Multi-base station sensing and transmission integrated system transmission optimization method and device and computer equipment

Abstract

The application discloses a multi-base station sensing integrated system transmission optimization method and device and computer equipment, the method comprises the following steps: for the imperfect channel scene of complex communication conditions in a closed environment, a new multi-base station sensing integrated system is constructed, and an optimization algorithm and a passive sensing multiple signal classification algorithm are proposed. The imperfect channel model in near field communication, the communication and sensing model in the system, and the cooperative sensing performance index Cramer-Rao bound are given. Finally, in order to improve the overall sensing performance of the system, the resource allocation optimization problem with the minimum Cramer-Rao bound as the objective function is constructed under the premise of guaranteeing the communication demand. The optimization problem is a non-convex optimization problem and is difficult to solve, therefore, the Shur complement theorem is used to convert the non-convex objective function into a convex objective function, and the semi-definite relaxation theorem is used to convert the communication demand constraint into a convex constraint. For the target positioning problem in the sensing task, the passive sensing MUSIC algorithm based on the orthogonal subspace is used for coordinate estimation.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method and apparatus for optimizing transmission in a multi-base station integrated sensing system, and computer equipment. Background Technology

[0002] With the increasing demand for high transmission rates in wireless communication systems, near-field communication (NFC) and short-range wireless technologies are becoming increasingly prevalent in various applications, intensifying competition for spectrum resources. Simultaneously, the development of the Internet of Things (IoT), smart homes, and industrial automation is driving a surge in demand for communication and sensing within enclosed spaces. To meet these communication and sensing needs and improve spectrum resource utilization, the novel Integrated Sensing and Communication (ISAC) system has emerged as a promising technology for realizing 5G / 6G. This system can simultaneously provide communication and sensing services using radio signals and wireless network infrastructure, offering a new technological platform for communication within enclosed spaces.

[0003] The integrated sensing technology is a technology that shares the same frequency band and hardware for sensing and communication. It achieves a unified design of communication and sensing functions through joint design of air interface and protocol, reuse of time, frequency and space resources, and sharing of hardware devices. This enables the wireless network to achieve high-precision and refined sensing functions while conducting high-quality communication interaction, thereby improving the system's spectrum efficiency, hardware efficiency and information processing efficiency.

[0004] In the context of the deployment of ultra-large-scale antenna arrays in 5G / 6G communication, the far-field effect of electromagnetic fields is gradually evolving into the near-field effect. The distance dimension introduced by spherical waves in the near-field region also promotes the potential for joint estimation of distance and angle in sensing. Compared with the open outdoor environment with few obstacles, the environment of a closed space is more complex and faces more challenges. Traditional far-field communication models are no longer suitable for communication in closed spaces. Summary of the Invention

[0005] In view of this, this application provides a transmission optimization method and apparatus, and a computer device for a multi-base station integrated sensing system. For imperfect channel scenarios with complex communication conditions in enclosed environments, a novel multi-base station integrated sensing system is constructed. Optimization algorithms and passive sensing multi-signal classification algorithms are presented for the resource allocation problem and the localization problem in the sensing task, respectively. First, an imperfect channel model in near-field communication and communication and sensing models in the system are constructed, and the cooperative sensing performance index Cramer-Rao bound (CRB) is given. Finally, to improve the overall sensing performance of the system, a resource allocation optimization problem with minimizing the Cramer-Rao bound as the objective function is constructed while ensuring communication requirements. This optimization problem is non-convex and difficult to solve; therefore, the non-convex objective function is first transformed into a convex objective function using the Schur complement theorem. For the communication requirement constraint, a semidefinite relaxation theorem is used to transform it into a convex constraint. For the target localization problem in the sensing task, a passive sensing MUSIC algorithm based on orthogonal subspaces is designed for coordinate estimation.

[0006] According to one aspect of this application, a transmission optimization method for a multi-base station integrated sensing system is provided, the method comprising:

[0007] A novel integrated sensing system is constructed, comprising multiple integrated sensing transmitting base stations, one integrated sensing receiving base station, multiple communication users, and one sensing target. The novel integrated sensing system utilizes communication channels to provide information transmission services to communication users and utilizes sensing channels to perceive in real time the location coordinates of at least one sensing target, including the communication users, while the communication users are using the information transmission service. The sensing target is determined based on multiple targets within a preset range around the communication users' environment. The integrated sensing receiving base station is used to receive echoes.

[0008] When a communication user in a confined space uses information transmission services based on the novel integrated sensing system, for the novel integrated sensing system under confined space conditions, an imperfect near-field communication channel model for the communication channel and an imperfect near-field sensing channel model for the sensing channel are constructed in the novel integrated sensing system.

[0009] Based on the constructed imperfect near-field communication channel model, a communication rate performance index is constructed to evaluate the information transmission performance of the communication channel, and a perception performance index is constructed to evaluate the perception capability of the perception channel based on the constructed imperfect near-field sensing channel model. The information transmission performance is characterized by the communication rate of the communication signal received by the communication user, and the perception capability is characterized by the change in position coordinates of the sensing target perceived by the novel integrated sensing system.

[0010] The objective function is to minimize the change in the position coordinates of the perceived target by the novel integrated sensing system, as evaluated based on sensing performance indicators. The first constraint is that the communication rate of the communication signal received by the communication user meets the user's needs, as evaluated based on communication rate performance indicators. The second constraint is that the total power of the novel integrated sensing system meets the requirements. The third constraint is that the covariance of the transmitted signal of the integrated sensing base station meets the positive definiteness requirement. The solution is to find the optimal configuration of each integrated sensing base station for the communication channel and sensing channel in the novel integrated sensing system. The integrated sensing base station includes an integrated sensing transmitting base station and an integrated sensing receiving base station. In the solution, the objective function is transformed into a non-convex function using the Schur complement theorem, and the first constraint is transformed into a non-convex constraint using a semi-definite relaxation technique.

[0011] According to another aspect of this application, a transmission optimization device for a multi-base station integrated sensing system is provided, the device comprising:

[0012] A novel integrated sensing system construction module is used to construct a novel integrated sensing system comprising multiple integrated sensing transmitting base stations, one integrated sensing receiving base station, multiple communication users, and one sensing target. The novel integrated sensing system utilizes a communication channel to provide information transmission services to communication users and utilizes a sensing channel to perceive in real time the location coordinates of at least one sensing target, including the communication users, while the communication users are using the information transmission service. The sensing target is determined based on multiple targets within a preset range around the communication users' environment. The integrated sensing receiving base station is used to receive echoes.

[0013] The imperfect channel model construction module is used to construct, when a communication user in a confined space uses information transmission services based on the new integrated sensing system, an imperfect near-field communication channel model for the communication channel and an imperfect near-field sensing channel model for the sensing channel in the new integrated sensing system under confined space conditions.

[0014] The performance index construction module is used to construct a communication rate performance index for evaluating the information transmission performance of the communication channel based on the constructed imperfect near-field communication channel model, and a perception performance index for evaluating the perception capability of the perception channel based on the constructed imperfect near-field sensing channel model. The information transmission performance is characterized by the communication rate of the communication signal received by the communication user, and the perception capability is characterized by the change in position coordinates of the sensing target perceived by the novel integrated sensing system.

[0015] The optimal configuration solution module is used to solve for the optimal configuration of each integrated sensing base station in the new integrated sensing system for the communication channel and sensing channel, with the objective function being the minimum change in the position coordinates of the sensing target perceived by the new integrated sensing system based on the sensing performance index, the first constraint being that the communication rate of the communication signal received by the communication user meets the needs of the communication user based on the communication rate performance index, the second constraint being that the total power of the new integrated sensing system meets the second constraint, and the third constraint being that the covariance of the signal transmitted by the integrated sensing base station meets the positive definiteness. The integrated sensing base station includes an integrated sensing transmitting base station and an integrated sensing receiving base station. During the solution process, the objective function is transformed into a non-convex function using the Schur complement theorem, and the first constraint is transformed into a non-convex constraint using a semi-definite relaxation technique.

[0016] According to another aspect of this application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described transmission optimization method for a multi-base station integrated sensing system.

[0017] Using the above technical solutions, this application provides a transmission optimization method and apparatus, and computer equipment for a multi-base station integrated sensing system. Addressing the imperfect channel conditions in complex communication environments within enclosed spaces, this method solves the beam design and power allocation of a novel integrated sensing system, and improves sensing performance through cooperative sensing. On one hand, compared to the limited sensing range of a single base station, the novel multi-base station integrated sensing system can further expand the sensing range and improve sensing performance, thereby achieving more accurate target positioning under imperfect channel conditions. On the other hand, in complex communication environments within enclosed spaces, due to the inaccuracy and incompleteness of Channel State Information (CSI), traditional beamforming techniques may fail to accurately align with target users and sensing targets, leading to a decline in communication and sensing quality. Therefore, for multi-base station systems in enclosed spaces, an accurate mathematical model and corresponding sensing performance indicators are established; and a beam optimization algorithm is designed to address the resource allocation problem under imperfect channel conditions, ensuring the communication quality and improving the sensing performance of the novel integrated sensing system.

[0018] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 A flowchart illustrating a transmission optimization method for a multi-base station integrated sensing system provided in an embodiment of this application is shown.

[0021] Figure 2 This illustration shows a schematic diagram of a novel multi-base station integrated sensing system provided in an embodiment of this application;

[0022] Figure 3 This paper illustrates a flowchart of an embodiment of the present application for solving the optimal configuration of a novel multi-base station integrated sensing system;

[0023] Figure 4 This paper illustrates a graph showing the relationship between an imperfect channel factor and sensing performance, as provided in an embodiment of this application.

[0024] Figure 5 This application provides an embodiment of a graph showing the relationship between target distance and perception performance.

[0025] Figure 6 This application provides an embodiment of a graph showing the relationship between the number of users and perceived performance.

[0026] Figure 7 This application provides a power-sensing performance relationship diagram according to an embodiment of the present application;

[0027] Figure 8 This illustration shows a single MUSIC base station positioning diagram under imperfect channel conditions provided in an embodiment of this application.

[0028] Figure 9 This illustration shows a multi-base station positioning diagram for MUSIC under imperfect channel conditions, provided by an embodiment of this application.

[0029] Figure 10 This illustration shows a MUSIC localization map under a conventional active sensing system provided in an embodiment of this application;

[0030] Figure 11 This application provides a MUSIC positioning diagram under a passive sensing system according to an embodiment of the present application.

[0031] Figure 12 A schematic diagram of a transmission optimization device for a multi-base station integrated sensing system provided in an embodiment of this application is shown. Detailed Implementation

[0032] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0033] This embodiment provides a transmission optimization method for a multi-base station integrated sensing system, such as... Figure 1 As shown, the method includes:

[0034] Step 101: Construct a novel integrated sensing system comprising multiple integrated sensing transmitting base stations, one integrated sensing receiving base station, multiple communication users, and one sensing target. The novel integrated sensing system utilizes a communication channel to provide information transmission services to communication users and utilizes a sensing channel to perceive in real time the location coordinates of at least one sensing target, including the communication user, while the communication user is using the information transmission service. The sensing target is determined based on multiple targets within a preset range around the communication user's environment. The integrated sensing receiving base station is used to receive echoes.

[0035] The novel integrated sensing and communication (ISAC) system is a new technology system that deeply integrates communication and sensing functions. This system aims to simultaneously achieve efficient data transmission and accurate environmental perception, thereby meeting the future demands of wireless networks for multifunctionality, efficiency, and intelligence. By integrating communication and sensing technologies, the ISAC system enables wireless networks to perceive changes in the surrounding environment in real time, including the position, speed, and shape of objects, while transmitting data. This integration not only improves the network's flexibility but also enhances its adaptability to the environment and its level of intelligence. Key features of the ISAC system include:

[0036] 1. Functional Integration: The ISAC system organically combines communication and sensing functions, enabling resource sharing and collaborative work.

[0037] 2. High efficiency: By optimizing the communication and sensing processing flow, the ISAC system can improve the accuracy and real-time performance of sensing while ensuring the quality of data transmission.

[0038] 3. Intelligent: The ISAC system has intelligent processing capabilities, which can dynamically adjust communication parameters and strategies based on the perceived environmental information to adapt to different application scenarios and needs.

[0039] For application scenarios of the ISAC system, such as:

[0040] 1. Intelligent Transportation Systems: The ISAC system can be applied to communication and perception between vehicles to achieve vehicle-to-vehicle and vehicle-to-infrastructure cooperation, thereby improving traffic efficiency and safety.

[0041] 2. Smart Home: Through the ISAC system, smart home devices can more accurately sense user behavior and needs, and provide more personalized services.

[0042] 3. Wireless Sensor Networks: The ISAC system can optimize the communication and sensing performance of sensor networks, and improve network coverage and monitoring accuracy.

[0043] 4. Future Wireless Networks: The ISAC system is one of the important directions for the development of future wireless networks, and it can meet the needs of next-generation wireless networks such as 5G / 6G for multifunctionality, efficiency and intelligence.

[0044] Currently, for ISAC systems, some approaches focus on establishing accurate channel models within the near-field ISAC framework and improving system sensing performance through joint beam optimization while ensuring sensing requirements are met. However, these near-field communication frameworks primarily focus on single-target systems and do not address multi-user service and multi-target detection issues. Another approach utilizes ISAC base stations operating in full-duplex mode to receive echoes from multiple targets while communicating with multiple users. By reducing the base station's transmit power, the effectiveness of the system in handling sensing and communication tasks is ensured. However, these works mainly study single-base station communication and sensing scenarios and do not investigate the impact of multi-base station cooperation on communication and sensing performance. Furthermore, in confined spaces, due to multiple reflections and refractions of radio electromagnetic waves, the shadowing effect and frequency-selective fading effect of the wireless channel are significant, and the electromagnetic environment is complex with severe multipath effects. Therefore, in confined environments, it is necessary to consider the interference impact of channel uncertainty on communication and sensing performance in complex channel environments.

[0045] In the above embodiments of this application, a multi-ISAC base station scenario in near-field communication is constructed. Through the cooperation of multiple ISAC base stations, communication quality can be improved while also enhancing the performance of positioning sensing. Specifically, a novel integrated sensing system containing multiple ISAC base stations is constructed, for example... Figure 2 As shown, Figure 2 It includes two integrated sensing and sensing transmitting base stations, one integrated sensing and sensing receiving base station, one communication user, and one sensing target. Specifically, the communication user refers to an individual or device that transmits or receives information in the ISAC system, and the sensing target refers to an object or entity that is detected, identified, or tracked in the ISAC system.

[0046] In particular, Figure 2The novel near-field integrated sensing system constructed includes I integrated sensing transmitting base stations (each integrated sensing transmitting base station is represented by i), K communication users (each communication user is represented by k), one sensing target (represented by s), and one integrated sensing receiving base station (represented by o). In particular, the total number of antennas in the antenna array of one sensing integrated transmitting base station i is N. t =2N+1, the total number of antennas in the antenna array of a sensor-integrated receiving base station o is N. r = 2N+1, the antenna array consists of 2N+1 antennas, communication user k is a single-antenna communication user, and the inductive integrated receiving base station o is used to receive the echo. Assume all base stations (inductive integrated transmitting base station and inductive integrated receiving base station) are configured with a uniform linear array (ULA) with an antenna spacing of d. Therefore, the antenna aperture size is D = 2Nd, where N represents the total number of antennas. Typically, the boundary between the near field and far field can be defined by the Rayleigh distance 2D. 2 / λ is determined, where λ is the signal wavelength. It is assumed that all communication users and sensing targets are located in the near field, and their distance from the base station is less than 2D. 2 / λ. Therefore, let the coordinates of the integrated sensing and communication base station i be... The coordinates of the communication user are The target coordinates are [x s ,y s ].

[0047] Specifically, regarding perceived targets, perceived targets refer to objects in the environment surrounding the communication user whose location coordinates the system needs to perceive in real time. These targets may include other communication users, mobile devices, fixed obstacles, potential security threats, etc.

[0048] The new integrated sensing system can determine sensing targets based on preset rules or algorithms. For example, it can be configured to sense only specific types of devices or objects, or only targets active within a specific area. The system can also dynamically adjust sensing targets based on real-time conditions. For instance, when abnormal activity is detected in a certain area, the system can temporarily increase the sensing intensity for that area. In some cases, the system allows communication users or administrators to manually specify sensing targets.

[0049] Target identification typically relies on advanced sensor technologies and signal processing algorithms. Novel integrated sensing systems may utilize sensors such as radar, LiDAR, and cameras to capture information about the surrounding environment. Through signal processing and data analysis, these systems can identify and track the location and status of the sensed target.

[0050] A preset range refers to a defined area or distance in a novel integrated sensing system, used to determine which targets fall within its sensing range. The preset range can be set based on the application requirements and scenarios of the system. For example, in intelligent transportation systems, the preset range might be set to several hundred meters or even several kilometers around a vehicle. The sensing capabilities and signal transmission distance of the sensors also limit the size of the preset range. Therefore, these technical factors need to be considered when setting the preset range. In some cases, safety factors also need to be considered when setting the preset range. The specific size of the preset range varies depending on the application scenario. In typical novel integrated sensing systems, the preset range may range from tens of meters to hundreds of meters. In certain special applications, such as drone monitoring or remote sensing, the preset range may reach several kilometers or even further.

[0051] Step 102: When a communication user in a confined space uses the information transmission service based on the novel integrated sensing system, for the novel integrated sensing system under confined space conditions, construct an imperfect near-field communication channel model for the communication channel and an imperfect near-field sensing channel model for the sensing channel in the novel integrated sensing system.

[0052] Next, by considering imperfect channels, the model can more closely approximate real-world application scenarios, especially in enclosed spaces where signal transmission may be subject to various interferences and attenuations. This modeling approach helps improve the system's robustness in complex environments, ensuring the reliability and stability of information transmission. Imperfect channel models can more accurately reflect channel conditions, thereby helping the system optimize resource allocation. For example, parameters such as transmit power and coding rate can be dynamically adjusted based on channel quality to maximize information transmission efficiency. For sensing targets, constructing an imperfect near-field sensing channel model helps to more accurately estimate the target's position, state, and other information. This is particularly important for applications requiring high-precision sensing (such as indoor positioning and environmental monitoring). By considering imperfections, the system can better adapt to changes in different environments and scenarios. For example, in enclosed spaces, channel conditions may change due to factors such as personnel movement and equipment placement. Imperfect channel models can capture these changes, making the system more flexible and adaptable.

[0053] In conclusion, constructing imperfect communication and sensing channel models is of great significance for improving the performance of novel integrated sensing and communication systems. These models not only enhance the robustness and adaptability of the system but also optimize resource allocation and improve sensing performance, providing strong support for communication and sensing technologies.

[0054] Optionally, step 102, "constructing the imperfect near-field communication channel model for the communication channel and the imperfect near-field sensing channel model for the sensing channel in the novel integrated sensing system," specifically includes:

[0055] Step 1021: Construct the near-field communication channel vector for the communication channel and the near-field sensing channel matrix for the sensing channel in the novel integrated sensing system.

[0056] Step 1022: Based on the near-field communication channel vector, construct an imperfect near-field communication channel model for the communication channel.

[0057] Step 1023: Based on the near-field sensing channel matrix, construct an imperfect near-field sensing channel model for the sensing channel, wherein the constructed imperfect near-field communication channel model is as follows:

[0058]

[0059] h i,k =β i,k a i (x k ,y k ),

[0060] For the imperfect near-field communication channel model between the integrated inductive and sensory base station i and the communication user k, h i,k h is the near-field communication channel vector between the integrated inductive transmitting base station i and the communication user k. e For the communication channel state information error that follows a complex Gaussian distribution with mean 0 and variance 1, h i,k with h e They are independent of each other, ξ is an error factor ranging from 0 to 1, and β i,k For the communication channel gain between the integrated induction and sensing base station i and the communication user k, a i (x k ,y k ) indicates that when the coordinates of communication user k are [x k ,y k ], and the coordinates of the integrated sensing and communication base station i are [x i ,y i At that time, the near-field array response vector between the integrated inductive transmitting base station i and the communication user k;

[0061] The imperfect near-field sensing channel model constructed is as follows:

[0062]

[0063] For the imperfect near-field sensing channel model between the integrated sensing transmitting base station i and the integrated sensing receiving base station o, G i G represents the near-field sensing channel matrix between the integrated sensing transmitting base station i and the integrated sensing receiving base station o. eLet ξ be the error of the sensing channel state information, which follows a complex Gaussian distribution with mean 0 and variance 1, and let β be the error factor ranging from 0 to 1. i,o b is the communication channel gain between the integrated sensing transmitting base station i and the sensing target s to the integrated sensing receiving base station o. o (x s ,y s () represents the response vector between the sensing target s and the integrated sensing and sensing base station o. The near-field array response vector a between the sensing target s and the integrated sensing base station i. i (x s ,x s The transpose of ).

[0064] In the above embodiments of this application, the coordinates of the nth antenna of the integrated sensing and communication base station i are defined as follows: Consider any communication user or sensing target with coordinates [x, y]. Then, the distance r of this communication user or sensing target from the nth antenna of the integrated sensing and communication base station i is... i,n (x, y) can be calculated as:

[0065]

[0066] r i,n (x, y) is used to represent the coordinates of any communication user or sensing target being [x, y], and the coordinates of the integrated sensing and communication base station i being [x, y]. i ,y i When ], the distance from the communication user or sensing target to the nth antenna of the integrated sensing and communication base station i.

[0067] Set r i r represents the distance from the integrated sensing and communication base station i to the communication user or sensing target. i for:

[0068]

[0069] r i (x, y) is used to represent the coordinates of any communication user or sensing target being [x, y], and the coordinates of the integrated sensing and communication base station i being [x, y]. i ,y i At that time, the distance from the communication user or sensing target to the integrated sensing and communication base station i is measured.

[0070] Since the channel gain of each antenna is approximately the same as that of the communication user or sensing target in the near-field Fresnel region, the channel gain of all links can be calculated as the free-space path loss of the central link. Therefore, the communication channel gain of the integrated sensing and communication base station i can be characterized by the following formula:

[0071]

[0072] β i Let ρ be the communication channel gain of the integrated sensing and communication base station i, used to measure the quality of the communication channel between base station i and the communication user or sensing target. Let ρ represent the reference distance used to calculate path loss. In a communication system, path loss is the phenomenon where a signal gradually weakens during propagation due to various factors (such as distance, obstacles, etc.). ρ is usually chosen as 1 meter as the reference distance for calculating path loss, that is:

[0073] Used to represent path loss at a reference distance of 1 meter.

[0074] λ represents wavelength, which is usually related to frequency. In electromagnetic wave propagation, wavelength is an important characteristic of the wave, and it is inversely proportional to the wave frequency. In communication systems, wavelength affects the signal propagation characteristics and antenna performance. j represents the path loss exponent, a constant used to describe the rate attenuation of a signal with increasing distance during transmission. Specifically: the larger the path loss exponent j, the faster the signal attenuates during transmission. The smaller the path loss exponent j, the slower the signal attenuates.

[0075] Therefore, in the integrated sensing and communication base station i, the communication channel from the nth antenna to the communication user or sensing target can be represented as:

[0076]

[0077] h i,n (x,y) represents the coordinates of any communication user or sensing target being [x,y], and the coordinates of the integrated sensing and communication base station i being [x,y]. i ,y i In the integrated sensing and communication base station i, the communication channel from the nth antenna to the communication user or sensing target.

[0078] Therefore, the near-field communication channel vector between the integrated sensing and communication base station i and the communication user or sensing target is... It can be represented as (N) t N represents the total number of antennas in the antenna array of a sensing integrated transmission base station. t =2N+1):

[0079] h i (x,y)=[h i,1 (x,y),…,h i,N (x,y)] T =β i a i (x,y).

[0080] Among them, a i (x,y) represents the coordinates of any communication user or sensing target being [x,y], and the coordinates of the integrated sensing and communication base station i being [x,y]. i ,y i At that time, the near-field array response vector between the integrated sensing and communication base station i and the communication user or sensing target. i The nth term of (x, y) can be represented as:

[0081]

[0082] The communication channel gain between the integrated sensing base station i and the communication user k is expressed as β. i,k Then the near-field communication channel vector between the integrated induction transmitting base station i and the communication user k can be expressed as:

[0083] h i,k =β i,k a i (x k ,y k ).

[0084] h i,k Let be the near-field communication channel vector between the integrated inductive and sensory base station i and the communication user k.

[0085] Based on b o (x,y) defines the response vector between the sensing target s and the integrated sensing base station o, and b o The nth term in (x,y) can be represented as:

[0086]

[0087] Where, r o,n (x, y) represents the distance from the nth antenna in the integrated sensing and sensing base station o to the sensing target s, r o (x,y) represents the distance from the integrated sensing and sensing base station o to the sensing target s;

[0088] The communication channel gain between the integrated sensing transmitting base station i and the sensing target s and the integrated sensing receiving base station o is expressed as β. i,s,o .

[0089] Therefore, the near-field channel matrix between the integrated sensing transmitting base station i and the integrated sensing receiving base station o can be expressed as:

[0090]

[0091] G i Let β be the near-field sensing channel matrix between the integrated sensing transmitting base station i and the integrated sensing receiving base station o. i,s,ob is the communication channel gain between the integrated sensing transmitting base station i and the sensing target s to the integrated sensing receiving base station o. o (x s ,y s () represents the response vector between the sensing target s and the integrated sensing and sensing base station o. The near-field array response vector a of the sensing target s and the integrated sensing base station i. i (x s ,x s The transpose of ).

[0092] The imperfect channel model is constructed as follows:

[0093]

[0094] Where h represents a perfect communication channel, h e This represents the corresponding communication channel state error information, which follows a complex Gaussian distribution with zero mean and one variance, and is independent of h. ξ represents the error factor ranging from 0 to 1, used to indicate CSI accuracy.

[0095] Ultimately, the imperfect near-field communication channel model and the imperfect near-field sensing channel model are as follows:

[0096]

[0097] h i,k =β i,k a i (x k ,y k ),

[0098]

[0099] Step 103: Based on the constructed imperfect near-field communication channel model, construct a communication rate performance index for evaluating the information transmission performance of the communication channel, and based on the constructed imperfect near-field sensing channel model, construct a sensing performance index for evaluating the sensing capability of the sensing channel. The information transmission performance is characterized by the communication rate of the communication signal received by the communication user, and the sensing capability is characterized by the change in position coordinates of the sensing target perceived by the novel integrated sensing system.

[0100] Furthermore, communication rate performance metrics can directly quantify the information transmission efficiency of a novel integrated sensing and communication system under given channel conditions. By comparing the communication rates of different systems or configurations, the strengths and weaknesses of the systems and their potential for improvement can be intuitively evaluated. Adjusting system parameters (such as transmit power, antenna configuration, and coding methods) can maximize the communication rate, thereby improving the overall system performance. In situations with limited resources, communication rate performance metrics can help the system allocate resources rationally, such as time-frequency resources and power resources. By optimizing resource allocation, the system's resource utilization efficiency can be improved while ensuring communication quality.

[0101] Sensing performance metrics can directly quantify the ability of a novel integrated sensing system to detect changes in target location. By comparing sensing performance metrics under different systems or configurations, the system's sensing accuracy and stability can be evaluated. Sensing performance metrics provide system designers with clear directions for improving sensing accuracy. Optimizing sensing algorithms, enhancing signal processing capabilities, or improving antenna configurations can improve the system's sensing accuracy, thereby acquiring target location information more accurately. Sensing performance metrics can also be used to evaluate the system's robustness under different environmental conditions. By testing the system's sensing performance under different noise levels, interference conditions, or channel variations, weaknesses and limitations of the system can be revealed, providing a basis for further system improvements.

[0102] By simultaneously constructing communication rate performance indicators and sensing performance indicators, we can promote the deep integration of communication and sensing functions.

[0103] Optionally, step 103, "based on the constructed imperfect near-field communication channel model, constructing a communication rate performance index for evaluating the information transmission performance of the communication channel," specifically includes:

[0104] Step 1031: Construct a communication signal representation model for communication users. Based on the communication signal representation model and the constructed imperfect near-field communication channel model, construct a communication rate performance index for evaluating the information transmission performance of the communication channel. The communication signal representation model for communication users is as follows:

[0105]

[0106] y k [t] represents the communication signal received by communication user k at time t. Let x be the sum of communication signals received by communication user k from each of the I integrated sensing base stations in the new integrated sensing system. i [t] represents the communication signal transmitted by the integrated inductive transmitting base station i at time t, z k [t] represents a sequence of values ​​for communication user k with a mean of 0 and a variance of . Normally distributed additive white Gaussian noise, For the imperfect near-field communication channel model between the integrated inductive and sensory base station i and the communication user k, for transpose;

[0107] The communication rate performance indicators are as follows:

[0108]

[0109] R k This is used to calculate the maximum communication rate that communication user k can achieve when using information transmission services based on a novel integrated sensing system. w represents the total communication signal power received by communication user k from each of the I integrated sensing base stations in the new sensing system. i,k The power of the communication signal transmitted from the integrated sensing and communication base station i to the communication user k. This represents the sum of interference signal power from other communication users j to communication user k. R is the interference term generated during the transmission of sensing signals. i,s To perceive the covariance matrix, The variance is the additive white Gaussian noise.

[0110] In the above embodiments of this application, the communication signal received by communication user k is defined as:

[0111]

[0112] Among them, z k [t] represents a sequence with a mean of 0 and a variance of . The noise is additive white Gaussian noise with a normal distribution. Therefore, the communication rate achievable by user k, i.e., the communication rate performance index, is:

[0113]

[0114] Therefore, the information transmission performance is characterized by the communication rate of the communication signal of the new integrated sensing system, which yields the communication rate performance index. For communication users, a higher communication rate indicates a stronger communication capability.

[0115] Optionally, for step 103, "based on the constructed imperfect near-field sensing channel model, construct a sensing performance index for evaluating the sensing capability of the sensing channel," the specific steps include:

[0116] Step 1032: Based on the constructed imperfect near-field sensing channel model, construct an echo signal representation model of the response matrix of the sensing target received by the integrated sensing and sensing base station.

[0117] Step 1032: Based on the echo signal characterization model and the Cramer-Rao bound, which directly measures the limit of sensing performance, construct a sensing performance index for evaluating the sensing capability of the sensing channel. The Cramer-Rao bound is calculated using the inverse of the Fisher information matrix related to preset unknown parameters. The echo signal characterization model is as follows:

[0118]

[0119] y o [t] represents the echo signal of the response matrix of the sensed target received by the integrated sensing and sensing base station o.

[0120] In a novel integrated sensing system, the sum of communication signals transmitted by each of the I integrated sensing base stations is x. i [t] represents the communication signal transmitted by the integrated inductive transmitting base station i at time t, z s [t] represents a signal with mean 0 and variance for the integrated sensing base station i. Normally distributed additive white Gaussian noise, An imperfect near-field sensing channel model between the integrated sensing transmitting base station i and the integrated sensing receiving base station o;

[0121] The constructed perception performance metrics are expressed as follows:

[0122]

[0123] CRB is a perception performance metric, applicable to location coordinates [x... s y s The perceived target s, CRB(x) s (x) represents the x-coordinate of the horizontal position. s Perception performance metrics at the location, CRB(y) s (y) represents the vertical position coordinate. s The perception performance index at the location, ξ is a preset unknown parameter, determined by the x-coordinate of the perceived target s. s y-coordinate s and the preset specific unknown parameters β of the integrated sensing and communication base station i i Composition, β i By the real part Re(β) i ) and imaginary part Im(β) i The system is composed of (F) and F is the Fisher information matrix used to describe the sensing capability of the sensing channel. Let CRB represent the Cramer-Rao bound of the inverse matrix of the Fisher information matrix under the preset unknown parameter ξ, where C is a preset constant and I is the total number of integrated sensing base stations in the new integrated sensing system.

[0124] In the above embodiments of this application, for an imperfect near-field sensing channel model, the echo signal of the response matrix of the sensing target received by the integrated sensing and sensing base station o is:

[0125]

[0126] The sensing objective is to estimate target parameter information from the echo signal samples received throughout the coherent time block. The sensing performance metric is defined as the Cramer-Rao bound (CRB), which provides a lower bound on the mean square error and has a closed-form expression.

[0127] Since the Cramé-Rao bound (CRB) can directly measure the limit of perception performance, it can be used as an indicator of perception performance. The Cramé-Rao bound matrix (CRB) can be calculated from the inverse of the Fisher Information Matrix (FIM) associated with the preset unknown parameters.

[0128] Define the preset unknown parameters as in, Secondly, the vectorized received echo signal u = vec(Y) s ), Y s =[y o [1],…,y o [T]].

[0129] The FIM matrix can be defined as follows:

[0130]

[0131] Based on the above derivation, the specific FIM is expressed as:

[0132]

[0133] definition in:

[0134]

[0135] From the above derivation, we can conclude that CRB is:

[0136]

[0137] In particular, optimizing sensing performance while ensuring communication quality is an important goal of new integrated sensing systems.

[0138] In novel integrated sensing and communication systems, sensing performance metrics can be used to quantify the sensing channel's ability to detect environmental changes. This metric can represent sensing error, delay, uncertainty, or any other measure related to sensing accuracy. Generally, a smaller sensing performance metric indicates a more accurate perception of environmental changes. A smaller metric typically indicates a smaller deviation between the perceived result and the actual environmental state, meaning the sensing system can more accurately reflect the environmental state. In dynamic environments, a smaller metric can also mean that the sensing system can detect environmental changes more quickly, thus responding more promptly. A smaller metric can also indicate that the sensing system maintains stable sensing performance under different environmental conditions, i.e., it has high reliability.

[0139] Therefore, when building and optimizing a sensing system, the goal is usually to minimize sensing performance indicators in order to improve the accuracy, timeliness and reliability of sensing.

[0140] Step 104: The objective function is to minimize the change in the position coordinates of the perceived target by the novel integrated sensing system, as evaluated based on sensing performance indicators. The first constraint is that the communication rate of the communication signal received by the communication user meets the user's requirements, as evaluated based on communication rate performance indicators. The second constraint is that the total power of the novel integrated sensing system meets the requirements. The third constraint is that the covariance of the transmitted signal from the integrated sensing base station meets the positive definiteness requirement. The optimal configuration of each integrated sensing base station for the communication channel and sensing channel in the novel integrated sensing system is then solved. The integrated sensing base station includes an integrated sensing transmitting base station and an integrated sensing receiving base station. During the solution process, the objective function is transformed using the Schur complement theorem for non-convex functions, and the first constraint is transformed using semi-definite relaxation techniques for non-convex constraints.

[0141] In the above embodiments of this application, when solving for the optimal configuration of each integrated sensing base station (including transmitting base station and receiving base station) in the novel integrated sensing system for the communication channel and sensing channel, the Schur complement theorem can be used to transform the non-convex objective function into a convex objective function. For the communication demand constraint (the first constraint condition), a semidefinite relaxation theorem is used to transform it into a convex constraint. Specifically:

[0142] 1. Objective function: Minimize the change in the position coordinates of the perceived target. Based on the evaluation of perception performance indicators, the new integrated sensory system is required to minimize the change in the position coordinates of the perceived target. The non-convex objective function can be transformed into a convex objective function using the Schur complement theorem.

[0143] 2. First Constraint: The rate of communication signals received by the communication user meets the requirements. Based on the communication rate performance index evaluation, the rate of communication signals received by the user must meet its requirements. A semidefinite relaxation technique is used to transform this non-convex constraint into a convex constraint. This ensures that the communication rate meets the user's requirements while maintaining the convexity of the problem, making it easier to solve. Semidefinite relaxation introduces additional variables and matrices, transforming the original non-convex problem into a more easily solvable convex optimization problem. The transformed convex constraint allows the problem to be solved in polynomial time, thereby improving the solution efficiency and feasibility.

[0144] 3. Second constraint: The total power of the system must meet the requirements; the total power of the novel integrated sensing system cannot exceed a certain limit. This constraint is usually linear and therefore does not require special transformation. It is directly used as one of the constraints in the optimization problem, limiting the total power consumption of the system.

[0145] 4. Third constraint: The covariance matrix of the communication signal must be positive definite. The covariance matrix of the communication signal transmitted by the new integrated inductive system must satisfy the positive definite property. Positive definite constraints are common in convex optimization problems and can be directly considered in the optimization problem. This ensures that the covariance matrix of the communication signal is positive definite, thereby guaranteeing the stability and reliability of the signal.

[0146] Therefore, in the solution process, the objective function and these three constraints can be considered comprehensively, and a corresponding optimization problem can be constructed. By using semidefinite relaxation techniques to handle non-convex constraints, the original problem can be transformed into a convex optimization problem, which can then be solved using existing convex optimization algorithms.

[0147] Ultimately, the solution yields the optimal configurations of each integrated sensing base station for both the communication and sensing channels. These configurations satisfy all constraints and achieve optimal system performance. This solution method not only improves the system's efficiency and feasibility but also provides strong theoretical support for the design and optimization of novel integrated sensing systems.

[0148] In practical implementation scenarios, the optimal configuration of each integrated sensing base station for the communication and sensing channels, as determined by the final solution, can be reflected in the following aspects:

[0149] 1. Frequency and Time Slot Allocation: For communication channels, optimal configuration means allocating the best frequency resources and time slots to different communication users or services to ensure that communication rates meet requirements and minimize interference. For sensing channels, specific frequency ranges or time slots can be selected for target sensing to avoid conflicts with communication channels while ensuring the accuracy and efficiency of sensing.

[0150] 2. Power Control: The base station adjusts its transmission power based on the solution results to ensure the communication signal can cover the required range while meeting the system's total power limit. In terms of sensing, appropriate power control ensures the sensing signal has sufficient strength to accurately detect targets, while avoiding unnecessary interference with the surrounding environment.

[0151] 3. Antenna Configuration and Beamforming: The antenna array and beamforming technology of a base station can be adjusted according to optimal configuration to optimize communication and sensing performance. For example, by adjusting the antenna direction and beamwidth, it can be ensured that communication signals are accurately delivered to the target user, while improving the accuracy and range of target sensing.

[0152] 4. Signal Processing and Algorithm Optimization: The signal processing algorithms and protocols within the base station may be adjusted according to the optimal configuration to improve communication speed and sensing accuracy. This may include using more advanced coding and modulation techniques, optimizing signal detection algorithms, and improving target tracking and localization algorithms.

[0153] 5. Resource Scheduling and Coordination: In a multi-base station environment, optimal configuration may involve resource scheduling and coordination among different base stations to ensure the performance and efficiency of the entire system. This can include coordination of frequency reuse, time slot allocation, power control, etc., among base stations to avoid interference and improve system capacity.

[0154] In summary, the optimal configuration obtained can be reflected in multiple aspects, including base station frequency and time slot allocation, power control, antenna configuration and beamforming, signal processing and algorithm optimization, and resource scheduling and coordination. These configuration methods can be adjusted and optimized according to the needs and constraints of the actual scenario to ensure that the new integrated sensing system can achieve the best performance in practical applications.

[0155] Optionally, regarding step 104, "taking the minimum change in the position coordinates perceived by the novel integrated sensing system for the sensing target as the objective function, based on the sensing performance index, the first constraint condition being that the communication rate of the communication signal received by the communication user meets the communication user's needs as the first constraint condition, the second constraint condition being that the total power of the novel integrated sensing system meets the second constraint condition, and the third constraint condition being that the covariance of the signal transmitted by the integrated sensing base station meets the positive definiteness, and solving for the optimal configuration of each integrated sensing base station for the communication channel and sensing channel in the novel integrated sensing system, in the solution, the objective function is transformed into a non-convex function using the Schur complement theorem, and the first constraint condition is transformed into a non-convex constraint using a semi-definite relaxation technique," refer to Figure 3 As shown, it specifically includes:

[0156] Step 1041: Based on the objective function formula, minimize the Cramer-Rao bound of the novel integrated sensory system for the perceived target, as evaluated based on the sensing performance index. The objective function formula is:

[0157]

[0158] w i,k f is the communication beam transmitted by the integrated sensing and communication base station i to the communication user k. i The sensing beam of the integrated sensing and communication base station i The Cramero boundary is used for estimating the position coordinates.

[0159] Step 1042: Based on the first constraint formula, the communication rate of the communication signal received by the communication user, as evaluated based on the communication rate performance index, meets the communication user's requirements. The first constraint formula is:

[0160]

[0161] R k Let R be the maximum communication rate that user k can achieve when using information transmission services based on a novel integrated sensing system, as evaluated based on communication rate performance indicators. min K represents the preset minimum communication rate required by communication users, and K represents the total number of communication users using the new integrated sensing system.

[0162] Step 1043: During the solution process, the Schur complement theorem is used to transform the objective function belonging to the non-convex function, resulting in the transformed objective function belonging to the convex function. Furthermore, the positive semidefinite relaxation technique is used to transform the first constraint condition belonging to the non-convex constraint, resulting in the transformed first constraint condition belonging to the convex constraint. The transformed objective function belonging to the convex function is:

[0163]

[0164] The transformed first constraint condition belonging to convex constraints is:

[0165]

[0166] For t from 1 to 2, a t Summation, e t With a t e is an auxiliary variable. t Let t represent a unit vector where the t-th element is 1 and the rest are 0.

[0167] Step 1044: Based on the second constraint formula, constrain the total power of the novel integrated sensing system, wherein the second constraint formula is:

[0168]

[0169] I represents the total number of integrated sensing base stations in the new integrated sensing system, K represents the total number of communication users using the new integrated sensing system, and w i,k The power of the communication signal transmitted from the integrated sensing and communication base station i to the communication user k. For w i,k The conjugate transpose of R i,s Let P be the covariance matrix of the dedicated sensing signal, tr be the trace of the sum of all diagonal elements in the matrix, and P be the covariance matrix of the dedicated sensing signal. max This is the preset maximum total power requirement.

[0170] Step 1045: Based on the third constraint formula, the covariance of the transmitted signal of the integrated sensing and communication base station is constrained to satisfy positive definiteness, wherein the third constraint formula is:

[0171]

[0172] The covariance of the signal transmitted by the integrated sensing and communication base station i must satisfy positive definiteness, where I is the total number of integrated sensing and communication base stations in the new integrated sensing and communication system.

[0173] In the above embodiments of this application, the communication signal (transmission signal) transmitted by the integrated sensing and communication base station i at time t is defined as:

[0174] x i [t]=∑ k∈K w i,k c k [t]+f i s i [t];

[0175] Among them, w i,k ∈C N×1 This indicates that the symbol c k [t] Beamforming transmitted to communication users, f i ∈C N×1 Represents the transmission sensing symbol s i Beamforming of [t]. Assume the information symbols are independently distributed and have unit power. Define the covariance matrix of the dedicated sensing signal as...

[0176] The covariance matrix of the communication signal transmitted by the integrated inductive transmitting base station i is:

[0177]

[0178] Define a collaboratively-aware resource allocation model. Since the optimization problem aims to improve the sensing performance of coordinate estimation, minimizing the Cramer-Rao bound (CRB) of coordinate estimation while ensuring the communication user needs are met, the optimization problem can be expressed as:

[0179]

[0180] Wherein, C1 is the constraint to guarantee communication requirements (first constraint), C2 is the total system power constraint (second constraint), and C3 is the constraint of positive covariance (third constraint). Since the objective function is a non-convex function and constraint C1 is also a non-convex constraint, it cannot be solved directly.

[0181] Since the original problem is difficult to solve, a solution strategy based on semidefinite relaxation techniques is proposed.

[0182] For the non-convex constraint C1, a semidefinite relaxation technique is used to transform the constraint:

[0183] set up

[0184]

[0185] And order

[0186] Therefore, the communication rate can be rewritten as:

[0187]

[0188] Therefore, constraint C1 can be replaced with:

[0189]

[0190] in, Therefore, this constraint has been transformed into a convex constraint. Since the objective problem is a non-convex function, it cannot be solved directly. First, the optimization problem needs to be preliminarily transformed, and corresponding constraints introduced:

[0191]

[0192] At this point, since C4 is a non-convex constraint, it can be transformed using Schuler's complement theorem. Schuler's complement theorem:

[0193] For block-symmetric matrices The Schul complement of matrix A in M ​​is:

[0194] S = [B - XA] -1 X T ];

[0195] The following properties hold true:

[0196]

[0197] Therefore, the non-convex constraint C4 can be transformed into:

[0198]

[0199] Based on the above derivation, the non-convex optimization problem is transformed into a convex optimization problem as follows:

[0200]

[0201] Specifically, ≥ represents a positive definite sign. Based on the above description, this optimization problem is transformed into a convex optimization problem, which can be solved using the convex optimization toolkit in MATLAB.

[0202] Specifically, in the dynamic optimization process of the novel integrated sensing system, the goal is to minimize the sensing performance index estimated based on the coordinates of the sensing target's location.

[0203] 1. Target location coordinates: This refers to the specific location or area that the sensing system needs to focus on or detect. In new integrated sensing systems, it may be necessary to sense the user's location, movement trajectory, or changes in the surrounding environment in real time.

[0204] 2. Sensing Performance Index Estimation: Based on the coordinates of the perceived target's location, the sensing performance index of the sensing system at that location or area can be estimated. This index may reflect the sensing system's accuracy, timeliness, or reliability at that location or area.

[0205] 3. Dynamic Optimization and Allocation of Targets: Using the sensing performance indicators estimated based on the location coordinates of the sensed targets as the optimization targets means that during system operation, the parameters or configurations of the sensing system will be continuously adjusted and optimized to minimize these indicators. This helps improve the overall performance of the sensing system throughout the entire service area.

[0206] Semidefinite relaxation (SDR) is a commonly used technique in mathematical optimization, particularly suitable for combinatorial optimization problems or non-convex optimization problems that are difficult to solve directly. By transforming the original problem into a semidefinite programming (SDP) problem, it can be solved using existing efficient algorithms, thereby finding an approximate solution or bound to the original problem.

[0207] In resource allocation problems, semidefinite relaxation can be used to relax some constraints, making the problem easier to handle. Specifically, resource allocation problems typically involve allocating limited resources to multiple tasks or users to maximize a utility function or satisfy certain needs. These problems are often NP-hard, and direct solutions can be very time-consuming. The steps for applying semidefinite relaxation in resource allocation are as follows:

[0208] 1. Problem Modeling: The resource allocation problem is modeled as an optimization problem, which typically includes an objective function (such as maximizing total utility) and a set of constraints (such as resource limitations).

[0209] 2. Relaxing constraints: Relaxing certain non-convex constraints (such as quadratic constraints, integer constraints, etc.) in the original problem into positive semi-definite constraints. This usually involves introducing a matrix variable and requiring that the matrix is ​​positive semi-definite.

[0210] 3. Transform into an SDP problem: The relaxed problem is transformed into a semi-positive definite programming problem. SDP problems have a specific form and can be solved using existing SDP solvers.

[0211] 4. Solve the SDP problem: Use the SDP solver to solve the relaxed SDP problem and obtain a solution (usually a matrix).

[0212] 5. Extracting the solution to the original problem: Extracting an approximate solution to the original problem from the solution to the SDP problem. This may involve some post-processing of the matrix, such as taking its diagonal elements or performing eigenvalue decomposition.

[0213] 6. Verification and Evaluation: Verify whether the extracted solution satisfies the constraints of the original problem (it may not be fully satisfied because relaxation was performed). Evaluate the quality of the solution, such as calculating its objective function value or the difference between it and the optimal solution.

[0214] Optionally, when the integrated sensing and communication base station uses the sensing channel to sense in real time the location coordinates of at least one sensing target, including the communication user, while the communication user is using the information transmission service, the method further includes:

[0215] Step 105: Improve the passive sensing multi-signal classification algorithm with orthogonal subspace so that the novel integrated sensing system can use the sensing channel and the improved passive sensing multi-signal classification algorithm to sense the location coordinates of at least one sensing target, including the communication user, in real time when the communication user is using the information transmission service. Specifically, the location coordinates of the sensing target sensed in real time using the improved passive sensing multi-signal classification algorithm are as follows:

[0216]

[0217] P MUSIC (xs ,y s ) represents the location coordinates of the sensing target as perceived in real time using the improved passive sensing multi-signal classification algorithm, and I represents the total number of integrated sensing base stations in the new integrated sensing system.

[0218] In the above embodiments of this application, for the localization function of the sensing task, a passive sensing MUSIC algorithm based on orthogonal subspace can be designed for localization (the full English name of the passive sensing MUSIC algorithm is "Passive Sensing Multi-Signal Classification," which can be translated into Chinese as "Passive Sensing Multi-Signal Classification Algorithm." This algorithm is commonly used in the field of signal processing, especially when it is necessary to passively, i.e., without emitting signals, sense and analyze multiple signals present in the environment). Considering the signal received by base station o, it can be represented as:

[0219]

[0220] in,

[0221] Consider using signals The output signal after matched filtering of the received signal is:

[0222] Y o =A(x s ,y s )X+n;

[0223] in, n is the output noise vector after matched filtering. Since the signal and noise are independent, the covariance of the signal matrix is:

[0224]

[0225] Eigenvalue decomposition can divide the signal into two parts: signal and noise. Among them, U n This is the noise subspace corresponding to the smallest eigenvalue. Under ideal conditions, the signal subspace and the noise subspace are orthogonal to each other, that is, the steering vector of the signal subspace is orthogonal to the noise subspace.

[0226]

[0227] in

[0228] However, due to the presence of noise, A i (x s ,y s ) and U nThey are not completely orthogonal, therefore we need to find the estimated value (x). s ,y s This is achieved by minimizing the search, that is:

[0229]

[0230] Therefore, the spectral estimation formula for the two-dimensional MUSIC algorithm is:

[0231]

[0232] Therefore, when x and y are the coordinates of the estimated target, a peak will appear in the spectral function by searching for the spectral peak.

[0233] By applying the technical solution of this embodiment, a passive sensing system and cooperative sensing method for near-field communication with multiple ISAC base stations under imperfect channel conditions in a complex communication environment within a confined space are constructed. An optimal resource allocation algorithm based on semidefinite relaxation and a passive sensing multiple signal classification (MUSIC) algorithm are designed to maximize sensing performance while ensuring communication requirements. The optimal resource allocation algorithm based on semidefinite relaxation transforms non-convex constraints in the optimization problem into convex constraints, and converts the non-convex objective function into a convex objective function using Schur's complement theorem. Simultaneously, a passive sensing multiple signal classification algorithm is proposed, achieving target localization based on a high-resolution estimation method using orthogonal subspaces.

[0234] The technical effects of this solution can be seen from the following aspects after applying the technical solution of this embodiment:

[0235] Regarding the construction of performance metrics, Figure 4 The graph illustrates the relationship between imperfect channel factors and sensing performance. The horizontal axis represents communication requirement, i.e., the communication rate achievable by each user, and the vertical axis represents the Cramer-Rao boundary (CRB). As communication requirement gradually increases, sensing performance initially remains constant. However, upon reaching a certain threshold, sensing performance deteriorates, indicating a trade-off between communication and sensing. Furthermore, as the error factor increases, sensing performance continuously worsens. Therefore, imperfect channels have a significant impact on sensing performance.

[0236] Figure 5A comparison chart of passive sensing and traditional active sensing systems was evaluated. The coordinates of the transmitting base station were set to [0,0], the receiving base station o in the passive sensing system was set to [0,30], and the initial coordinates of the sensing target were set to [0,5]. The horizontal axis represents the distance between the sensing target and the transmitting base station (TargetDistance), and the vertical axis represents the Cramer-Rao boundary (CRB). As the target distance increases, the sensing performance of the traditional active sensing system gradually deteriorates. In contrast, the sensing performance of the passive sensing system initially deteriorates, then improves, and finally deteriorates again with distance. This is because initially, as the sensing target moves further away from the transmitting base station, the sensing performance deteriorates; as the target moves closer to the receiving base station o, the sensing performance improves; and finally, as the target moves further away from the receiving base station o, the sensing performance deteriorates again. Overall, the passive sensing system outperforms the traditional active sensing system.

[0237] Figure 6 The graph illustrates the relationship between the number of users and base stations and sensing performance. As the number of users increases, sensing performance deteriorates. Conversely, as the number of base stations increases, sensing performance improves. When communication demand increases, overall performance becomes more stable, and the communication threshold that causes sensing performance to deteriorate increases. This phenomenon demonstrates that multi-base station collaborative sensing helps improve the robustness of the entire system's sensing capabilities.

[0238] Figure 7 This graph illustrates the relationship between power and sensing performance. As power resources increase, sensing performance improves. Compared to single-base station systems, multi-base station systems exhibit more stable sensing performance.

[0239] Figure 8 and Figure 9 The figures show the MUSIC normalized spectra for coordinate estimation under imperfect channel conditions for single-base station and multi-base station systems, respectively. In this scenario, the number of antennas is set to 64, the error factor to 0.5, the target coordinates to be [20,10], and the coordinates of the two base stations to be [0,0] and [40,0], respectively. It can be seen that in the imperfect channel scenario with sufficient antennas, the single-base station system loses the target coordinate information, while the multi-base station system can determine the target coordinates by combining information from different base stations. The spectra in the figures represent the spectrum.

[0240] Figure 10 and Figure 11The images show the MUSIC normalized spectra of traditional active sensing and passive sensing under imperfect channel conditions. In this scenario, the number of antennas is set to 16, the error factor to 0.5, the target coordinates to be [20, 10], and the coordinates of the two base stations to be [0, 0] and [40, 0], respectively. In the passive sensing system, the receiving base station o has coordinates to be [40, 40]. It can be seen that in imperfect channel conditions with a limited number of antennas, the traditional active sensing system cannot distinguish the target coordinates, but the passive sensing system using the proposed passive sensing MUSIC algorithm achieves good localization results.

[0241] Furthermore, as Figure 1 In terms of specific implementation, this application provides a transmission optimization device for a multi-base station integrated sensing system, such as... Figure 12 As shown, the device includes:

[0242] A novel integrated sensing system construction module 201 is used to construct a novel integrated sensing system comprising multiple integrated sensing transmitting base stations, one integrated sensing receiving base station, multiple communication users, and one sensing target. The novel integrated sensing system utilizes a communication channel to provide information transmission services to communication users and utilizes a sensing channel to perceive in real time the location coordinates of at least one sensing target, including the communication user, while the communication user is using the information transmission service. The sensing target is determined based on multiple targets within a preset range around the communication user's environment. The integrated sensing receiving base station is used to receive echoes.

[0243] The imperfect channel model construction module 202 is used to construct, for the new integrated sensing system under closed space conditions, an imperfect near-field communication channel model for the communication channel and an imperfect near-field sensing channel model for the sensing channel when a communication user in a closed space uses information transmission services based on the new integrated sensing system.

[0244] The performance index construction module 203 is used to construct a communication rate performance index for evaluating the information transmission performance of the communication channel based on the constructed imperfect near-field communication channel model, and to construct a perception performance index for evaluating the perception capability of the perception channel based on the constructed imperfect near-field sensing channel model. The information transmission performance is characterized by the communication rate of the communication signal received by the communication user, and the perception capability is characterized by the change in position coordinates of the sensing target perceived by the novel integrated sensing system.

[0245] The optimal configuration solution module 204 is used to solve for the optimal configuration of each integrated sensing base station in the new integrated sensing system for the communication channel and sensing channel, with the objective function being the minimum change in the position coordinates of the sensing target perceived by the new integrated sensing system based on the sensing performance index, the first constraint being that the communication rate of the communication signal received by the communication user meets the needs of the communication user based on the communication rate performance index, the second constraint being that the total power of the new integrated sensing system meets the second constraint, and the third constraint being that the covariance of the signal transmitted by the integrated sensing base station meets the positive definiteness. The integrated sensing base station includes an integrated sensing transmitting base station and an integrated sensing receiving base station. During the solution process, the objective function is transformed into a non-convex function using the Schur complement theorem, and the first constraint is transformed into a non-convex constraint using a semi-definite relaxation technique.

[0246] It should be noted that other corresponding descriptions of the functional units involved in the multi-base station sensing integrated system transmission optimization device provided in this application embodiment can be found by referring to... Figure 1 The corresponding descriptions in the method will not be repeated here.

[0247] Based on the above, Figure 1 The method shown, and Figure 12 To achieve the above objectives, the present application also provides a computer device, specifically a personal computer, server, network device, etc., as shown in the virtual device embodiment. This computer device includes a storage medium and a processor; the storage medium stores a computer program; the processor executes the computer program to achieve the above-described objectives. Figure 1 The method for optimizing transmission in a multi-base station integrated sensing system is shown.

[0248] Optionally, the computer device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Bluetooth interfaces, Wi-Fi interfaces), etc.

[0249] Those skilled in the art will understand that the computer device structure provided in this embodiment does not constitute a limitation on the computer device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0250] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages and stores the hardware and software resources of a computer device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software within the physical device.

[0251] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented using software plus necessary general-purpose hardware platforms, or it can be implemented in hardware to construct a passive sensing system and cooperative sensing method for multi-ISAC base stations in near-field communication under imperfect channel conditions in complex communication environments in confined spaces. A semidefinite relaxation-based optimization resource allocation algorithm and a passive sensing multiple signal classification (MUSIC) algorithm are designed to maximize sensing performance while ensuring communication requirements. This semidefinite relaxation-based optimization resource algorithm transforms non-convex constraints in the optimization problem into convex constraints, and uses Schur's complement theorem to transform the non-convex objective function into a convex objective function. Simultaneously, a passive sensing multiple signal classification algorithm is proposed, achieving target localization based on a high-resolution estimation method using orthogonal subspaces.

[0252] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0253] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any modifications that can be made by those skilled in the art should fall within the protection scope of this application.

Claims

1. A transmission optimization method for a multi-base station integrated sensing system, characterized in that, The method includes: A novel integrated sensing system is constructed, comprising multiple integrated sensing transmitting base stations, one integrated sensing receiving base station, multiple communication users, and one sensing target. This system utilizes a communication channel to provide information transmission services to communication users and a sensing channel to perceive the location coordinates of at least one sensing target, including the communication users, in real time while they are using the information transmission service. The sensing target is determined based on multiple targets within a preset range around the communication users' environment. The integrated sensing receiving base station receives echoes. When the integrated sensing transmitting base station perceives the location coordinates of at least one sensing target, including the communication users, in real time while they are using the information transmission service, an orthogonal subspace improvement is applied to the passive sensing multiple signal classification algorithm. This allows the novel integrated sensing system to perceive the location coordinates of at least one sensing target, including the communication users, in real time while they are using the information transmission service, utilizing the sensing channel and the improved passive sensing multiple signal classification algorithm. The location coordinates of the sensing target perceived in real time using the improved passive sensing multiple signal classification algorithm are as follows: ； To obtain the location coordinates of the sensed target in real time using the improved passive sensing multi-signal classification algorithm, I represents the total number of integrated sensing base stations in the new integrated sensing system. When a communication user in a confined space uses information transmission services based on the novel integrated sensing system, for the novel integrated sensing system under confined space conditions, an imperfect near-field communication channel model for the communication channel and an imperfect near-field sensing channel model for the sensing channel are constructed in the novel integrated sensing system. Based on the constructed imperfect near-field communication channel model, a communication rate performance index is constructed to evaluate the information transmission performance of the communication channel, and a perception performance index is constructed to evaluate the perception capability of the perception channel based on the constructed imperfect near-field sensing channel model. The information transmission performance is characterized by the communication rate of the communication signal received by the communication user, and the perception capability is characterized by the change in position coordinates of the sensing target perceived by the novel integrated sensing system. The objective function is to minimize the change in the position coordinates of the perceived target by the novel integrated sensing system, as evaluated based on sensing performance indicators. The first constraint is that the communication rate of the communication signal received by the communication user meets the user's requirements, as evaluated based on communication rate performance indicators. The second constraint is that the total power of the novel integrated sensing system meets the requirements. The third constraint is that the covariance of the transmitted signal from the integrated sensing base station satisfies positive definiteness. The solution is to find the optimal configuration of each integrated sensing base station for the communication channel and sensing channel in the novel integrated sensing system. The integrated sensing base station includes an integrated sensing transmitting base station and an integrated sensing receiving base station. In the solution, the objective function is transformed using the Schur complement theorem for non-convex functions, and the first constraint is transformed using semi-definite relaxation techniques for non-convex constraints. The third constraint, satisfying the positive definiteness of the covariance of the transmitted signal from the integrated sensing transmitting base station, includes: Based on the third constraint formula, the covariance of the transmitted signal of the integrated sensing and communication base station satisfies positive definiteness, wherein the third constraint formula is: ； The covariance of the signal transmitted by the integrated sensing and communication base station i must satisfy positive definiteness, where I is the total number of integrated sensing and communication base stations in the new integrated sensing and communication system.

2. The method according to claim 1, characterized in that, The construction of the novel integrated sensing system includes an imperfect near-field communication channel model for the communication channel and an imperfect near-field sensing channel model for the sensing channel, comprising: Construct the near-field communication channel vector for the communication channel and the near-field sensing channel matrix for the sensing channel in the novel integrated sensing system. Based on the near-field communication channel vector, an imperfect near-field communication channel model is constructed for the communication channel; Based on the aforementioned near-field sensing channel matrix, an imperfect near-field sensing channel model is constructed for the sensing channel. The constructed imperfect near-field communication channel model is as follows: ， ， For the imperfect near-field communication channel model between the integrated inductive and sensory base station i and the communication user k, For integrated sensing and communication base station Communication users Near-field communication channel vectors between them The communication channel state information error follows a complex Gaussian distribution with mean 0 and variance 1. and Independent of each other, The error factor is in the range of 0 to 1. For integrated sensing and communication base station With communication users Inter-channel communication gain This indicates that when the coordinates of communication user k are Furthermore, the integrated sensing and communication base station The coordinates are At that time, the near-field array response vector between the integrated inductive transmitting base station i and the communication user k; The imperfect near-field sensing channel model constructed is as follows: ， ， For integrated sensing and communication base station Integrated receiving base station with sensing Imperfect near-field sensing channel model. For integrated sensing and communication base station Integrated Sensing Receiving Base Station The near-field sensing channel matrix between them The sensing channel state information error follows a complex Gaussian distribution with mean 0 and variance 1. The error factor is in the range of 0 to 1. For integrated sensing and communication base station From the sensing target s to the integrated sensing receiving base station Inter-channel communication gain For sensing target s and integrated sensing receiving base station The response vector between, For sensing target s and integrated sensing base station Near-field array response vector The transpose of .

3. The method according to claim 1, characterized in that, Based on the constructed imperfect near-field communication channel model, a communication rate performance index is constructed to evaluate the information transmission performance of the communication channel; and based on the constructed imperfect near-field sensing channel model, a sensing performance index is constructed to evaluate the sensing capability of the sensing channel, including: A communication signal representation model for communication users is constructed. Based on the communication signal representation model and the constructed imperfect near-field communication channel model, a communication rate performance index for evaluating the information transmission performance of the communication channel is constructed. The communication signal representation model for communication users is as follows: Let be the communication signal received by communication user k at time t. For communication user k, this represents the sum of communication signals received by each of the I integrated sensing base stations in the new sensing system. The communication signal transmitted by the integrated sensing and communication base station i at time t. For communication user k, the following parameters are given: mean 0 and variance ? Normally distributed additive white Gaussian noise, For the imperfect near-field communication channel model between the integrated inductive and sensory base station i and the communication user k, for transpose; The communication rate performance indicators are as follows: ， This is used to calculate the maximum communication rate that communication user k can achieve when using information transmission services based on a novel integrated sensing system. The sum of communication signal power received by communication user k from each of the I integrated sensing base stations in the new sensing system. The power of the communication signal transmitted from the integrated sensing and communication base station i to the communication user k. This represents the sum of interference signal power from other communication users j to communication user k. This refers to the interference generated during the transmission of sensing signals. To perceive the covariance matrix, The variance of the additive white Gaussian noise; Based on the constructed imperfect near-field sensing channel model, an echo signal representation model of the response matrix of the sensing target received by the integrated sensing and sensing base station is constructed. Based on the echo signal characterization model and the Cramer-Rao bound, which directly measures the limit of sensing performance, a sensing performance index is constructed to evaluate the sensing capability of the sensing channel. The Cramer-Rao bound is calculated using the inverse of the Fisher information matrix related to preset unknown parameters. The echo signal characterization model is as follows: For integrated sensing and sensing receiving base station The echo signal of the response matrix of the received target. In the novel integrated sensing system, the total number of communication signals transmitted by each of the I integrated sensing base stations is [number]. The communication signal transmitted by the integrated sensing and communication base station i at time t. For the integrated sensing and communication base station i, the mean is 0 and the variance is . Normally distributed additive white Gaussian noise, For integrated sensing and communication base station Integrated receiving base station with sensing An imperfect near-field sensing channel model; The constructed perception performance metrics are expressed as follows: ； ， ； For perception performance metrics, targeting location coordinates [ , The perceived target s, x-coordinate Perception performance indicators at the location. The vertical position coordinates Perception performance indicators at the location. To pre-set unknown parameters, the horizontal position coordinates of the perceived target s are used. , vertical position coordinates and the preset specific unknown parameters of the integrated sensing and communication base station i composition, From the real part and the virtual part constitute, This is the Fisher information matrix used to describe the sensing capability of the sensing channel. To represent the preset unknown parameters The Cramérox matrix CRB is the inverse of the Fisher information matrix, where C is a preset constant and I is the total number of integrated sensing base stations in the new integrated sensing system.

4. The method according to claim 1, characterized in that, The objective function, which is to minimize the change in the position coordinates of the perceived target by the novel integrated sensing system based on sensing performance indicators, includes: Based on the objective function formula, the Cramer-Rao bound of the novel integrated sensory system for the perceived target, as evaluated by sensing performance indicators, is minimized. The objective function formula is: ， The communication beam transmitted by the integrated sensing and communication base station i to the communication user k. The sensing beam of the integrated sensing and communication base station i The Cramero boundary is used for estimating the position coordinates.

5. The method according to claim 4, characterized in that, The first constraint, which is that the communication rate of the communication signal received by the communication user, as evaluated based on the communication rate performance index, meets the communication user's requirements, includes: Based on the first constraint formula, which is based on the communication rate performance index, the communication rate of the communication signal received by the communication user meets the communication user's requirements. The first constraint formula is as follows: ； This represents the maximum communication rate that user k can achieve when using information transmission services based on a novel integrated sensing system, as evaluated based on communication rate performance metrics. The preset minimum communication rate is the requirement of communication users, and K is the total number of communication users using the new integrated sensing system; Accordingly, during the solution process, the objective function is transformed using the Schur complement theorem for non-convex functions, and the first constraint condition is transformed using semi-definite relaxation techniques for non-convex constraints, including: In solving the problem, the Schur complement theorem is used to transform the objective function belonging to the non-convex function, resulting in the transformed objective function belonging to the convex function. Furthermore, the positive semidefinite relaxation technique is used to transform the first constraint condition belonging to the non-convex constraint, resulting in the transformed first constraint condition belonging to the convex constraint. The transformed objective function belonging to the convex function is as follows: ， ； The transformed first constraint condition belonging to convex constraints is: ； For t from 1 to 2 Summation, and As an auxiliary variable, Indicates the first A unit vector with one element being 1 and the rest being 0.

6. The method according to claim 1, characterized in that, The requirement that the total power of the novel integrated sensing system satisfies the second constraint includes: Based on the second constraint formula, the total power of the novel integrated sensing system is constrained, wherein the second constraint formula is: ； I represents the total number of integrated sensing base stations in the new integrated sensing system, and K represents the total number of communication users using the new integrated sensing system. The power of the communication signal transmitted from the integrated sensing and communication base station i to the communication user k. for The conjugate transpose of . The covariance matrix of the dedicated sensing signal. To represent the trace of the sum of all diagonal elements in the matrix, This is the preset maximum total power requirement.

7. A transmission optimization device for a multi-base station integrated sensing system, characterized in that, The device includes: A novel integrated sensing system construction module is used to construct a novel integrated sensing system comprising multiple integrated sensing transmitting base stations, one integrated sensing receiving base station, multiple communication users, and one sensing target. The novel integrated sensing system utilizes a communication channel to provide information transmission services to communication users and utilizes a sensing channel to perceive in real time the position coordinates of at least one sensing target, including the communication users, while the communication users are using the information transmission service. The sensing target is determined based on multiple targets within a preset range around the communication users' environment. The integrated sensing receiving base station is used to receive echoes. When the integrated sensing transmitting base station uses the sensing channel to perceive in real time the position coordinates of at least one sensing target, including the communication users, while the communication users are using the information transmission service, the passive sensing multi-signal classification algorithm is improved with orthogonal subspace. This allows the novel integrated sensing system to perceive in real time the position coordinates of at least one sensing target, including the communication users, while the communication users are using the information transmission service, using the sensing channel and the improved passive sensing multi-signal classification algorithm. The position coordinates of the sensing target perceived in real time using the improved passive sensing multi-signal classification algorithm are as follows: To obtain the location coordinates of the sensed target in real time using the improved passive sensing multi-signal classification algorithm, I represents the total number of integrated sensing base stations in the new integrated sensing system. The imperfect channel model construction module is used to construct, when a communication user in a confined space uses information transmission services based on the new integrated sensing system, an imperfect near-field communication channel model for the communication channel and an imperfect near-field sensing channel model for the sensing channel in the new integrated sensing system under confined space conditions. The performance index construction module is used to construct a communication rate performance index for evaluating the information transmission performance of the communication channel based on the constructed imperfect near-field communication channel model, and a perception performance index for evaluating the perception capability of the perception channel based on the constructed imperfect near-field sensing channel model. The information transmission performance is characterized by the communication rate of the communication signal received by the communication user, and the perception capability is characterized by the change in position coordinates of the sensing target perceived by the novel integrated sensing system. The optimal configuration solution module is used to solve for the optimal configuration of each integrated sensing base station in the new integrated sensing system for the communication channel and sensing channel, with the objective function being the minimum change in the position coordinates of the perceived target as evaluated based on sensing performance indicators, the first constraint being that the communication rate of the communication signal received by the communication user meets the user's needs as evaluated based on communication rate performance indicators, the second constraint being that the total power of the new integrated sensing system meets the second constraint, and the third constraint being that the covariance of the transmitted signal of the integrated sensing base station meets the positive definiteness. The module solves for the optimal configuration of each integrated sensing base station in the new integrated sensing system for the communication channel and sensing channel, where the integrated sensing base station includes an integrated sensing transmitting base station and an integrated sensing receiving base station. During the solution process, the objective function is transformed using the Schur complement theorem for non-convex functions, and the first constraint is transformed using a semi-definite relaxation technique for non-convex constraints. The third constraint, which requires the covariance of the transmitted signal of the integrated sensing base station to meet the positive definiteness, includes: Based on the third constraint formula, the covariance of the transmitted signal of the integrated sensing and communication base station satisfies positive definiteness, wherein the third constraint formula is: ； The covariance of the signal transmitted by the integrated sensing and communication base station i must satisfy positive definiteness, where I is the total number of integrated sensing and communication base stations in the new integrated sensing and communication system.

8. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for transmission optimization of the multi-base station integrated sensing system according to any one of claims 1 to 6.

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