Beam forming method of non-cellular large-scale MIMO (Multiple Input Multiple Output) flux-sensing integrated system based on near field

Abstract

The invention discloses a beam forming method of a non-cellular large-scale MIMO (Multiple Input Multiple Output) sensing integrated system based on a near field, which comprises the following steps of: acquiring model information, and calculating a sum rate of a user and sensing power from an access point to a sensing target direction, the model information is model information of a downlink channel model and a radar sensing target model of the constructed non-cellular large-scale MIMO sensing integrated system based on the near field; the sum rate of users is maximized by optimizing a transmitting beam forming matrix of an access point by taking the transmitting power of the access point and the sensing power from the access point to a sensing target direction as constraints. According to the invention, the sum rate of the communication-inductance integrated system can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication, and particularly relates to a beamforming method for a cell-free massive MIMO communication and sensing integrated system based on near field. Background Art

[0002] In recent years, with the wide deployment of the Fifth Generation Wireless Communications (5G) globally, the research on the Sixth Generation Wireless Communications (6G) for 2030 and beyond has been fully launched. The 6G network aims to support lower latency, higher data rates, and more diverse user terminals, and can support various emerging applications, including intelligent healthcare, autonomous driving, industrial control, etc. However, with the continuous expansion of application scenarios and the continuous growth of the number of users, the traditional communication architecture faces many challenges. In this context, the concept of cell-free massive Multiple-Input Multiple-Output (MIMO) has emerged, providing new ideas and directions for the further development of wireless communication.

[0003] Cell-free massive MIMO breaks the architecture of traditional cellular networks. Each user equipment is served by a large number of distributed access points (APs) connected to a central processing unit (CPU), effectively eliminating the limitations of cell boundaries and improving the network coverage and capacity. Multiple access points work together to better adapt to the needs of different users and provide more stable and high-speed communication services. With the continuous development of ultra-large-scale antenna communication and radar, millimeter-wave communication and radar technology, the technical characteristics, channel characteristics, and application scenarios of communication and sensing are becoming more and more similar, showing a development trend of institutional integration. Moreover, the development of emerging services such as intelligentization, immersion, and digital twin has continuously increased the demand for high-precision detection, positioning, identification, imaging of targets and large-bandwidth, low-latency information transmission, promoting the in-depth research of communication and sensing integrated technology.

[0004] The integration of communication and sensing functions combines communication and sensing capabilities. By sharing physical resources such as hardware, spectrum, and signals in the same system, it is possible to sense the state information of target objects, such as their orientation, distance, and speed, while transmitting information at a relatively low cost, and obtain fusion gain and cooperation gain. Cell-free massive MIMO can utilize this concept to achieve efficient communication while obtaining sensing information about the surrounding environment through its distributed access points. For example, by analyzing the characteristics of signal reflection, scattering, etc., it is possible to sense the user's location, movement speed, and the surrounding environment. This integration can not only further optimize the communication link and improve communication quality but also provide richer functions and services for numerous application scenarios such as intelligent transportation and smart homes. The integration of communication and sensing is an important direction for the future development of 6G, bringing broader application prospects and innovation space for wireless communication.

[0005] The cell-free massive MIMO communication and sensing integrated system has characteristics such as good performance, strong anti-interference ability, and stable service. However, most of the existing systems are modeled based on the far field. In recent years, traditional antenna arrays have been evolving towards ultra-large-scale arrays and are expected to be applied in the mid-band and millimeter-wave bands. Compared with traditional antenna arrays, the significant increase in the equivalent aperture of large-scale array antennas and the introduction of cell-free massive MIMO systems have made the distance between users and antennas very close. Therefore, the probability of users being in the near field is higher, and the near-field effect becomes more obvious and prominent. The near-field effect refers to the fact that at a specific location, the channel parameters vary with the position of the array elements in the antenna array. That is to say, the assumption that the electromagnetic wave transmission wavefront is a plane wave no longer holds, and it needs to be modeled as a non-plane wavefront. In traditional channel models, it is assumed that the wavefront of the electromagnetic wave arriving at all elements in the antenna panel is a plane wave, which cannot reflect the propagation characteristics under the near-field effect. Therefore, in order to more accurately evaluate the communication performance, it is particularly important to study the communication performance in the near-field case. Summary of the Invention

[0006] To address the deficiencies in the prior art, the present invention provides a beamforming method for a cell-free massive MIMO communication and sensing integrated system based on the near field, which can effectively improve the sum rate of the communication and sensing integrated system.

[0007] To achieve the above objective, the technical solution adopted by the present invention is: In a first aspect, a beamforming method for a near-field-based cell-free massive MIMO integrated communication and sensing system is provided, including: obtaining model information, and calculating the sum rate of users and the sensing power in the direction from the access point to the sensing target, where the model information is the model information of the downlink channel model and the radar sensing target model of the constructed near-field-based cell-free massive MIMO integrated communication and sensing system; maximizing the sum rate of users by optimizing the transmit beamforming matrix of the access point with the transmit power of the access point and the sensing power in the direction from the access point to the sensing target as constraints.

[0008] Further, the near-field-based cell-free massive MIMO integrated communication and sensing system includes: a plurality of access points, users, and sensing targets; each access point is connected to a CPU, and the users and sensing targets are respectively in communication and sensing connection with the access point; each access point is an integrated communication and sensing access point and is configured with a number of transmit / receive dual-functional antennas, and each user is configured with a single antenna.

[0009] Further, constructing the downlink channel model and the radar sensing target model of the near-field-based cell-free massive MIMO integrated communication and sensing system includes: Modeling the downlink channel model under near-field conditions. The formula for the near-field spherical wave channel model between the access point and the user is: (3) where represents the near-field spherical wave channel model between the m th access point and the k th user; P represents the number of near-field non-line-of-sight paths of the k th user. When l = 0, represents the LoS path complex gain of the k th user. When , represents the complex gain of the l rd NLoS path; represents the steering vector between the m th access point and the k th user in the near field, represents the steering vector between the m th access point and the l th path in the near field, represents the steering vector between the m th access point and the l th path in the near field; Define the joint sensing and communication signal model for downlink transmission. The access points jointly transmit K communication streams and Q sensing streams.T = K + Q At the m th access point, the transmitted signal is expressed as: (4) where and respectively represent the communication beamforming matrix and the sensing beamforming matrix; represents the communication symbol vector; represents the sensing symbol vector, assuming they are statistically independent of each other; The overall beamforming matrix is defined as and the transmitted symbol vector is defined as that is ; The received signal of the k th user, the specific formula is: (6) where represents the signal received by user k from all access points, represents the channel state information vector from the m th access point to the k th user, represents the channel state information vector from all access points to the k th user, H represents the conjugate transpose; represents the transmit beamforming vector from the m th access point to the k th user, represents the transmit beamforming vector from all access points to the k th user, represents the additive white Gaussian noise at user k , which follows a complex Gaussian distribution with a mean of zero and a variance of , represents the communication symbols transmitted by all access points to user k ; represents the communication or sensing symbols transmitted by all access points to other users or the target, represents the transmit beamforming vector from all access points to other users or the target.

[0010] Furthermore, calculating the sum rate of the user includes: Calculating the signal-to-interference-plus-noise ratio k of the th user, the specific formula is: (7) Among them, represents the modulus operation on complex numbers; Calculate the sum rate of the users R , and the specific formula is: (8) Among them, represents the k th user's rate.

[0011] Furthermore, calculate the sensing power in the direction from the access point to the sensing target, and the specific formula is: (9) Among them, represents the sensing power in the direction from the access point to the sensing target.

[0012] Furthermore, with the transmit power of the access point and the sensing power in the direction from the access point to the sensing target as constraints, maximize the sum rate of the users by optimizing the transmit beamforming matrix of the access point, including: Set the optimization problem of maximizing the sum rate of the users as P0, and the specific formula is: (10) Among them, represents to ensure the minimum sensing performance required by the access point, represents the total transmission power; Solve the optimization problem through the FP fractional programming method to obtain the optimal solutions of the access point beamforming matrix and the user sum rate.

[0013] Furthermore, solve the optimization problem through the FP fractional programming method, including: First, use the Lagrangian dual transformation for the optimization problem P0, move the SINR term outside the logarithm, and by introducing the auxiliary variable , the equivalent specific optimization problem P1 obtained is expressed as: (11) Then, transform the fractional term into the form of polynomial addition and subtraction by the quadratic transformation theorem, and by introducing the auxiliary variable , the optimization problem P1 is re-expressed as the optimization problem P2: (12).

[0014] Furthermore, fix the access point beamforming vector in the optimization problem P2, optimize the introduced auxiliary variables, and obtain the optimal values of the auxiliary variables and , and then substitute the optimized auxiliary variables into the objective function of optimization problem P2 and ignore the constant term to obtain optimization problem P3, which is expressed as: (15a) (15b) (15c) Since constraint (15b) is a non-convex constraint, the MM algorithm is used to process it to make it a convex constraint, and the lower bound of the constraint term is obtained: (16) where, represents the solution of in the previous iteration. Therefore, optimization problem P3 is transformed into optimization problem P4: (15a) (17b) (15c) where, , and the above problem is a convex problem. The CVX toolbox is used to solve optimization problem P4 to obtain the optimized solutions of the access point beamforming matrix and the user sum rate.

[0015] In a second aspect, a beamforming device for a near-field-based cell-free massive MIMO communication and sensing integrated system is provided, including: a data processing module, configured to obtain model information and calculate the sum rate of users and the sensing power in the direction from the access point to the sensing target, where the model information is the model information of the downlink channel model and the radar sensing target model of the constructed near-field-based cell-free massive MIMO communication and sensing integrated system; an optimization module, configured to maximize the sum rate of users by optimizing the transmit beamforming matrix of the access point with the transmit power of the access point and the sensing power in the direction from the access point to the sensing target as constraints.

[0016] In a third aspect, a computer program product is provided, including computer programs / instructions, and when the computer programs / instructions are executed by a processor, the steps of the beamforming method for the near-field-based cell-free massive MIMO communication and sensing integrated system described in the first aspect are implemented.

[0017] Compared with the prior art, the beneficial effects achieved by the present invention: (1) The present invention calculates the sum rate of the user and the sensing power in the direction from the access point to the sensing target according to the model information, where the model information is the model information of the downlink channel model of the near-field-based cell-free massive MIMO communication and sensing integrated system and the radar sensing target model; with the transmission power of the access point and the sensing power in the direction from the access point to the sensing target as constraints, the sum rate of the user is maximized by optimizing the transmit beamforming matrix of the access point; the present invention can effectively improve the sum rate of the communication and sensing integrated system, and thus can effectively improve the signal transmission efficiency and environmental sensing ability. (2) The present invention considers combining the cell-free massive MIMO system and the communication and sensing integrated technology in the near-field environment, giving play to the advantages of the communication and sensing integrated technology in user communication and target sensing, and at the same time considering channel modeling in the near-field environment, and jointly designing beamforming to maximize the user sum rate while ensuring the sensing service quality. (3) Through the combination of near-field modeling and massive MIMO technology, and through accurate channel modeling and optimization algorithms, the present invention can effectively improve the signal transmission efficiency and environmental sensing ability, providing a more efficient and intelligent solution for the next-generation communication system. Brief Description of the Drawings

[0018] Figure 1 is a schematic diagram of the main process of a beamforming method for a near-field-based cell-free massive MIMO communication and sensing integrated system provided by an embodiment of the present invention; Figure 2 is a model diagram of a near-field-based cell-free massive MIMO communication and sensing integrated system in an embodiment of the present invention; Figure 3 is a comparative simulation diagram of an embodiment of the present invention. Specific Embodiments

[0019] The present invention will be further described below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be used to limit the protection scope of the present invention.

[0020] Embodiment 1 A beamforming method for a near-field-based cell-free massive MIMO communication and sensing integrated system, including: obtaining model information, and calculating the sum rate of the user and the sensing power in the direction from the access point to the sensing target, where the model information is the model information of the downlink channel model of the near-field-based cell-free massive MIMO communication and sensing integrated system and the radar sensing target model; with the transmission power of the access point and the sensing power in the direction from the access point to the sensing target as constraints, the sum rate of the user is maximized by optimizing the transmit beamforming matrix of the access point.

[0021] Build the downlink channel model and radar sensing model of a near-field-based cell-free massive MIMO communication and sensing integrated system.

[0022] As Figure 1 , Figure 2 shown, the downlink communication model of the near-field-based cell-free massive MIMO communication and sensing integrated system includes multiple communication data streams for downlink communication through an AP and the desired received signal of the communication data stream. The near-field-based cell-free massive MIMO communication and sensing integrated system includes: M communication and sensing integrated APs (access points), K users, a sensing target, and S scatterers; each AP is connected to a CPU, and the users, sensing target, and scatterers are respectively in communication connection with the AP; each AP is equipped with transmit / receive dual-functional antennas, and each user is configured with a single antenna. Without loss of generality, assume that the AP antenna array is a uniform linear array (ULAs), and the spacing between antennas is d . The origin of the coordinate system is located at the center of the ULAs. For simplicity, assume that all APs have digital beamforming capabilities, that is, each antenna element has a dedicated radio frequency chain. is the signal wavelength. Assume that the k st user is located at , the n th antenna is located at , is the distance from the k st user to the m rd AP, and is the angle between the k st user and the m rd AP. Therefore, the distance from the k st user to the m rd AP on the n th antenna is: (1) where represents the distance from the k st user to the m th antenna on the n th AP; Then the steering vector in the near field can be obtained: (2) where represents the steering vector between the m th AP and the k st user in the near field; Model the downlink channel model under near-field conditions. The specific formula for the near-field spherical wave channel model between the AP and the user is as follows: (3) Where, represents the near-field spherical wave channel model between the m th access point and the k th user; P represents the number of near-field non-line-of-sight paths of the k th user. When l = 0, represents the complex gain of the near-field LoS (Line-of-Sight) path from the k th user. When , represents the complex gain of the l th NLoS (Non-Line-of-Sight) path; represents the steering vector between the m th access point and the k th user in the near field, represents the steering vector between the m th access point and the l th path in the near field, represents the steering vector between the m th access point and the l th path in the near field; To simultaneously achieve satisfactory communication and radar sensing performance, define the joint sensing and communication signal model for downlink transmission. The AP jointly transmits K communication streams and Q sensing streams, T = K + Q The signal m transmitted by the th access point is expressed as: (4) Where, and represent the communication beamforming matrix and the sensing beamforming matrix respectively; represents the communication symbol vector, satisfying ; represents the sensing symbol vector, satisfying , where represents taking the mathematical expectation. Assuming they are statistically independent of each other, i.e., . For the sake of simplicity, the overall beamforming matrix is defined as , and the transmitted symbol vector is defined as , that is .

[0023] The k th user receives signals from the m th AP. The specific formula can be expressed as follows: (5) where represents the signal received by user k from the m th AP, represents the conjugate transpose, represents the additive white Gaussian noise at user k , which follows a complex Gaussian distribution with a mean of zero and a variance of , denoted as ; The received signal of the k th user, the specific formula is: (6) where represents the signals received by user k from all access points, represents the channel state information vector from the m th access point to the k th user, represents the channel state information vector from all access points to the k th user, H represents the conjugate transpose; represents the transmit beamforming vector from the m th access point to the k th user, represents the transmit beamforming vector from all access points to the k th user, represents the communication symbols transmitted by all access points to user k ; represents the communication or sensing symbols transmitted by all access points to other users or targets, represents the transmit beamforming vector from all access points to other users or targets.

[0024] Based on the obtained channel model information, calculate the sum rate of the integrated communication and sensing transmission system and the sensing power in the direction from the AP to the target.

[0025] Calculate the signal-to-interference-plus-noise ratio k of the th user. The specific formula is: (7) where Denotes the modulus operation on complex numbers; Calculate the sum rate of users R , and the specific formula is: (8) Where, Denotes the rate of the k -th user.

[0026] Calculate the sensing power in the direction from the AP to the sensing target, and the specific formula is: (9) Where, Denotes the sensing power in the direction from the access point to the sensing target.

[0027] With the transmit power of the AP and the sensing power in the direction from the AP to the sensing target as constraints, optimize the transmit beamforming matrix of the AP to maximize the sum rate of users.

[0028] Set the optimization problem of maximizing the sum rate of users as P0, and the specific formula is: (10) Where, Denotes to ensure the minimum sensing performance required by the access point, Denotes the total transmit power; under the constraints of transmit power and minimum sensing performance requirements, optimize the AP transmit beamforming with the goal of maximizing the communication sum rate.

[0029] Solve the optimization problem through the FP fractional programming method to obtain the optimal solutions of the AP beamforming matrix and the user sum rate.

[0030] For the optimization problem P0, first use the Lagrangian dual transformation to move the SINR term outside the logarithm, and by introducing the auxiliary variable , the equivalent specific optimization problem P1 is expressed as: (11) Then transform the fractional term in it into the form of polynomial addition and subtraction by the quadratic transformation theorem, and by introducing the auxiliary variable , the optimization problem P1 is re-expressed as the optimization problem P2: (12) Fix the AP beamforming vector in the optimization problem P2, optimize the introduced auxiliary variables, and obtain the optimal values and of: (13) (14) Then, substitute the optimized auxiliary variables into the objective function of optimization problem P2 and ignore the constant terms to obtain optimization problem P3, which is expressed as: (15a) (15b) (15c) Since constraint (15b) is a non-convex constraint, use the MM algorithm to process it to make it a convex constraint, and obtain the lower bound of the constraint term: (16) where represents the solution of in the previous iteration. Therefore, optimization problem P3 is transformed into optimization problem P4: (15a) (17b) (15c) where , the above problem is a convex problem. Use the CVX toolbox to solve optimization problem P4 to obtain the optimized solutions of the access point beamforming matrix and the user sum rate.

[0031] Let , optimization problem P3 can be further expressed as problem P5: (18a) (18b) (18c) where the matrix can be simplified as follows: (19a) (19b) (19c) where . The lower bound of the constraint term is further expressed as: (20) where represents the solution of in the previous iteration. Therefore, optimization problem P5 can be expressed as: (18a) (21b) (18c) Among them, the above problem is a convex problem. Therefore, the CVX toolkit can be used to solve the above optimization problem, and the optimized beamforming matrix can be obtained.

[0032] As Figure 2 shown below, an example of implementing the present invention on a computer using MATLAB is given. The near-field-based cell-free massive MIMO communication and sensing integrated system in the embodiment of the present invention includes a CPU, K users, L sensing targets, and M APs. Among them, the AP is a uniform linear array with transmitting and receiving antennas, and each user is configured with a single antenna. In the simulation example, a two-dimensional Cartesian coordinate system in meters is considered. Among them, the APs are distributed on the y-axis coordinate system, and the users and sensing targets are located at (20, 50), (60, 50), and (35, 50) respectively. The scatterers on the path are randomly distributed between the users, sensing targets, and APs. The channel modeling between the users and the APs adopts the spherical wave model in the near-field case, and the superposition of the Los path and the NLos path is considered. Other parameter settings are as follows: noise power , transmit power .

[0033] As Figure 3 shown, a comparison diagram of the method of the present invention and the traditional beamforming method. As can be seen from the figure, in the case of the same system, the system sum rate obtained by the method of the present invention is significantly higher than that of the traditional beamforming scheme.

[0034] Embodiment 2 Based on the beamforming method of a near-field-based cell-free massive MIMO communication and sensing integrated system described in Embodiment 1, this embodiment provides a beamforming device for a near-field-based cell-free massive MIMO communication and sensing integrated system, including: A data processing module, configured to obtain model information and calculate the sum rate of users and the sensing power in the direction from the access point to the sensing target. The model information is the model information of the downlink channel model and the radar sensing target model of the constructed near-field-based cell-free massive MIMO communication and sensing integrated system; An optimization module, configured to maximize the sum rate of users by optimizing the transmit beamforming matrix of the access point with the transmit power of the access point and the sensing power in the direction from the access point to the sensing target as constraints.

[0035] Embodiment 3 Based on the beamforming method of a near-field-based 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 beamforming method of the near-field-based cell-free massive MIMO communication and sensing integrated system described in Embodiment 1.

[0036] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. 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 memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0037] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also 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, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0038] 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, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the specified functions in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0039] 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. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0040] 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 of the present invention and the scope protected by the claims. All of these fall within the protection scope of the present invention.

Claims

1. A beamforming method for a near-field-based non-cellular massive MIMO interaceptive integrated system, characterized in that: include: Acquire model information, and calculate the user's sum rate and the perception power in the direction from the access point to the perception target, wherein the model information is the downlink channel model of the near-field-based non-cellular massive MIMO interaceptive integration system and the model information of the radar perception target model; Taking the transmit power of the access point and the sensing power from the access point to the sensing target as constraints, the user's sum rate is maximized by optimizing the transmit beamforming matrix of the access point.

2. The beamforming method of the near-field-based non-cellular massive MIMO synaesthesia integrated system according to claim 1, characterized in that: The near-field-based non-cellular large-scale MIMO interaceptive integration system includes: a plurality of access points, users and sensing targets; each access point is connected to a CPU, and the user and the sensing target communicate and sense the connection with the access point respectively; each access point is an interaceptive integration access point and is configured with a plurality of transmitting / receiving dual-function antennas, and each user is configured with a single antenna.

3. The beamforming method of the near-field-based non-cellular massive MIMO synaesthesia integrated system according to claim 2, characterized in that: Construct a downlink channel model of a near-field-based non-cellular massive MIMO interaceptive integrated system and a radar perception target model, including: The downlink channel model is modeled under near-field conditions. The formula of the near-field spherical wave channel model between the access point and the user is: （3） in, Indicates m access point and k Near-field spherical wave channel model between users; P represents the k The number of near-field non-line-of-sight paths per user is l =0, Indicates k The complex gain of the LoS path of each user is hour, Indicates l The complex gain of each NLoS path; In the near field m The access point and k The steering vector between users, In the near field m The access point and l The guiding vector between the paths, In the near field m The access point and l Steering vectors between paths; Defines a joint sensing and communication signal model for downlink transmission, with access points transmitting together K Communication flows and Q A sensory stream, T = K + Q In the m The signal transmitted by the access point It is expressed as: （4） in, and They represent the communication beamforming matrix and the sensing beamforming matrix respectively; represents a communication symbol vector; represents the perceptual symbol vectors, assuming they are statistically independent of each other; the overall beamforming matrix Defined as , the emission symbol vector Defined as ,Right now ; No. k The received signal of a user is expressed as follows: （6） in, Indicates user k The signals received from all access points, Indicates m Access point to k The channel state information vector of each user is Indicates all access points to k The channel state information vector of each user is H represents conjugate transpose; Indicates m Access point to k The transmit beamforming vector of each user is, Indicates all access points to k The transmit beamforming vector of each user is, Indicates user k The additive Gaussian white noise at has a mean of zero and a variance of The complex Gaussian distribution of Indicates that all access points are available to users k The communication symbol transmitted; Represents the communication or sensing symbols transmitted by all access points to other users or targets, Represents the transmit beamforming vectors of all access points to other users or targets.

4. The beamforming method of the near-field-based non-cellular massive MIMO synaesthesia integrated system according to claim 3, characterized in that: Calculate the user's sum rate, including: Calculate the k Signal-to-interference-noise ratio , the specific formula is: （7） in, Represents the modulo operation on complex numbers; Calculate the user's sum rate R , the specific formula is: （8） in, Indicates k The rate of a user.

5. The beamforming method of the near-field-based non-cellular massive MIMO synaesthesia integrated system according to claim 4, characterized in that: Calculate the sensing power from the access point to the sensing target. The specific formula is: （9） in, Indicates the sensed power from the access point to the sensing target.

6. The beamforming method of the near-field-based non-cellular massive MIMO synaesthesia integrated system according to claim 5, characterized in that: Taking the transmit power of the access point and the sensing power from the access point to the sensing target as constraints, the user's sum rate is maximized by optimizing the transmit beamforming matrix of the access point, including: The optimization problem of maximizing the sum of users and rates is set to P0, and the specific formula is: （10） in, Indicates the minimum perceived performance required to ensure access points. Indicates the total transmission power; The optimization problem is solved by FP fractional programming method, and the optimal solution of access point beamforming matrix and user sum rate is obtained.

7. The beamforming method of the near-field-based non-cellular massive MIMO synaesthesia integrated system according to claim 6, characterized in that: The optimization problem is solved by FP fractional programming method, including: For the optimization problem P0, the Lagrange dual transformation is first used to move the SINR term to the outside of the logarithm, and then the auxiliary variable , the equivalent specific optimization problem P1 is expressed as: （11） Then, the fractional terms are transformed into polynomial addition and subtraction forms using the quadratic transformation theorem by introducing auxiliary variables. , the optimization problem P1 is reformulated as the optimization problem P2: （12）。 8. The beamforming method of the near-field-based non-cellular massive MIMO synaesthesia integrated system according to claim 7, characterized in that: Fixed access point beamforming vectors in optimization problem P2 , optimize the introduced auxiliary variables and obtain the optimal value of the auxiliary variables and , and then substitute the optimized auxiliary variables into the objective function of the optimization problem P2 and ignore the constant term to obtain the optimization problem P3, which is expressed as: （15a） （15b） （15c） Since constraint (15b) is a non-convex constraint, the MM algorithm is used to make it a convex constraint, and the lower bound of the constraint term is obtained: （16） in, Indicates that in the previous iteration Therefore, the optimization problem P3 is transformed into the optimization problem P4: （15a） （17b） （15c） in, ,The above problem is a convex problem. The CVX toolkit is used to solve the optimization problem P4 and obtain the ,optimal solution of the access point beamforming matrix and the user and ,rate.

9. A beamforming device for a near-field-based non-cellular massive MIMO interaceptive integrated system, characterized in that: include: A data processing module is used to obtain model information and calculate the user's sum rate and the perception power in the direction from the access point to the perception target, wherein the model information is the downlink channel model of the near-field-based non-cellular large-scale MIMO interawareness integrated system and the model information of the radar perception target model; The optimization module is used to maximize the sum rate of the user by optimizing the transmit beamforming matrix of the access point with the transmit power of the access point and the sensing power in the direction from the access point to the sensing target as constraints.

10. 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 beamforming method of the near-field-based non-cellular massive MIMO synaesthesia integrated system described in any one of claims 1 to 8 are implemented.

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