Super-large scale MIMO communication perception integration method

Abstract

The invention relates to the technical field of wireless communication, and discloses a super-large-scale MIMO (Multiple Input Multiple Output) communication perception integrated method, which comprises the following steps of: establishing a super-large-scale MIMO communication perception integrated system model; respectively establishing a communication model and a sensing model; calculating a communication performance index according to the communication model and calculating a perception performance index according to the perception model; the method comprises the following steps: constructing a joint optimization problem taking maximized communication performance indexes and perception performance indexes as objective functions, solving the joint optimization problem, obtaining an optimal beam forming vector, optimizing a transmission signal by using the optimal beam forming vector, obtaining an optimized transmission signal, and realizing a communication task and a perception task by using the optimized transmission signal. According to the invention, efficient cooperative operation of sensing and communication can be realized, the comprehensive performance of the system is improved, and a flexible and adjustable solution is provided for power distribution in different application scenes.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a method for integrated communication and sensing in ultra-large-scale MIMO. Background Art

[0002] Ultra-large-scale multiple-input multiple-output systems (XL-MIMO) are regarded as one of the technologies most likely to achieve a breakthrough from 5G to 6G. From large-scale multiple-input multiple-output systems (MIMO) to ultra-large-scale multiple-input multiple-output systems (XL-MIMO), this evolution is not simply an increase in the number or scale of antennas, but rather a fundamental change in the characteristics of the channel at the same time. In the past, when using MIMO systems to propagate signals, the near-field range was small, and users and targets were basically located in the far-field region. The propagation process could be regarded as uniform plane wave (UPW) transmission, that is, the signal streams transmitted and received by the base station antennas were regarded as parallel transmission beams; after introducing and using XL-MIMO systems, the increase in the antenna scale has also led to the expansion of the near-field scale, which makes more and more users and targets exist in the near-field. The traditional UPW transmission is not accurate enough and needs to be changed to non-uniform spherical wave (NUSW) transmission that is more in line with the actual situation. With the breakthrough of XL-MIMO in the near-field region, the related research on sharing frequency bands between communication devices and other devices has gradually attracted attention. Integrated sensing and communication (ISAC) technology conforms to the trend of the integration of communication and sensing.

[0003] However, at the present stage, the research on co-sensing integration still remains at the stage of single optimization of sensing performance or communication performance. For example, in the Chinese patent "MIMO Sensing Method, Apparatus and Communication Equipment" with the publication number CN119519771A, the prior art specifically includes: a first node obtains first information, where the first information is configuration information of a first signal; the first node transmits the first signal through at least two transmit antenna ports, and the first signals transmitted by different transmit antenna ports are orthogonal in at least one of the time delay domain and the Doppler domain. Thus, MIMO sensing can be achieved based on the signal orthogonality method in at least one of the time delay domain and the Doppler domain. Compared with the MIMO sensing based on the TDM and FDM orthogonality methods in the related art, the utilization rate of signal resources for MIMO sensing can be improved. Compared with the MIMO sensing based on the CDM orthogonality method in the related art, the reliability of MIMO sensing can be improved. This prior art only starts from optimizing sensing performance and ignores communication performance. This approach of emphasizing one index while ignoring the other can only solve single and fixed real situations. In some other situations where it is necessary to dynamically change the power allocation between the communication and sensing modules to adapt to real demands, Summary of the Invention The primary object of the present invention is to overcome the problems existing in the prior art and provide a method for ultra-large-scale MIMO communication and sensing integration. The present invention can achieve efficient collaborative operation of sensing and communication, not only improving the overall system performance, but also providing a flexible and adjustable solution for power allocation in different application scenarios.

[0004] To achieve the above object, the present invention provides a method for ultra-large-scale MIMO communication and sensing integration, and the method includes the following steps: Establish an ultra-large-scale MIMO communication and sensing integration system model including at least one of a base station, a user, a sensing target, and a scatterer; Based on the system model, respectively establish a communication model between the base station and the user and a sensing model between the base station and the sensing target; Calculate communication performance indicators according to the communication model and calculate sensing performance indicators according to the sensing model; Construct a joint optimization problem with the constraint conditions that the communication performance indicator is not less than the minimum communication performance of the system model and the sensing performance indicator is not less than the minimum sensing performance of the system model, and with the objective function of maximizing the communication performance indicator and the sensing performance indicator, solve the joint optimization problem to obtain the optimal beamforming vector; Optimize the transmitted signal using the optimal beamforming vector to obtain the optimized transmitted signal, and use the optimized transmitted signal to implement communication tasks and sensing tasks.

[0005] Further, establishing the communication model between the base station and the user specifically includes: Calculate the near-field channel vector of the user, where the near-field channel vector includes the visible channel vector and the non-visible channel vector. Taking the received signal of the k th user as an example, the specific calculation method is as follows: ,

[0006] where, is the visible channel vector, is the non-visible channel vector, K is the number of users, is the channel gain, L is the number of scatterers, is the steering vector of the antenna, is the k th distance from the user to the center point of the antenna; Calculate the received signal of the user according to the near-field channel vector. Taking the received signal of the k th user as an example, the calculation method is as follows:

[0007] is the signal expected to be received; is the interference between users; is k the noise interference received by the user, and its power spectral density is ; Calculate the signal-to-interference-plus-noise ratio received by the user according to the received signal. Taking the signal-to-interference-plus-noise ratio received by user k in the T time slot as an example, the calculation method is as follows:

[0008] where , , is the covariance matrix of the transmitted signal.

[0009] Further, the sensing model between the base station and the sensing target specifically includes: Define the response matrix of the sensing signal in the echo channel and the echo signal interference matrix. Among them, the response matrix is as follows:

[0010] where, is the steering vector of the target transmitting antenna, is the steering vector of the target receiving antenna, is the echo channel gain, and the echo signal interference matrix is as follows:

[0011] wherein, is the steering vector of the interfering transmitting antenna, is the steering vector of the interfering receiving antenna, is the echo channel gain; Calculate the echo signal received by the base station according to the response matrix and the echo signal interference matrix, and the specific calculation method is as follows:

[0012] wherein, Z is the noise in the echo channel, and its power spectral density is , X is the transmitted signal.

[0013] Furthermore, calculate the communication performance index according to the communication model, wherein the communication performance index is the communication rate. Taking the communication rate of the k th user as an example, the specific calculation method is as follows:

[0014] wherein, B is the channel bandwidth.

[0015] Furthermore, calculate the sensing performance index according to the sensing model, wherein the sensing performance index is the sensing mutual information, and the specific calculation method is as follows:

[0016] wherein, , , , .

[0017] 6. A method for integrated communication and sensing of ultra-large-scale MIMO according to claim 5, characterized in that the constructed joint optimization problem is as follows:

[0018] s.t.

[0019]

[0020]

[0021]

[0022] Among them, Constraint 1 is that the transmit signal covariance matrix is a positive semi - definite matrix, Constraint 2 is the maximum transmit power of the base station, Constraint 3 is the minimum threshold of sensing performance, and Constraint 4 is the minimum threshold of communication performance.

[0023] Furthermore, the solving of the joint optimization problem specifically includes: Decouple the joint optimization problem into two extreme - value sub - problems, denoted as the first extreme - value sub - problem and the second extreme - value sub - problem. Solve the first extreme - value sub - problem to obtain the maximum value of the sensing mutual information, and solve the second extreme - value sub - problem to obtain the maximum value of the signal - to - interference - plus - noise ratio; According to the maximum value of the sensing mutual information and the maximum value of the signal - to - interference - plus - noise ratio obtained by solving the two extreme - value sub - problems, construct a system utility function, and the system utility function is used to relate sensing performance and communication performance; Solve the system utility function to obtain the covariance matrix of the optimal beamforming vector, and obtain the optimal beamforming vector based on the covariance matrix.

[0024] Furthermore, the solving of the first extreme - value sub - problem to obtain the maximum value of the sensing mutual information specifically includes: using an auxiliary variable and the Schur complement method to transform the non - convex first extreme - value sub - problem into a first convex problem; then using a solver to solve the first convex problem to obtain the maximum value of the sensing mutual information.

[0025] Furthermore, the solving of the second extreme - value sub - problem to obtain the maximum value of the signal - to - interference - plus - noise ratio specifically includes: determining whether the user is a multi - user. If it is a multi - user, then use the Dinkelbach algorithm to solve and obtain the maximum value of the signal - to - interference - plus - noise ratio, otherwise solve directly.

[0026] Furthermore, the system utility function is as follows:

[0027] Among them, and are the weights in the system utility function, satisfying .

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention constructs a joint optimization problem with the constraint that the communication performance index is not less than the minimum communication performance of the system model and the sensing performance index is not less than the minimum sensing performance of the system model, and with the objective function of maximizing the communication performance index and the sensing performance index. During the process of solving the joint optimization, by transforming the non-convex problems of the communication performance index and the sensing performance index into convex problems, the Pareto solutions of sensing and communication are effectively solved, greatly improving the optimization efficiency and the solution accuracy. Also, by adjusting the weight ratio in the system utility function to flexibly allocate the power between sensing and communication, the efficient cooperative operation of sensing and communication can be achieved, which not only improves the comprehensive performance of the system but also provides a flexible and adjustable solution for power allocation in different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flowchart of a method for integrated communication and sensing of ultra-large-scale MIMO according to Embodiment 1 of the present invention; Figure 2 is a system model diagram of Embodiment 1 of the present invention; Figure 3 is the trend diagram of the sensing mutual information and the user rate varying with the weight ρ in Embodiment 2 of the present invention; Figure 4 is the comparison relationship diagram of the sensing mutual information and the multi-user average rate in the near and far fields in Embodiment 2 of the present invention; Figure 5 is the comparison relationship diagram of the sensing target and different distance differences of the scatterers in Embodiment 2 of the present invention; Figure 6 is the trend diagram of the sensing mutual information increasing with the increase of the distance difference in Embodiment 2 of the present invention; Figure 7 is the relationship diagram of the user average rate varying with the SNR in Embodiment 2 of the present invention; Figure 8 is the relationship diagram of the target sensing mutual information varying with the SNR in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The following further describes in detail the specific embodiments of the present invention with reference to the drawings and embodiments. The following embodiments are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0031] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0032] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "mounted", "connected", "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0033] In addition, in the description of the present invention, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0034] Embodiment 1 As Figure 1 shown, a method for integrated communication and sensing of ultra-large-scale MIMO in a preferred embodiment of an embodiment of the present invention includes the following steps: Step S1: Establish an ultra-large-scale MIMO communication and sensing integrated system model including at least one of a base station, a user, a sensing target, and a scatterer; Step S2: Based on the system model, respectively establish a communication model between the base station and the user and a sensing model between the base station and the sensing target; Step S3: Calculate communication performance indicators according to the communication model and calculate sensing performance indicators according to the sensing model; Step S4: Construct a joint optimization problem with the constraint conditions that the communication performance indicator is not less than the minimum communication performance of the system model and the sensing performance indicator is not less than the minimum sensing performance of the system model, and with the objective function of maximizing the communication performance indicator and the sensing performance indicator, solve the joint optimization problem to obtain the optimal beamforming vector; Step S5: Use the optimal beamforming vector to optimize the transmitted signal, obtain the optimized transmitted signal, and use the optimized transmitted signal to implement communication tasks and sensing tasks.

[0035] Next, the technical solution of the present invention will be specifically described: As Figure 2 shown, in this embodiment, a near-field ISAC system with multiple communication users and a single sensing target and a single scatterer is established. In this system, the base station adopts a uniformly distributed linear antenna array (ULA) to transmit signals and k communicate with users (denoted as K ), and receive the echo signal of the sensing target. And the scatterer in the communication system will cause multipath effects on the users and interference on the sum echo of the sensing target. A polar coordinate system is established, and it is set that the antennas are distributed on the y-axis and the center of the antenna is the origin of the coordinate system. There are transmitting antennas and receiving antennas. The user coordinates can be expressed as ( ), the coordinates of the target can be expressed as , and the coordinates of the scatterer can be expressed as

[0036] The transmitted signal X = FS = , where F= is the beamforming matrix, is the original transmitted signal, T is the number of time slots, satisfying , then the covariance matrix of the transmitted signal is: 。 Then the constraint condition for the covariance matrix of the transmitted signal is

[0037] Then, a communication model between the base station and the users in this system model is established, specifically including: Since there are scatterers in the environment, during the communication process, the channel is divided into a line-of-sight channel (LOS) and a non-line-of-sight channel (NLOS). In the LOS channel, the channel from the k th user to the n th antenna element is: , where is the wavelength of the transmitted signal, is the distance from the kth user to the nth antenna element, is the distance from the kth user to the center of the antenna, , d is the spacing between antenna elements. Then the near-field LOS channel vector of the kth user is: , where . Then the channel of the kth communication user is the sum of the LOS channel and the NLOS channel, that is: ,

[0038] where is the channel gain, L is the number of scatterers, refers to the steering vector of the antenna.

[0039] Based on the above information, the received signal of the user k can be obtained as: , where is the desired received signal, is the interference between users, is the noise interference received by user k, and its power spectral density is . Therefore, the signal-to-interference-plus-noise ratio (SINR) received by user k in time slot T is given by the following formula: , where

[0040] Furthermore, a sensing model between the base station and the sensing target is established, which specifically includes: Different from the traditional communication model where the channel only involves the signal transmission process, the channel in the sensing module simultaneously includes both the signal transmission and reception processes. When constructing a computational model for describing the sensing target, it is first necessary to clearly define the response matrix of the sensing signal in the echo channel:

[0041] where is the target transmit antenna steering vector, is the target receive antenna steering vector, is the echo channel gain. These metrics represent the transmission characteristics of the echo signal of the sensing target in the near-field environment and directly affect the strength of the echo signal. In this system, the scatterers will act as interference to the sensing module and also generate interference echoes, and the interference echo signal matrix is defined as:

[0042] where is the interference transmit antenna steering vector, is the interference receive antenna steering vector, is the echo channel gain. The interference echo signal matrix represents the channel characteristics of the scatterer echo interference and is directly related to the interference strength, thus indirectly affecting the strength of the echo signal of the sensing target.

[0043] Then the echo signal received at the base station receiver can be expressed as: , where Z is the noise in the echo channel and its power spectral density is .

[0044] Further, calculate the communication performance metrics according to the communication model. In this embodiment, the communication performance metric is the communication rate. Taking the communication rate of the k th user as an example, the unit is bps / Hz, and the specific calculation method is as follows:

[0045] where B is the channel bandwidth.

[0046] Further, calculate the sensing performance metrics according to the sensing model. In this embodiment, the sensing performance metric is the mutual information of sensing (Mutal Information, MI), which can be used to measure how much environmental information can be observed in the base station. The sensing MI is usually defined as the conditional mutual information between the sensing channel and the received signal , that is, the general expression is:

[0047] where , , , , then the covariance matrix of the echo response matrix is:

[0048] Similarly, the covariance matrix of the echo interference matrix is:

[0049] The noise matrix in the echo channel is: . , with the unit of bits.

[0050] Let , . Substitute the above expressions and simplify to obtain the mutual information of sensing in the case of a single target and a single scatterer as:

[0051] In the formula , , , .

[0052] Further, construct a joint optimization problem with the constraint that the communication performance metric is not less than the minimum communication performance of the system model and the sensing performance metric is not less than the minimum sensing performance of the system model, and with the objective of maximizing the communication performance metric and the sensing performance metric. Specifically, the objective of this embodiment is to simultaneously optimize the communication user rate and the perceptual mutual information , a beamforming vector F that meets both communication requirements and sensing requirements is obtained. By adjusting the weight ratio in the utility function, the power allocation between sensing and communication can be flexibly adjusted. Since the improvement of communication performance will inevitably lead to the decline of sensing performance, and vice versa, and this relationship is not linear, in this multi-objective optimization problem, it is necessary to simultaneously obtain the maximum value of the communication user rate and the perceptual mutual information as much as possible. Due to the relationship between the user rate and the user signal-to-interference-plus-noise ratio, the user signal-to-interference-plus-noise ratio can also be used to measure the communication quality. The maximization problem of calculating the communication rate and the sensing signal-to-noise ratio can be expressed as:

[0053] Constraint 1: s.t.

[0054] Constraint 2:

[0055] Constraint 3:

[0056] Constraint 4:

[0057] Among them, Constraint 1 is that the transmit signal covariance matrix is a positive semi-definite matrix, Constraint 2 is the maximum transmit power of the base station, Constraint 3 is the minimum threshold of sensing performance, and Constraint 4 is the minimum threshold of communication performance. Constraints 1 and 2 are convex, and Constraints 3 and 4 are non-convex, but can be transformed into convex constraints through processing.

[0058] Furthermore, solving the joint optimization problem specifically includes: (1). Decouple the joint optimization problem into two maximum value sub-problems, denoted as the first maximum value sub-problem and the second maximum value sub-problem, solve the first maximum value sub-problem to obtain the maximum value of the perceptual mutual information, and solve the second maximum value sub-problem to obtain the maximum value of the signal-to-interference-plus-noise ratio; (2). Construct a system utility function based on the maximum value of the perceptual mutual information and the maximum value of the signal-to-interference-plus-noise ratio obtained by solving the two maximum value sub-problems, and the system utility function is used to relate the sensing performance and the communication performance; (3). Solve the system utility function to obtain the covariance matrix of the optimal beamforming vector, and obtain the optimal beamforming vector based on the covariance matrix. Specifically, In this embodiment, a multi-objective optimization algorithm based on the SDP problem is designed. First, the joint problem is decoupled into two types of sub-problems to lay the foundation for obtaining the system utility function: 1) Centered on sensing, solve to obtain the maximum value of the mutual information of sensing. 2) Centered on communication, solve for the maximum signal-to-interference-plus-noise ratio (SINR) of each user. 3) On the premise of obtaining the maximum mutual information of sensing and the maximum SINR of users, construct the system utility functions of sensing and communication, and obtain the Pareto solutions of sensing and communication by optimizing the system utility functions. The specific processes of solving the two maximum value sub-problems are as follows: 1. Solve for the maximum value of the mutual information of sensing when centered on sensing, that is, solve the first maximum value sub-problem: Given the expression of the mutual information of sensing , in the case of being centered on sensing, by optimizing the beamforming vector , the sub-problem of obtaining the mutual information of sensing (i.e., the first maximum value sub-problem) can be expressed as:

[0059] Constraint conditions:

[0060] Although the first maximum value sub-problem has only one optimization problem, due to the expression of , it is non-convex, and the first maximum value sub-problem is difficult to solve by ordinary algorithms. For the complex expression of , in order to maximize , it is only necessary to maximize . To transform it into a convex problem, an auxiliary variable V needs to be introduced, and the Schur complement method is used to transform it into an SDP problem. That is, transform the first maximum value sub-problem into the first convex problem:

[0061] Constraint conditions:

[0062] From the first maximum value sub-problem to the first convex problem, the mutual information of sensing is converted into an SDP problem, thus making it have the property of a convex function. The first convex problem can be easily solved with the help of existing solvers (such as the CVX toolbox).

[0063] 2. Solve for the maximum value of the communication SINR when centered on communication, that is, solve the second maximum value sub-problem: Given the known user positions, by finding an appropriate beamforming vector , the problem of solving for the maximum value of the SINR of the communication users (the second maximum value sub-problem) can be expressed as:

[0064]

[0065]

[0066] Observation For the expression of Since both are positive semi - definite matrices, the problem (P4) can be transformed into a convex problem through some operations. First, assume the single - user case, i.e., k = 1. At this time, the signal - to - interference - plus - noise ratio (SINR) of the user can be obtained as: , given the known channel, the SINR of a single user is obviously convex and can be directly solved.

[0067] In the case of multiple users, due to the existence of interference between users, it is difficult to guarantee the convex - function property of the user SINR. In this case, according to the Dinkelbach algorithm, with the help of , this constraint condition, we can get:

[0068] where P satisfies . Since are both positive semi - definite matrices, the SINR in the multi - user case is transformed into the form of P to obtain the property of a convex function, and thus can be directly solved.

[0069] 3. Establish a system utility function to solve the multi - objective optimization problem: When solving through the first maximum - value sub - problem and the second maximum - value sub - problem, the maximum value of the mutual information centered on sensing and the maximum value of the SINR centered on communication are obtained. To link sensing and communication in the multi - objective optimization problem, a system utility function is needed. In the process of constructing the system utility function, the meaning of the function needs to be considered. From the previous derivations and expressions, it is easy to think that: Given that | is the distance between the sensing mutual information and its maximum value, is the distance between the communication SINR and its maximum value. Then the system utility function can be set as:

[0070] where, and are the weights in the system utility function, indicating the degree of emphasis on the communication module and the sensing module, and satisfy .

[0071] To make the sensing mutual information and the communication SINR as close as possible to their respective maximum values, the minimum value of the system utility function U needs to be solved, that is, the joint optimization problem is transformed into:

[0072] Constraint 1: s.t.

[0073] Constraint 2:

[0074] Constraint 3:

[0075] Constraint 4:

[0076] By solving the minimum value of the system utility function U, the Pareto solution that simultaneously satisfies the communication performance and sensing performance can be obtained. It should be noted that all the above formulas are linear or convex constraints, that is, the minimum value of the system utility function U is a convex problem, which can be directly and effectively solved by existing solvers (such as: CVX). Finally, the optimal beamforming vector is obtained; the optimal beamforming vector is used to optimize the transmitted signal, the optimized transmitted signal is obtained, and the optimized transmitted signal is used to implement the communication task and the sensing task.

[0077] In this embodiment, a joint optimization problem is constructed with the constraint conditions that the communication performance index is not less than the minimum communication performance of the system model and the sensing performance index is not less than the minimum sensing performance of the system model, and the objective function is to maximize the communication performance index and the sensing performance index. In the process of solving the joint optimization, by transforming the non-convex problems of the communication performance index and the sensing performance index into convex problems, the Pareto solution of sensing and communication is effectively solved, greatly improving the optimization efficiency and the solution accuracy; by adjusting the weight ratio in the system utility function, the power between sensing and communication can be flexibly allocated, enabling the efficient collaborative operation of sensing and communication, not only improving the comprehensive performance of the system, but also providing a flexible and adjustable solution for power allocation in different application scenarios.

[0078] Embodiment 2 This embodiment is the specific experimental process of the method proposed in Embodiment 1. Specifically, in the multi-objective optimization process, the weight ρ is defined as the resource allocation weight, which is used to balance the resource allocation between sensing and communication. According to the previous description, when the weight of the sensing target is set to ρ , the weight of the communication user is correspondingly determined to be 1 - ρ . Figure 3 shows the trends of the sensing mutual information and the user rate with the change of the weight ρ . As the weight ρ gradually increases from 0 to 1, the sensing mutual information shows an increasing trend, while the user rate decreases accordingly. The reason for this phenomenon is that the transmit power allocated to the sensing function in the ISAC system increases with the increase of ρ . This shows that by adjusting the weight ρ , the resources of the base station can be flexibly allocated, thereby effectively controlling the relative proportion of sensing and communication in the ISAC system and realizing the performance trade-off between the two.

[0079] As shown Figure 4 in the figure, the comparison relationship between the sensing mutual information and the multi-user average rate obtained by solving the Pareto optimal solution of sensing and communication under different weight configurations is presented. Among them, the blue curve represents the comparison relationship under far-field conditions, while the red curve corresponds to the comparison relationship under near-field conditions. This figure intuitively shows the trend of the sensing mutual information and the user rate changing with the weight ρ variation. Specifically, as the weight ρ varies, the changes in the sensing mutual information and the user rate exhibit non-linear characteristics, forming a curved line with an arc. This indicates that by adjusting the weight ρ , a flexible trade-off can be achieved between sensing and communication performance to adapt to different system requirements and application scenarios. In addition, the comparison relationship curve under near-field conditions in the figure is completely outside the curve under far-field conditions, which fully demonstrates the performance advantage of the near-field ISAC system compared to the far-field ISAC system. Due to the unique characteristics of signal propagation in the near-field environment, such as the improvement of distance gain and angular resolution, the near-field ISAC system can utilize resources more effectively in complex environments, achieving higher communication efficiency and more accurate sensing ability, and showing better performance in both sensing mutual information and user rate.

[0080] According to the above analysis, it can be seen that the scatterers have a significant interference on the echo signal of the sensing target, which directly leads to the degradation of the performance of the sensing target, that is, the decrease of the sensing mutual information. In the near-field scenario, due to the influence of the distance domain, this embodiment deeply studies the interference characteristics of the scatterers on the echo of the sensing target at near-field distances. To exclude the potential influence of the angle factor on the experimental results, the scatterer is fixed at the polar coordinate position (35m, 45°) in the experiment, and the angle coordinate of the sensing target is also set to 45°. By systematically changing the distance coordinate of the sensing target, the Figure 5 comparison relationship diagram between the sensing target and the scatterer at different distance differences as shown

[0081] is obtained. Figure 5 From this, it can be clearly observed that in the distance domain, the scatterers have a large interference on the sensing mutual information of the sensing target. When the distance difference between the two is only 5m, the interference of the scatterers on the sensing target is particularly significant; while as the distance difference increases, the interference of the scatterers on the sensing target gradually decreases, which is manifested as the gradual increase of the sensing mutual information. During the process of moving the sensing target, although the sensing target is getting farther and farther away from the base station antenna, resulting in a gradual decrease of the distance gain, according to the formula of the sensing mutual information in Equation (10), due to the existence of the subtraction of the cross term, the influence of the distance gain on the sensing mutual information can be ignored.

[0082] In addition, fromFigure 5 The comparison of different comparison relationships and Figure 6 the curve trend of can be observed. As the distance difference increases, the increase in the perceived mutual information is not fixed. Specifically, as the distance difference increases, the increase in the perceived mutual information gradually decreases. This is because when the distance between the scatterer and the perceived target becomes farther and farther, the interference of the scatterer on the perceived target gradually weakens, and finally the perceived mutual information will gradually converge as the distance difference increases, as Figure 6 shown. This phenomenon indicates that in the near-field ISAC system, the interference effect of the scatterer on the perceived target gradually weakens as the distance difference increases, and finally it will reach a situation close to no interference, that is, the perceived mutual information will gradually converge.

[0083] The signal-to-noise ratio (SNR) is a key indicator to measure the signal quality, and it is defined as the ratio of the base station transmission power to the channel noise power. When the channel noise power remains constant, the change of SNR is mainly affected by the base station transmission power. Figure 7 shows the relationship diagram of the average user power varying with SNR. By analyzing this diagram, it can be found that as SNR increases, each curve shows an upward trend. Specifically, Figure 7 the blue curve in represents the optimization result centered on communication, and its position is significantly higher than other curves. This is because in the communication-centered optimization strategy, the vast majority of the base station transmission power is allocated to communication tasks, thus achieving the maximization of communication performance, representing the upper bound of communication performance.

[0084] In contrast, the red curve corresponding to the Pareto optimal solution obtained through multi-objective optimization is lower than the blue curve. This is because in the Pareto optimal solution, part of the transmission power is allocated to sensing tasks, thus achieving a compromise between communication and sensing performance. The optimization result centered on sensing is represented by the black curve, and its average user rate is significantly lower than other curves. This is because in the sensing-centered optimization strategy, the vast majority of the base station transmission power is allocated to sensing tasks, resulting in relatively low communication performance.

[0085] In the experiment, this embodiment sets the Pareto weight ρ to 0.1, and the remaining weight 1 - ρ is evenly distributed to communication users. This means that in the ISAC system, more resources are allocated to communication tasks, thus improving communication performance. It can be seen from the comparison diagram that the Pareto optimal solution (red curve) obtained through multi-objective optimization is very close to the green curve obtained by using the zero-forcing algorithm, which indicates that in terms of resource allocation, the Pareto optimal solution can effectively balance communication and sensing performance.

[0086] In addition, Figure 7The communication user rates of the two curves (yellow curve and pink curve) representing the far field are significantly lower than those of the curves in the near field. This phenomenon fully demonstrates the advantage of the near field over the far field in terms of communication performance. Specifically, the communication system in the near field environment can utilize the transmit power more effectively, thereby achieving a higher communication rate. This advantage is mainly attributed to the characteristics of signal propagation in the near field environment, such as the distance gain and the improvement of angular resolution, which enable the communication system to transmit information more efficiently in the near field environment.

[0087] While keeping the experimental parameters consistent with Figure 7 the following was obtained through experiments Figure 8 , that is, the relationship diagram of the target perception mutual information varying with SNR. As SNR increases, the perception performance represented by each curve shows an upward trend. This phenomenon is consistent with the logic of Figure 7 . In Figure 8 , the blue curve represents the perception mutual information when perception is centered. At this time, the vast majority of the base station transmit power is allocated to the perception task, thus achieving the maximization of perception performance, representing the upper bound of perception performance in this embodiment. The red curve represents the result of Pareto multi-objective optimization, which reflects the trade-off between communication performance and perception performance. The black curve represents the perception mutual information when communication is centered, showing the suppression of perception performance in the case of communication-centered.

[0088] The green curve represents the perception performance curve obtained by using the zero-forcing algorithm. It can be seen from the figure that the perception mutual information at this time is relatively low. This indicates that, as a classic multi-user interference processing algorithm, the zero-forcing algorithm inevitably sacrifices some perception performance while effectively reducing the interference between users and improving the communication rate.

[0089] In addition, Figure 8 the perception mutual information of the two curves (yellow curve and pink curve) representing the far field in Figure 7 is significantly lower than that of the curves in the near field. This phenomenon fully demonstrates the advantage of the near field over the far field in terms of perception performance. Combining the analysis results of Figure 8 and the following can be found: Figure 7 1. Under the communication-centered optimization strategy, if there is no threshold bottom line for perception performance set, the system will obtain higher communication performance (as shown by the blue curve in Figure 8 ), but at the same time, the perception performance will be extremely suppressed (as shown by the black curve in

[0090] ). Conversely, the same is true for the perception-centered optimization strategy. 2. The Pareto multi-objective optimization strategy (such as Figure 7 and Figure 8As shown by the red curve in [FIGURE REFERENCE], it can flexibly adjust the resource allocation between sensing and communication by assigning different weights, thereby achieving a better balance between the two and realizing excellent communication and sensing performance.

[0091] 3. By comparing Figure 7 and Figure 8 , it can be found that by adjusting the weights, the communication performance can be made close to the communication rate obtained by the classical zero-forcing algorithm, while the sensing performance is still better than that obtained by the zero-forcing algorithm. This shows that the Pareto multi-objective optimization strategy has higher flexibility and superiority in resource allocation, and can meet the requirements of sensing performance while satisfying the communication performance.

[0092] Embodiment 3 The embodiment of the present invention also provides a computer-readable storage medium, on which a program of a method for integrated communication and sensing of ultra-large-scale MIMO is stored. When the program is executed, the steps of the method for integrated communication and sensing of ultra-large-scale MIMO are implemented.

[0093] In summary, the embodiment of the present invention provides a method and a storage medium for integrated communication and sensing of ultra-large-scale MIMO. By constructing a joint optimization problem with the constraint conditions that the communication performance index is not less than the minimum communication performance of the system model and the sensing performance index is not less than the minimum sensing performance of the system model, and with the objective function of maximizing the communication performance index and the sensing performance index, during the process of solving the joint optimization, by transforming the non-convex problems of the communication performance index and the sensing performance index into convex problems, the Pareto solutions of sensing and communication are effectively solved, greatly improving the optimization efficiency and the solution accuracy; also, by adjusting the weight ratio in the system utility function to flexibly allocate the power between sensing and communication, the efficient cooperative operation of sensing and communication can be realized, not only improving the comprehensive performance of the system, but also providing a flexible and adjustable solution for power allocation in different application scenarios.

[0094] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and replacements can still be made, and these improvements and replacements should also be regarded as the protection scope of the present invention.

Claims

1. A method for integrating ultra-large-scale MIMO communication perception, characterized in that: The method comprises the following steps: Establishing a super-large-scale MIMO communication sensing integrated system model including at least a base station, a user, a sensing target and one of scatterers; Based on the system model, a communication model between the base station and the user and a perception model between the base station and the perception target are respectively established; Calculating a communication performance indicator according to the communication model and calculating a perception performance indicator according to the perception model; Constructing a joint optimization problem with constraints that the communication performance index is not less than the minimum communication performance of the system model and the perception performance index is not less than the minimum perception performance of the system model, and taking maximizing the communication performance index and the perception performance index as the objective function, solving the joint optimization problem, and obtaining an optimal beamforming vector; The optimal beamforming vector is used to optimize the transmission signal to obtain the optimized transmission signal, and the optimized transmission signal is used to achieve the communication task and the perception task.

2. The ultra-large-scale MIMO communication perception integrated method according to claim 1, characterized in that: The establishing of a communication model between the base station and the user specifically includes: Calculate the near-field channel vector of the user, the near-field channel vector including the visible channel vector and the non-visual channel vector. k Take the received signal of a user as an example, the specific calculation method is as follows: ， in, is the visible channel vector, is the non-visual channel vector, K is the number of users, is the channel gain, L is the number of scatterers, is the antenna’s steering vector, For the k The distance from each user to the center of the antenna; The received signal of the user is calculated according to the near-field channel vector. k Take the received signal of a user as an example, the calculation method is as follows: is the signal expected to be received; Interference between users; for k The power spectral density of the noise interference received by the user is ; The signal to interference noise ratio (SINR) received by the user is calculated according to the received signal to obtain the signal to be received by the user in the T time slot. k Taking the received signal-to-interference-to-noise ratio as an example, the calculation method is as follows: in , , is the covariance matrix of the transmitted signal.

3. The ultra-large-scale MIMO communication perception integrated method according to claim 2, characterized in that: The perception model between the base station and the perception target specifically includes: A response matrix of the sensing signal in the echo channel and an echo signal interference matrix are defined, wherein the response matrix is ​​as follows: in, is the target transmitting antenna steering vector, is the target receiving antenna steering vector, is the echo channel gain, and the echo signal interference matrix is ​​as follows: in, is the interference transmitting antenna steering vector, To interfere with the receiving antenna steering vector, is the echo channel gain; The echo signal received by the base station is calculated according to the response matrix and the echo signal interference matrix, and the specific calculation method is as follows: in, Z is the noise in the echo channel, and its power spectral density is , X To transmit the signal.

4. The ultra-large-scale MIMO communication perception integrated method according to claim 3, characterized in that: The communication performance index is calculated according to the communication model, wherein the communication performance index is the communication rate, k Take the communication rate of a user as an example, the specific calculation method is as follows: in, B is the channel bandwidth.

5. The ultra-large-scale MIMO communication perception integrated method according to claim 4, characterized in that: The perceptual performance index is calculated according to the perceptual model, wherein the perceptual performance index is perceptual mutual information, and the specific calculation method is as follows: in, , , , .

6. The ultra-large-scale MIMO communication perception integrated method according to claim 5, characterized in that: The joint optimization problem constructed is as follows: s.t. Among them, constraint 1 is that the covariance matrix of the transmitted signal is a matrix with semi-positive definite properties, constraint 2 is the maximum transmit power of the base station, constraint 3 is the minimum threshold of the perception performance, and constraint 4 is the minimum threshold of the communication performance.

7. The ultra-large-scale MIMO communication perception integrated method according to claim 6, characterized in that: The solving of the joint optimization problem specifically includes: Decouple the joint optimization problem into two maximum subproblems, denoted as the first maximum subproblem and the second maximum subproblem subproblems, solving the first minimum subproblem to obtain the maximum value of the perceptual mutual information, and solving the second minimum subproblem to obtain the maximum value of the signal to interference noise ratio; According to the maximum value of the perceptual mutual information and the maximum value of the signal to interference noise ratio obtained by solving the two maximum sub-problems, a system utility function is constructed, wherein the system utility function is used to link the perceptual performance and the communication performance; The system utility function is solved to obtain a covariance matrix of an optimal beamforming vector, and the optimal beamforming vector is obtained based on the covariance matrix.

8. The ultra-large-scale MIMO communication perception integrated method according to claim 7, characterized in that: The method of solving the first maximum subproblem to obtain the maximum value of the perceived mutual information specifically includes: using auxiliary variables and the Schur complement method to transform the non-convex first maximum subproblem into a first convex problem; and then using a solver to solve the first convex problem to obtain the maximum value of the perceived mutual information.

9. The ultra-large-scale MIMO communication perception integrated method according to claim 7, characterized in that: Solving the second minimum subproblem to obtain the maximum value of the signal to interference plus noise ratio specifically includes: determining whether the user is a multi-user, and if so, using the Dinkelbach algorithm to obtain the maximum value of the signal to interference plus noise ratio, otherwise directly solving it.

10. The ultra-large-scale MIMO communication perception integrated method according to claim 7, characterized in that: The system utility function is as follows: in, and is the weight in the system utility function, satisfying .

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