensing performance can be demonstrated. In the cell-free massive MIMO communication and sensing integrated system, the precoding is optimized for the first time to enhance the utilization efficiency of wireless spectrum resources and comprehensively expand and improve the integrity of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 FIG. is a schematic diagram of the main implementation process of a precoding method for a cell-free massive MIMO communication and sensing integrated system provided by an embodiment of the present invention; Figure 2 FIG. is a graph showing the variation of the downlink sum rate with the number of access points in an embodiment of the present invention M changing; Figure 3 FIG. is the trade-off relationship between communication performance and sensing performance in an embodiment of the present invention; Figure 4 FIG. is the beam pattern corresponding to different numbers of antennas in an embodiment of the present invention N in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] 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 solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0018] Embodiment 1 A precoding method for a cell-free massive MIMO communication and sensing integrated system, where 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 at the same time tracks a point-like sensing target from its reflected echo; the method includes: calculating the downlink achievable rate of the communication user and the signal-to-noise ratio of the reflected echo, taking the maximization of the downlink sum rate of the communication user as the goal and the signal-to-noise ratio of the reflected echo as the constraint, constructing a problem function; solving the constructed problem function to obtain a precoding scheme for the system.

[0019] The main implementation process of the precoding method for a cell-free massive MIMO communication and sensing integrated system according to the present invention is as Figure 1 shown.

[0020] S1: Construct a cell-free massive MIMO communication and sensing integrated system, which includes access points, communication users, and point-like sensing targets. In the embodiment of the present invention, the constructed cell-free massive MIMO communication and sensing integrated system includes: M access points and K users, all of which are uniformly distributed in a plane; and, a point-like sensing target; where all access points are equipped with N antennas, and all communication users are single-antenna devices.

[0021] Exemplarily, there is a cell-free massive MIMO communication and sensing integrated system with access points, users are uniformly distributed in a square plane area with a side length of 1 kilometer, and there is a point-like sensing target in the system; where all access points are equipped with N antennas, and all communication users are single-antenna devices; where the transmission power of the access point or . It should be noted that for the convenience of later simulation, any one of the access points can be placed at the center of the square area, and the point-like sensing target can be placed at the 0° azimuth of this access point.

[0022] S2: The access point provides data services to the communication users through the downlink, and at the same time tracks the point-like sensing target from its reflected echo. In the embodiment of the present invention, the access point provides data services to the communication users through the downlink, including: The wireless channel between the th access point and the th communication user is a Rayleigh channel, which is expressed as: , where the subscript represents the communication system, represents the large-scale fading coefficient between the th access point and the th communication user, represents the small-scale fading vector between the th access point and the th communication user.

[0023] It should be noted that the large-scale fading is modeled as , where is a log-normal random variable with a standard deviation = 8db, is the the distance between a communication user and the th access point, where = 3.8 is the path loss exponent.

[0024] In an embodiment of the present invention, the access point tracks a point-like sensing target from the reflected echo, including: 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: , where the subscript represents the sensing system, and the superscript represents the conjugate transpose operation, is the reflection coefficient, including the influence of path loss and the target radar cross section, and are respectively the azimuth angles of the point-like sensing target relative to the th access point and the th access point, and are respectively the steering vectors of the transmitting antenna of the th access point and the receiving antenna of the th access point.

[0025] It should be noted that here the access point preferably adopts a uniform linear array with a spacing of half a wavelength, and the steering vector is expressed as: , where the superscript T represents the transpose operation.

[0026] S3: Calculate the downlink achievable rate of the communication user and the signal-to-noise ratio of the reflected echo, and construct a problem function with the maximization of the downlink sum rate of the communication user as the objective and the signal-to-noise ratio of the reflected echo as the constraint; In an embodiment of the present invention, calculating the downlink achievable rate of the communication user includes: The integrated signal M transmitted by the th access point is modeled as: , where represents the transmission precoding matrix of the th access point, is the th column of, represents the data symbol vector sent by the access point to the communication user, satisfying , and the data symbols between different communication users Independent of each other.

[0027] Therefore, the received signal of the th communication user is expressed as: , where represents the Gaussian white noise received by the th communication user.

[0028] Thus, the downlink achievable rate of the communication user can be calculated and is expressed as: , where can be expressed as: , In the embodiment of the present invention, calculating the signal-to-noise ratio of the reflected echo includes: Installing a radar receiver at each AP to preliminarily process the reflected echo signal, then the output of the radar receiver can be expressed as: , where represents 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. Therefore, the optimal can be calculated as: , All APs transmit the output signal of the radar receiver to the CPU through a lossless backhaul link, which is specifically expressed as: , Therefore, the signal-to-noise ratio of the reflected echo can be calculated and is expressed as: , where represents the trace operation, and the standard deviation takes a value of 0.1.

[0029] In the embodiment of the present invention, aiming at maximizing the downlink sum rate of the communication user and taking the signal-to-noise ratio of the reflected echo as a constraint, a problem function is constructed, including: aiming at maximizing the sum rate of the communication user, ensuring the intensity of the reflected echo signal and considering the system power constraint, finding an optimal precoding design method, and constructing the problem function (P1), which is expressed as: , where represents the th access point corresponding to the The precoding vector of a communication user represents the downlink sum rate of the communication user 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 for ensuring the sensing performance

[0030] S4: Solve the problem function through the weighted minimum mean square error algorithm and the CVX toolbox in Matlab software, and adopt the Gaussian randomization method to restore the optimal design method of the system precoding

[0031] In the embodiment of the present invention, the optimal design method of the system precoding is restored by solving the problem function through the weighted minimum mean square error algorithm and the CVX toolbox in Matlab software, and adopting the Gaussian randomization method, including Through the weighted minimum mean square error algorithm, by introducing , , The three variables, the original sum rate maximization problem (P1) is equivalently transformed into a weighted sum mean square error minimization problem (P2) Specifically , , The three variables are expressed as , , , In the formula , ; Specifically, the weighted sum mean square error minimization problem (P2) is expressed as , It should be noted that the weighted sum mean square error minimization problem (P2) is a convex optimization problem for , , Therefore, the alternating iteration optimization method can be used to solve it, that is, fix , , and obtain the optimal precoding scheme by further solving the problem (P3). The problem (P3) is expressed as , Specifically, the alternating iteration optimization method includes 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 ; Specifically, the iteration termination error can take the value of 10 -3 or be customized according to actual needs.

[0032] Step 1, according to the precoding , calculate , two variables in sequence, and substitute the expression into the objective function of problem (P3). To further simplify the expression, define , where the superscript represents the transpose operation, is an auxiliary variable, , is the th row of the identity matrix , where, , , , , ; Step 2, use the CVX toolbox in Matlab software to solve problem (P4), and decompose through the Gaussian randomization method to obtain the precoding , which is specifically described as: First, perform eigenvalue decomposition on the positive semi - definite matrix to obtain . Then, randomly generate groups of Gaussian random vectors with a standard normal distribution, construct the corresponding , and select the that can maximize the objective function of problem (P4). Finally, through normalizing the auxiliary variable in , take the first N terms to obtain the precoding , and calculate the downlink sum rate of the communication users; loop through Step 1 and Step 2 until is reached to obtain the optimal precoding of the system.

[0033] It should be noted that the precoding scheme after optimized design can effectively improve the downlink and rate of the system; and in the optimization process, by adjusting the minimum threshold value of the perception performance, the trade-off between communication and perception performance in the non-cellular large-scale MIMO communication perception integrated system can be intuitively demonstrated.

[0034] Figure 2 , Figure 3 and Figure 4 The system downlink and rate change with the number of access points under the precoding design method of the non-cellular massive MIMO communication sensing integrated system. M Graph of changes, trade-offs between communication performance and perception performance, and different numbers of antennas N The corresponding beam pattern.

[0035] exist Figure 2 The accuracy and convergence of the results of the present invention are verified. M The downlink and rate of the system under the condition of is compared. It can be seen from the figure that in the iterative process, the downlink and rate of the system can be effectively increased, and the convergence of the proposed weighted minimum mean square error algorithm is also guaranteed. In addition, it can be found that as the number of access points in the area increases, the downlink and rate of the system also increase. This is 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 degree of freedom of the system is also improved accordingly, and the system can transmit more data streams at the same time to achieve spatial multiplexing gain. Therefore, under the condition that the perception constraint remains unchanged, increasing the number of access points can effectively realize the synergy of multiple gain mechanisms, improve the signal-to-noise ratio of the transmission signal under the same bandwidth conditions, thereby improving the channel capacity of the system and supporting higher communication data transmission rates.

[0036] exist Figure 3 The trade-off between downlink rate and perceived signal-to-noise ratio constraints for different numbers of communicating users is clearly shown in Figure 2. The results show that the system performance trade-off boundary can be obtained by and The two boundary points are divided into three parts: communication constraints, dual constraints and perception constraints. On the left, the system is under communication constraints, and the performance is mainly limited by the communication capacity. At this time, the system downlink and rate reach the maximum value. , while the perceived signal-to-noise ratio Relatively low, suitable for scenarios with high communication rate requirements and relatively low perception accuracy requirements, such as high-speed data transmission. and The system is under dual constraints, and is subject to dual constraints of communication and perception. and perceived signal-to-noise ratio Both are at the intermediate level. The system needs to make a trade-off between the two, and the system resources need to be reasonably allocated between communication and sensing tasks to achieve a balance between the two. It is applicable to scenarios with certain requirements for both communication and sensing, such as the integrated communication and sensing system of drones, which needs to ensure the performance of data transmission and target detection simultaneously; at the demarcation point On the right side, the system is under sensing constraints, and the performance of the system is mainly limited by the sensing ability. At this time, the sensing signal-to-noise ratio reaches the maximum value , while the downlink sum rate is relatively low. It is applicable to scenarios with high requirements for sensing accuracy and relatively low requirements for communication rate, such as high-precision radar detection. Taking the case where the number of communication users K =5 and the transmit power of the access point =20dB as an example, the demarcation points of the system performance trade-off boundary are (8.64, 15) and (7.35, 18) respectively. It should be noted that the boundary points dividing the system performance trade-off boundary into three parts cannot be calculated by a fixed formula.

[0037] In Figure 4 , the optimized beam patterns of access points with different numbers of antennas are shown. The present invention defines the optimized beam pattern of the th access point as , where is the optimal receiver vector of the rd AP given by S3 to maximize the sensing signal-to-noise ratio. Figure 4 shows that the main beam is located at the azimuth angle of the sensing target. When the number of AP antennas increases, from the perspective of the radar, the performance of the beam pattern becomes better. Specifically, the maximum side lobe ratio decreases with the increase in the number of radar antennas, and the main beam width also narrows with the increase in the number of AP antennas. This is because more antennas can provide higher array gain, enabling the beam to point more precisely in the target direction, thereby improving the directivity and gain of the beam and the spatial resolution of the system, making the main lobe of the beam pattern narrower and stronger.

[0038] Embodiment 2 Based on the precoding method of a cell-free massive MIMO communication and sensing integrated system described in Embodiment 1, this embodiment provides a precoding device for a cell-free massive MIMO communication and sensing integrated system. The access point provides data services to communication users through the downlink of the cell-free massive MIMO communication and sensing integrated system, and simultaneously tracks point-like sensing targets from its reflected echoes. The device includes: a function construction module, configured to calculate the downlink achievable rate of communication users and the signal-to-noise ratio of the reflected echoes, construct a problem function with the maximization of the downlink sum rate of the communication users as the objective and the signal-to-noise ratio of the reflected echoes as the constraint; a solution module, configured to solve the constructed problem function to obtain the precoding scheme of the system.

[0039] Embodiment 3 Based on the precoding method of a cell-free massive MIMO communication and sensing integrated system described in Embodiment 1, this embodiment provides a computer program product, including computer programs / instructions, which when executed by a processor, implement the steps of the precoding method of the cell-free massive MIMO communication and sensing integrated system described in Embodiment 1.

[0040] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0041] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0042] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions in the flow Figure 1One or more processes and / or boxes Figure 1 The functions specified in one or more boxes.

[0043] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes.

[0044] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the claims of the present invention. All of these are within the protection scope of the present invention.

Claims

1. A precoding method for a non-cellular massive MIMO interaceptive integrated system, characterized in that: The access point provides data services to communication users through the downlink of the non-cellular large-scale MIMO interawareness integrated system, and simultaneously tracks the point-like sensing target from its reflected echo; the method comprises: Calculate the downlink achievable rate of the communication user and the signal-to-noise ratio of the reflected echo, take maximizing the downlink sum rate of the communication user as the goal, and take the signal-to-noise ratio of the reflected echo as the constraint to construct a problem function; Solve the constructed problem function to obtain a precoding solution for the system.

2. The precoding method of the non-cellular massive MIMO synaesthesia integrated system according to claim 1, characterized in that: The non-cellular large-scale MIMO interaceptive integrated system includes: a number of access points evenly distributed in a set plane, a number of communication users and a point-shaped 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 of the non-cellular massive MIMO synaesthesia integrated system according to claim 2, characterized in that: The access point provides data services to communication users through the downlink of the cell-free massive MIMO interawareness integrated system, including: No. The access point and The wireless channel between the communicating users is a Rayleigh channel. , expressed as: ， Among them, the subscript represents the communication system, Indicates The access point and The large-scale fading coefficient between communicating users, Indicates The access point and The small-scale fading vectors between the communicating users.

4. The precoding method of the non-cellular massive MIMO synaesthesia integrated 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 the access point and the point-like sensing target is a direct link, and the target response matrix It is expressed as: ， Among them, the subscript represents the perception system, the superscript represents the conjugate transpose operation, is the reflection coefficient, including the effects of path loss and target radar cross section, and They are the point-like perceived target relative to the access point and The azimuth of the access point, and They are The access point transmit antenna and The steering vector of each access point receive antenna.

5. The precoding method of the non-cellular massive MIMO synaesthesia integrated system according to claim 4, characterized in that: The problem function constructed is: ， in, Indicates The access point corresponds to The precoding vector of each communicating user is Indicates the downlink and rate of the communication user, and Respectively represent The actual power and maximum power constraints of each access point, and They respectively represent the signal-to-noise ratio of the reflected echo and the minimum threshold to ensure perception performance.

6. The precoding method of the non-cellular massive MIMO synaesthesia integrated system according to claim 5, characterized in that: Solving the constructed problem function includes: The problem function constructed is the sum rate maximization problem (P1), and the weighted minimum mean square error algorithm is introduced. , , Three variables, which convert the sum rate maximization problem (P1) into a weighted sum mean square error minimization problem (P2); ， in, represents the trace operation, It means to find the determinant; The weighted and mean square error minimization problems (P2) are respectively , , They are all convex optimization problems, which are solved by alternating iterative optimization method, that is, fixed , , by further solving problem (P3) we can get the optimal precoding scheme , where the superscript represents the optimal solution, and problem (P3) is expressed as: 。 7. The precoding method of the non-cellular massive MIMO synaesthesia integrated system according to claim 6, characterized in that: The solution is solved by using the alternating iterative optimization method, including: Initialize precoding , where the superscript Indicates the number of iterations to meet the system power constraint and set the iteration termination error , iteration variable ; Step 1: According to the precoding , calculate successively , Two variables, back The objective function of problem (P3) is expressed as ;definition , where the superscript represents the transpose operation, As auxiliary variables, define , Unit matrix No. And through the semi-positive relaxation algorithm, problem (P3) is reconstructed into problem (P4) to satisfy the definition of convex constraints. Problem (P4) is expressed as: ， in, , , , , ; Step 2: Use the CVX toolbox in Matlab to solve problem (P4) and decompose it into Get precoding , at the same time, calculate the communication user downlink and rate ; Repeat steps 1 and 2 until So far, the optimal precoding of the system is obtained .

8. A precoding device for a non-cellular massive MIMO interaceptive integrated system, characterized in that: The access point provides data services to communication users through the downlink of the non-cellular large-scale MIMO interawareness integrated system, and tracks the point-like sensing target from its reflected echo; the device includes: A function construction module, used for calculating the downlink achievable rate of the communication user and the signal-to-noise ratio of the reflected echo, taking the maximization of the downlink and rate of the communication user as the goal and taking the signal-to-noise ratio of the reflected echo as the constraint, to construct a problem function; The solution module is used to solve the constructed problem function to obtain a precoding solution for the system.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the precoding method of the non-cellular massive MIMO synaesthesia integrated system described in any one of claims 1 to 8 are implemented.

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