An adaptive joint beamforming method for vehicle integrated perception and communication

Abstract

The present invention discloses an adaptive joint beamforming method for vehicle integrated perception and communication, comprising: establishing a physical signal model of all transceiver links by real-time perception of target signals and communication access point signals, in combination with the vehicle antenna array state; obtaining physical parameters of each link through the physical signal model of the transceiver link, and calculating performance indicators of the perception target and the communication access point respectively according to the physical parameters of each link; jointly optimizing the beamforming matrix and transmission power distribution of the vehicle antenna array and minimizing the total transmission power by combining the target requirements of perception and communication with the constraint that the performance indicators of each perception target and communication access point exceed a specified threshold value; and adaptively adjusting the vehicle antenna array transmission parameters according to the optimal vehicle antenna array beamforming matrix and transmission power distribution.

Description

Technical Field

[0001] The present invention relates to the field of beamforming technology, and in particular to an adaptive joint beamforming method for vehicle integrated perception and communication. Background Art

[0002] With the rapid development of sixth-generation mobile communications (6G) and vehicle-to-everything (V2X) technologies, intelligent vehicles are placing higher demands on the performance of their onboard wireless communication systems. Semi-autonomous and autonomous driving scenarios require not only high-speed, low-latency, and highly reliable communication links but also efficient perception of the road environment. Consequently, Integrated Sensing and Communication (ISAC) systems have emerged, achieving a deep integration of communication and perception functions through hardware and spectrum resource sharing. Beamforming, a core technology for improving signal-to-noise ratio and anti-interference capabilities, has been widely used in ISAC systems. However, existing technologies still face numerous technical bottlenecks and challenges in V2X / ISAC scenarios.

[0003] First, in highly dynamic vehicular environments, existing beamforming methods generally assume a static or quasi-static communication environment, making it difficult to track channel changes caused by high-speed movement, resulting in insufficient adaptability and robustness. Second, current beamforming methods mostly focus on the separate optimization of communication and perception, lacking systematic and coordinated optimization of the mutual interference and resource conflicts between the two. This makes the communication and perception beams prone to mainlobe and sidelobe leakage in terms of angle or spectrum, affecting the overall system performance. Furthermore, given limited resources (such as power, bandwidth, and number of antennas), over-optimization of one task often comes at the expense of the performance of another, making it difficult to achieve optimal system spectrum utilization and energy efficiency.

[0004] Furthermore, while new hardware technologies such as intelligent reflecting surfaces (RIS) have achieved some success in base stations and infrastructure, efficient deployment of RIS onboard vehicles remains difficult due to space and energy constraints. At the algorithmic level, traditional joint optimization problems are typically non-convex fractional forms, resulting in high computational complexity when directly solved, making it difficult to achieve the low latency and high reliability requirements required for on-device deployment. While recent attempts to improve beamforming efficiency have used AI methods such as reinforcement learning, their generalization and real-time adaptability in high-speed, high-interference scenarios still require improvement. Model inference overhead and resource consumption are high, making them unsuitable for real-time, lightweight deployment.

[0005] In view of the above problems in the prior art, the purpose of this invention is to design an adaptive joint beamforming method for vehicle integrated perception and communication. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to propose an adaptive joint beamforming method for vehicle integrated perception and communication, which can solve the above problems.

[0007] The present invention provides an adaptive joint beamforming method for vehicle integrated perception and communication, comprising:

[0008] By sensing the target signal and communication access point signal in real time and combining the vehicle antenna array status, a physical signal model of all transceiver links is established;

[0009] The physical parameters of each link are obtained through the physical signal model of the transceiver link, and the performance indicators of the sensing target and the communication access point are calculated based on the physical parameters of each link.

[0010] With the performance indicators of each sensing target and communication access point exceeding the specified threshold as the constraint, the beamforming matrix and transmit power allocation of the vehicle antenna array are jointly optimized to minimize the total transmit power, combining the target requirements of perception and communication.

[0011] The vehicle's antenna array transmission parameters are adaptively adjusted according to the optimal vehicle antenna array beamforming matrix and transmission power allocation.

[0012] Beneficial effects of the present invention:

[0013] First, it collects and integrates real-time signal information of targets and communication access points, combines the status of the vehicle antenna array, and establishes a physical signal model of the transmit and receive link to solve the problem of difficult to accurately model the signal coupling and dynamic changes of multiple targets and multiple nodes in complex vehicle environments. It can reflect the signal physical model of actual perception and communication scenarios with high precision, laying a solid foundation for subsequent beamforming and resource allocation optimization, and improving the accuracy and adaptability of system modeling.

[0014] Secondly, the physical signal transmission and reception model is used to accurately obtain the signal, interference and noise parameters of each link, and SINR is adopted as a common performance evaluation indicator for communication and perception. This solves the problems of incomparable perception and communication performance, inconsistent indicators, and difficult to quantify resource trade-offs. It can comprehensively balance perception and communication performance, support the effective allocation of system resources (such as power and beam direction) for actual scenarios, and provide quantitative and operational performance evaluation criteria for joint optimization.

[0015] Third, the efficiency indicators of perception and communication are unified into SINR constraints and converted into a convex second-order cone programming problem. The beamforming matrix and transmit power allocation are jointly optimized, which solves the problems of mutual interference between perception beams and communication beams, resource waste, and the difficulty of traditional optimization algorithms to achieve global optimization. It can reduce power leakage between beams, maximize the overall performance of the system, adaptively and robustly allocate system resources in dynamic and complex scenarios, and realize the coordinated guarantee of multi-service performance.

[0016] Fourth, the optimal beamforming matrix and power allocation scheme obtained by joint optimization are applied to the vehicle antenna array in real time, solving the problem that traditional vehicle antennas cannot dynamically adjust the transmission strategy according to the business and environment and have low space resource utilization. The system can automatically adjust the beam direction and intensity as the target / node distribution changes, significantly improving the performance stability and environmental adaptability of perception and communication, and ensuring the efficiency and robustness of the system in actual operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a scene diagram for implementing the method of this embodiment.

[0019] Figure 2 is the optimized beam pattern of this embodiment.

[0020] Figure 3 This is the optimized beam pattern of this embodiment that changes the angle between the sensing target and the communication access point.

[0021] Figure 4 4 is a normalized relationship curve diagram of the perceived signal-to-interference ratio threshold and channel capacity in this embodiment. DETAILED DESCRIPTION

[0022] To facilitate understanding by those skilled in the art, the structure of the present invention will now be further described in detail with reference to the embodiments and accompanying drawings. It should be understood that the steps mentioned in this embodiment, unless otherwise specified, can be adjusted in sequence according to actual needs, and can even be executed simultaneously or partially simultaneously.

[0023] like Figure 1 As shown, an embodiment of the present invention provides an adaptive joint beamforming method for vehicle integrated perception and communication, including:

[0024] S1 builds a physical signal model of all transceiver links by sensing target signals and communication access point signals in real time and combining the vehicle antenna array status;

[0025] In this step, a normal street environment is envisioned, assuming that the ISAC (Integrated Sensing and Communication) vehicle is equipped with transmit antennas and Specifically, the ISAC vehicle sends out multi-beam signals through its transmitting antenna. These signals are not only used for target perception, but also for connection with communication access points. The objects served by the vehicle include perception targets and communication access points. Figure 1 The layout of the simulation scene and the distribution of various components are shown.

[0026] Perception targets are objects that need to be sensed by the vehicle, including other vehicles on the street, pedestrians, and surrounding buildings. Perception targets can be device-less (such as pedestrians and buildings) or device-based (such as vehicles equipped with communication equipment). Communication access points are infrastructure devices required to receive high-quality communication services, such as base stations and roadside units (RSUs) installed on both sides of the street. These access points receive communication signals from ISAC vehicles through receiving antennas, ensuring reliable data transmission and network connectivity.

[0027] S101 in At each moment, the perception signal and communication signal of the ISAC vehicle are shaped by the perception beamforming matrix and the communication beamforming matrix respectively, and superimposed to form the total transmission signal. , the calculation formula is as follows:

[0028] ,

[0029] in, represents the baseband transmission signal, Represents the total number of perceived targets, Indicates the total number of communication access points, represents the matrix, This means that the matrix size is + OK 1 column, No. The sensory signals of the moment, Indicates the The communication signal at time , assuming that the signal has unit average energy, then , represents the transmit beamforming matrix, Indicates the number of transmitting antennas, and They are the sensing beamforming matrix and the communication beamforming matrix respectively, and the beamforming vector satisfies the transmit power constraint , Indicates the total transmit power.

[0030] In this step, the ISAC vehicle must simultaneously perform both perception and communication tasks. It must jointly transmit perception and communication signals to form a multi-beam, superimposable signal. Transmit power is constrained to ensure total energy is manageable, balancing communication and perception performance. The beamforming matrix distinguishes and flexibly allocates service objects (targets / users). The ISAC vehicle can simultaneously transmit radar and communication signals, corresponding to Q perception targets and K communication access points, respectively. The transmitted signal at the ISAC vehicle is defined as the sum of the corresponding beamformed communication and sensing signals. The perception and communication signals can be continuous waves, OFDM waves, or other similar technologies.

[0031] S102 passed The ISAC vehicle receives the The reflected echo of the perceived target baseband representation , build the perception target model, and the calculation formula is as follows:

[0032] ,

[0033] in, represents the array gain factor, Indicates the number of receiving antennas, Indicates the The moment The transmit power of each sensing target, Indicates the The reflection coefficient of the target is perceived at each moment, Indicates the The moment The Doppler frequency of the perceived target, Indicates the The moment The target receiving beamforming matrix is Indicates the The moment The transpose of the receiving beamforming matrix of the sensing target, Indicates the The moment The phase angle of the perceived target, Indicates the The vector of the receiving direction is sensed at each moment, Indicates the The vector of the emission direction is sensed at each moment, Indicates the The moment The delay of sensing the target, Indicates the The moment The sensing beamforming matrix of the sensing target, means the mean is zero and the variance is Gaussian white noise.

[0034] Specifically, the antenna array is a uniform linear array with a half-wavelength interval, and the calculation formulas for the vector for sensing the transmission direction and the vector for sensing the receiving direction are as follows:

[0035] ,

[0036] ,

[0037] in, Indicates the number of transmitting antennas, Indicates the number of receiving antennas, Indicates the phase angle of the current signal;

[0038] In this step, by physically modeling the actual target echo and considering physical quantities such as Doppler frequency shift, delay, and reflection coefficient, the real perception scene can be reflected, and the spatial diversity and beamforming capabilities of the array can be fully utilized to enhance the target detection capability.

[0039] S103 in Period, based on ISAC vehicles and Baseband communication signals transmitted between communication access points , respectively build uplink and downlink communication channel models, the calculation formula is as follows:

[0040] ,

[0041] in, represents the array gain factor, Indicates the Period The transmission power of each communication access point, Indicates the Period The communication channel coefficient of each communication access point, Indicates the Period The Doppler frequency of each communication access point, Indicates the Period The phase angle of each communication access point, Indicates the The vector of the communication receiving direction in each period, Indicates the The vector of the communication transmission direction in each period, Indicates the Period The delay of each communication access point, Indicates the Period The communication beamforming matrix of the communication access points, means the mean is zero and the variance is Gaussian white noise.

[0042] In this step, it is assumed that the Doppler frequency and time delay can be perfectly compensated by synchronization.

[0043] Specifically, the antenna array is a uniform linear array with half-wavelength spacing, and the calculation formulas for the vector in the communication transmission direction and the vector in the communication reception direction are as follows:

[0044] ,

[0045] ,

[0046] in, Indicates the number of transmitting antennas, Indicates the number of receiving antennas;

[0047] In this step, the physical communication channels between the vehicle and the access point / user are described for both uplink and downlink scenarios. Elements such as Doppler, channel fading, and directional vectors are also introduced to ensure that the model is consistent with the actual scenario, accurately modeling the communication channel and achieving spatial decoupling and multi-user interference suppression.

[0048] S2 obtains the physical parameters of each link through the physical signal model of the transceiver link, and calculates the performance indicators of the sensing target and communication access point respectively according to the physical parameters of each link;

[0049] In complex environments (such as dense wireless communication networks or radar scenarios with severe interference), optimizing the resource allocation of the ISAC system helps improve stability and reliability. The signal-to-interference ratio (SINR), as a common performance indicator for communication and perception, comprehensively considers signal strength, interference strength, and noise strength, and can help the system balance the power allocation of beamforming between communication and perception. By analyzing SINR, we can ensure the effectiveness of perception tasks while meeting communication needs, achieving coordinated optimization of the two. (Q+K) Allocate the total system transmission power among the objects To maximize signal quality and system performance, the SINR calculation formula is:

[0050] ,

[0051] Where P is the signal power, I is the interference power, and N is the noise power.

[0052] Therefore, the signal-to-interference ratio of the perceptual target model and the signal-to-interference ratio of the communication channel model The calculation process is as follows:

[0053] S201 calculates the signal-to-interference ratio of the perceived target through the perceived target model and beamforming matrix , the calculation formula is as follows:

[0054] ,

[0055] in, Indicates the The echo power of the perceived target, Indicates the The reflection coefficient of the perceived target, Indicates the The transpose of the receiving beamforming matrix of the sensing target, Indicates the The phase angle of the perceived target, Indicates the A vector of the receiving direction of the perceived target, Indicates the The vector transpose of the target emission direction, Indicates the The sensing beamforming matrix of the sensing target, Indicates the The communication beamforming matrix of the communication access points, Indicates the perception signal, Represents communication signals, is the Gaussian white noise variance of the perception target model;

[0056] Further, let , represents the sensing channel matrix, and assumes , the signal-to-interference ratio of the perceived target The calculation formula is as follows:

[0057] ,

[0058] in, Indicates the echo power of all perceived targets;

[0059] In this step, for the perception target, the communication signal acts as an interference signal, which accurately models the real engineering constraints of physical signals leaking and interfering with each other in resource reuse scenarios. It is also the theoretical basis for achieving joint optimization and improving system collaborative efficiency.

[0060] S202 calculates the signal-to-interference ratio of the communication access point using the communication channel model and the beamforming matrix , the calculation formula is as follows:

[0061] ,

[0062] in, represents the communication power of k communication access points Indicates the The communication channel coefficient of each communication access point, Indicates the The phase angle of each communication access point, A vector representing the direction of communication reception, A vector representing the direction of communication transmission, Indicates the The sensing beamforming matrix of the sensing target, Indicates the The communication beamforming matrix of the communication access points, Indicates the perception signal, Represents communication signals, is the Gaussian white noise variance of the communication channel model;

[0063] Further, let , Denotes the communication channel matrix, and assumes , the signal-to-interference ratio of the communication access point The calculation formula is as follows:

[0064] ,

[0065] in, Indicates the communication power of all communication access points.

[0066] In this step, for the communication signal, the signal of the sensing target is regarded as an interference signal.

[0067] S3 takes the performance indicators of each sensing target and communication access point exceeding the specified threshold as a constraint, combines the target requirements of sensing and communication, and jointly optimizes the beamforming matrix and transmit power allocation of the vehicle antenna array to minimize the total transmit power;

[0068] In this step, interference with both perception and communication in different beams arises from power leakage from one beam to another, i.e., overlapping main or side beams. This power leakage can cause mutual interference between beams, especially in areas of overlapping main or side beams. This not only reduces perception accuracy but also compromises communication quality. Obviously, to maximize the overall performance of the ISAC system, it's not enough to optimize only the perception or communication beams individually. Instead, both should be modeled jointly, and their respective beamforming matrices should be jointly optimized to minimize mutual interference and meet their respective mission requirements.

[0069] Maximizing a system objective (such as total signal-to-interference ratio / system throughput / minimized transmit power) requires that the SINR of each sensing target and each communication access point exceeds the corresponding SINR threshold, and the sum of the sensing beamforming matrix and the communication beamforming matrix does not exceed the total transmit power. Therefore, the calculation formula is as follows:

[0070]

[0071] in, represents the SINR threshold of the perceived target, represents the SINR threshold of the communication access point, Indicates the The sensing beamforming matrix of the sensing target, Indicates the The communication beamforming matrix of the communication access points, Indicates the total transmit power;

[0072] Furthermore, since the optimization variables in the SINR constraint are in fractional form, the problem in the above formula is non-convex and there are multiple local optimal solutions. and Convert it into a second-order cone constraint and convert it into a convex problem for optimization and solution. The calculation formula is as follows:

[0073] ,

[0074] ,

[0075] ,

[0076] .

[0077] In this step, After expansion and rewriting, taking the square root of both sides simultaneously converts the SINR constraints of the sensing target into a second-order cone. The SINR of the communication access point is converted similarly. Once the SINR constraints of the sensing target and the communication access point are converted into second-order cone constraints, a convex solution can be used to find the unique optimal solution, thus determining the optimal allocation scheme. The optimal allocation scheme achieves the optimal distribution of power and interference.

[0078] S4 adaptively adjusts the vehicle's antenna array transmission parameters according to the optimal vehicle antenna array beamforming matrix and transmission power allocation.

[0079] like Figure 2 Figure 2 shows the beam pattern resulting from the adaptive joint beamforming optimization. As can be seen from the results, the main lobes of the sensing and communication beams precisely point in the direction of the sensing target and communication access point, respectively. This demonstrates that the joint beamforming optimization method effectively coordinates and manages resource allocation between different beams, ensuring efficient execution of sensing and communication functions. The sensing beam forms a null point in the direction of the communication access point to eliminate interference with the communication signal. Similarly, the communication beam forms a null point in the direction of the sensing target, minimizing interference with the sensing signal.

[0080] This adaptive joint beamforming optimization significantly reduces the interference between the sensing and communication beams by optimizing the beamforming matrix, ensuring that the performance of the two does not affect each other. Figure 2 and Figure 3 A comparison shows that adaptive joint beamforming optimization maintains excellent performance even when the angle between the sensing target and the communication access point changes. This demonstrates the robustness and adaptability of this optimization method, enabling dynamic adjustments to beam direction and shape to address real-time changes in environmental conditions and service requirements. Both sensing and communication beams can rapidly respond to changes in target position, ensuring system stability and reliability in a variety of complex scenarios.

[0081] In order to fully understand and optimize the performance of perception and communication systems, it is very necessary to use communication channel capacity as a research indicator. This is because channel capacity directly affects the effectiveness and efficiency of the system and is a key parameter for measuring data transmission rate and system performance. According to Shannon's formula:

[0082] ,

[0083] in, The bandwidth occupied by the communication beam. In the present invention, no allocation of bandwidth resources is performed. According to the formula, if SINR increases, the system capacity will also increase. In order to look at it more dialectically, Impact on channel capacity, the standard channel capacity value is selected Perform performance analysis of channel capacity, is the mean channel capacity, is the standard deviation of the channel capacity.

[0084] Figure 4 The figure shows the normalized relationship curve between the perceived signal-to-interference ratio threshold (SINR Threshold) and the channel capacity (ChannelCapacity). The two curves in the figure represent the situation under different communication threshold conditions: 0dB and 10dB. It can be seen that when the communication threshold remains unchanged, as the perception threshold increases, the As increases, the capacity of the communication channel becomes larger, indicating that more resources are allocated to communication. At lower values, the slopes of both curves are steeper, indicating that channel capacity is more sensitive to changes in the perceived SINR threshold. This suggests that, under poor channel conditions, appropriately raising the SINR threshold can significantly improve channel capacity. When the perceived SINR threshold increases further (e.g., exceeding 10dB), the growth rate of channel capacity begins to slow and stabilize. This indicates that, under good channel conditions, further raising the SINR threshold has limited effect on improving channel capacity.

[0085] Adaptive beamforming adjusts beam direction and strength based on actual signal quality and demand, thereby enhancing signal reception quality. By adaptively selecting communication thresholds and SINR thresholds, adaptive beamforming maximizes channel capacity and data transmission rates, thereby improving system efficiency. During peak communication demand periods, beamforming can be optimized to increase channel capacity; during periods of lower communication demand, energy consumption can be reduced, improving system energy efficiency. This provides critical support for the efficient operation of ISAC systems in practical applications.

[0086] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0090] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several distinct components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third etc. does not indicate any order. These words may be interpreted as names.

[0091] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0092] Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations fall within the scope of the claims and their equivalents, the present invention is intended to include such modifications and variations.

[0093] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0094] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

Claims

1. An adaptive joint beamforming method for vehicle integrated perception and communication, characterized in that: include: By sensing the target signal and communication access point signal in real time and combining the vehicle antenna array status, a physical signal model of all transceiver links is established; The physical parameters of each link are obtained through the physical signal model of the transceiver link. The performance indicators of the sensing target and the communication access point are calculated based on the physical parameters of each link. Specifically: Calculate the signal-to-interference ratio of the perceived target through the perceived target model and beamforming matrix , the calculation formula is as follows: ， in, represents the array gain factor, Indicates the The echo power of the perceived target, Indicates the The reflection coefficient of the perceived target, Indicates the The transpose of the receiving beamforming matrix of the sensing target, Indicates the The phase angle of the perceived target, Indicates the A vector of the receiving direction of the perceived target, Indicates the The vector transpose of the target emission direction, Indicates the The sensing beamforming matrix of the sensing target, Indicates the The communication beamforming matrix of the communication access points, Indicates the perception signal, Represents communication signals, is the Gaussian white noise variance of the perception target model; Further, let , represents the sensing channel matrix, and assumes , the signal-to-interference ratio of the perceived target The calculation formula is as follows: ， in, Indicates the echo power of all perceived targets; Calculate the signal-to-interference ratio of the communication access point through the communication channel model and beamforming matrix , the calculation formula is as follows: ， in, express The communication power of each communication access point, Indicates the The communication channel coefficient of each communication access point, Indicates the The phase angle of each communication access point, A vector representing the direction of communication reception, A vector representing the direction of communication transmission, Indicates the The sensing beamforming matrix of the sensing target, Indicates the The communication beamforming matrix of the communication access points, Indicates the perception signal, Represents communication signals, is the Gaussian white noise variance of the communication channel model; Further, let , Denotes the communication channel matrix, and assumes , the signal-to-interference ratio of the communication access point The calculation formula is as follows: ， in, Indicates the communication power of all communication access points; With the performance indicators of each sensing target and communication access point exceeding the specified threshold as the constraint, the beamforming matrix and transmit power allocation of the vehicle antenna array are jointly optimized to minimize the total transmit power, combining the target requirements of perception and communication. The vehicle's antenna array transmission parameters are adaptively adjusted according to the optimal vehicle antenna array beamforming matrix and transmission power allocation.

2. The adaptive joint beamforming method for vehicle integrated perception and communication according to claim 1, characterized in that: The physical signal model of all transceiver links is established by real-time sensing of target signals and communication access point signals in combination with the vehicle antenna array status, including: In the At each moment, the perception signal and communication signal of the ISAC vehicle are shaped by the perception beamforming matrix and the communication beamforming matrix respectively, and superimposed to form the total transmission signal. ; Through the The ISAC vehicle receives the The reflected echo of the perceived target baseband representation , build a perception target model; In the Period, based on ISAC vehicles and Baseband communication signals transmitted between communication access points , construct the uplink and downlink communication channel models respectively.

3. The adaptive joint beamforming method for vehicle integrated perception and communication according to claim 2, characterized in that: The total transmitted signal The calculation formula is as follows: ， in, represents the baseband transmission signal, Represents the total number of perceived targets, Indicates the total number of communication access points, represents the matrix, This means that the matrix size is + OK 1 column, Indicates the The sensory signals of the moment, Indicates the The communication signal at time , assuming that the signal has unit average energy, then , represents the transmit beamforming matrix, Indicates the number of transmitting antennas, and They are the sensing beamforming matrix and the communication beamforming matrix respectively, and the beamforming vector satisfies the transmit power constraint , Indicates the total transmit power.

4. The adaptive joint beamforming method for vehicle integrated perception and communication according to claim 2, characterized in that: The reflected echo The calculation formula is as follows: , in, represents the array gain factor, Indicates the number of receiving antennas, Indicates the The moment The transmit power of each sensing target, Indicates the The moment The reflection coefficient of the perceived target, Indicates the The moment The Doppler frequency of the perceived target, Indicates the The moment The target receiving beamforming matrix is Indicates the The moment The transpose of the receiving beamforming matrix of the sensing target, Indicates the The moment The phase angle of the perceived target, Indicates the The moment A vector of the perceived receiving direction, Indicates the The moment A vector that senses the emission direction, Indicates the The moment The delay of sensing the target, Indicates the The moment The sensing beamforming matrix of the sensing target, means the mean is zero and the variance is Gaussian white noise.

5. The adaptive joint beamforming method for vehicle integrated perception and communication according to claim 4, characterized in that: The calculation formulas for the vector sensing the transmission direction and the vector sensing the receiving direction are as follows: ， ， in, Indicates the number of transmitting antennas, Indicates the number of receiving antennas, Indicates the phase angle of the current signal.

6. The adaptive joint beamforming method for vehicle integrated perception and communication according to claim 2, characterized in that: The communication signal The calculation formula is as follows: , in, represents the array gain factor, Indicates the Period The transmission power of each communication access point, Indicates the Period The communication channel coefficient of each communication access point, Indicates the Period The Doppler frequency of each communication access point, Indicates the Period The phase angle of each communication access point, Indicates the The vector of the communication receiving direction in each period, Indicates the The vector of the communication transmission direction in each period, Indicates the Period The delay of each communication access point, Indicates the Period The communication beamforming matrix of the communication access points, means the mean is zero and the variance is Gaussian white noise.

7. The adaptive joint beamforming method for vehicle integrated perception and communication according to claim 6, characterized in that: The calculation formulas for the vector in the communication transmission direction and the vector in the communication reception direction are as follows: ， ， in, Indicates the number of transmitting antennas, Indicates the number of receiving antennas.

8. The adaptive joint beamforming method for vehicle integrated perception and communication according to claim 1, characterized in that: The method of optimizing the beamforming matrix and transmit power allocation of the vehicle antenna array to minimize the total transmit power by taking the performance indicators of each sensing target and communication access point exceeding a specified threshold as a constraint and combining the target requirements of sensing and communication includes: The SINR of each sensing target and each communication access point exceeds the corresponding SINR threshold, and the sum of the sensing beamforming matrix and the communication beamforming matrix does not exceed the total transmit power. Therefore, the calculation formula is as follows: in, represents the SINR threshold of the perceived target, represents the SINR threshold of the communication access point, Indicates the The sensing beamforming matrix of the sensing target, Indicates the The communication beamforming matrix of the communication access points, Indicates the total transmit power.

9. The adaptive joint beamforming method for vehicle integrated perception and communication according to claim 8, characterized in that: The step of making the SINR of each sensing target and each communication access point exceed the corresponding SINR threshold, and the sum of the sensing beamforming matrix and the communication beamforming matrix does not exceed the total transmit power further includes: Will and Convert it into a second-order cone constraint and convert it into a convex problem for optimization and solution. The calculation formula is as follows: ， ， ， 。

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