An intelligent reflecting surface assisted vehicle networking communication and perception integrated method based on alternating optimization

Abstract

The present application relates to a kind of intelligent reflecting surface assisted car networking communication perception integration method based on alternative optimization, belong to car networking field.The present application is with the maximum communication vehicle signal-to-noise ratio as target, while meeting the minimum signal-to-noise ratio of perception vehicle, solve the active beamforming and passive beamforming joint design problem in simultaneously transmitting and reflecting reconfigurable intelligent surface assisted car networking communication perception integration system.Through the adoption of alternative optimization to decouple variable and convert it into two easy-to-handle subproblems, i.e., transmitting and transmitting and reflecting beamforming subproblem.Specifically, transmitting beamforming problem uses semi-positive definite relaxation problem.Transmitting and reflecting beamforming problem uses optimization minimization method.Alternative optimization two subproblems obtain optimal solution.Considering the Doppler shift and multiple-input multiple-output in car networking scene, joint beamforming optimization is carried out to communication and target perception, and system performance is improved.

Description

Technical Field

[0001] This invention belongs to the field of vehicle networking and relates to a method for integrating vehicle networking communication and perception based on alternating optimization intelligent reflective surface. Background Technology

[0002] Communication-sensing integration, enabling efficient spectrum and hardware sharing between radar sensing and communication, has become a promising technology that can significantly improve spectrum and energy efficiency. Compared to traditional independent radar and communication system designs, communication-sensing integration not only offers advantages such as lower cost and less interference, but also achieves synergistic gains between radar and communication functions. Specifically, sensing capabilities can enhance the performance of the communication system, while communication functions can strengthen the sensing effect. Therefore, communication-sensing integration has become a current research hotspot, attracting widespread attention and interest.

[0003] Reconfigurable smart surfaces are another promising technology that can reshape wireless channels for efficient information transmission and establish virtual line-of-sight links for sensing. Unlike traditional wireless networks with uncontrollable environments, reconfigurable smart surfaces offer a novel communication paradigm, flexibly adjusting the phase shift of the incident signal to create favorable propagation conditions. However, due to their limited physical implementation, a key issue is that the transmitter and receiver must be located on the same side of the reconfigurable smart surface, leading to incomplete wireless coverage in space. Compared to existing reflective reconfigurable smart surfaces, reconfigurable smart surfaces that simultaneously transmit and reflect are recently developed to enable a fully spatial smart radio environment where base stations and vehicles can be located on either side of the reconfigurable smart surface.

[0004] The realization of future autonomous vehicles requires robust connectivity and high quality of service (i.e., vehicle-to-vehicle and vehicle-to-infrastructure) between vehicles and infrastructure (i.e., vehicle-to-infrastructure). The aforementioned advantages of simultaneously transmissive and reflective reconfigurable smart surface technology make vehicle communication assisted by simultaneously transmissive and reflective smart surfaces an attractive option for enhancing vehicle network connectivity and improving autonomous driving performance. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide an integrated method for vehicle-to-everything (V2X) communication and perception based on alternating optimization of intelligent reflective surfaces.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A vehicle communication and perception integrated optimization method supporting reconfigurable smart surfaces that simultaneously transmit and reflect light, the method comprising the following steps:

[0008] S1. Based on the vehicle-to-everything (V2X) integrated sensing system, construct a first channel model between the integrated communication and sensing device, a reconfigurable smart surface that transmits and reflects simultaneously, and a second channel model between the integrated communication and sensing device and the reconfigurable smart surface that transmits and reflects simultaneously and a sensing vehicle.

[0009] S2. Obtain the signal-to-noise ratio of the communication signal and the signal-to-noise ratio of the sensing signal of the vehicle-to-everything (V2X) communication and sensing integrated system based on the first channel model and the second channel model;

[0010] S3. Determine the communication signal-to-noise ratio and the sensing signal-to-noise ratio based on the communication signal and the sensing signal, with the goal of maximizing the communication vehicle signal-to-noise ratio while satisfying the minimum signal-to-noise ratio of the sensing vehicle;

[0011] S4. Alternating optimization is used to decouple the variables and transform them into two easily tractable subproblems: the transmit beamforming subproblem and the transmission and reflection beamforming subproblem. Specifically, the transmit beamforming problem uses a positive semidefinite relaxation problem. The transmission and reflection beamforming problem uses an optimization minimization method. Alternating optimization of the two subproblems yields the optimal solution.

[0012] Furthermore, in S1, a first channel model between the integrated communication and sensing device, the simultaneously transmissive and reflective reconfigurable smart surface, and the communication vehicle are constructed, along with a second channel model between the integrated communication and sensing device, the simultaneously transmissive and reflective reconfigurable smart surface, and the sensing vehicle.

[0013] The first channel model is: the signal received by the communication vehicle;

[0014] The received signal from the communication vehicle consists of two parts. The first part is the product of the base station beamforming matrix and the base station-simultaneous transmission and reflection reconfigurable smart surface-communication vehicle channel and the base station-communication vehicle channel. The second part is additive white Gaussian noise.

[0015] The second channel model is: the received signal of the sensing vehicle;

[0016] The received signal from the sensing vehicle consists of two parts. The first part is the product of the base station beamforming matrix and the base station-simultaneous transmission and reflection reconfigurable smart surface-sensing vehicle channel and the base station-sensing vehicle channel. The second part is additive white Gaussian noise.

[0017] Furthermore, in S2, the first channel model and the second channel model acquire the transmitted signals and sensed signals of the vehicle-to-everything (V2X) sensing integrated system:

[0018] The signal-to-noise ratio (SNR) of a communication vehicle is defined as the ratio of the channel gain of the communication vehicle to the channel gains of other communication vehicles, the channel gain of the sensing vehicle, and the noise.

[0019] The radar signal-to-noise ratio of a sensing vehicle is defined as the ratio of the channel gain of the sensing vehicle to the channel gain and noise of the communication vehicle.

[0020] Furthermore, in S3, the communication signal-to-noise ratio and the sensing signal-to-noise ratio are determined based on the communication signal and the sensing signal, with the goal of maximizing the communication vehicle signal-to-noise ratio while simultaneously satisfying the minimum signal-to-noise ratio of the sensing vehicle.

[0021] Define the maximum communication signal-to-noise ratio and the communication vehicle beamforming matrix relative to the base station, the sensing vehicle beamforming matrix at the base station, and the transmission beamforming and reflection beamforming at the simultaneously transmitted and reflected reconfigurable smart surface.

[0022] Furthermore, in S4, alternating optimization is used to decouple the variables and transform them into two easily tractable subproblems: the transmit and transmit / reflect beamforming subproblems. Specifically, the transmit beamforming problem uses a positive semidefinite relaxation method. The transmit / reflect beamforming problem uses an optimization minimization method. Alternating optimization of the two subproblems yields the optimal solution.

[0023] To address this issue, maximizing the signal-to-noise ratio (SNR) of the communication vehicle is decoupled into two sub-problems: transmit and transmission / reflection beamforming. Specifically, in transmit beamforming, the beamforming matrices of the communication vehicle and the sensing vehicle are jointly optimized to maximize the SNR of the communication vehicle, while keeping the transmission and reflection beamforming matrices fixed. Then, while keeping the communication and sensing vehicle beamforming matrices constant, the transmission and reflection beamforming matrices are optimized.

[0024] For the transmit beamforming problem, a positive semidefinite relaxation method is used to transform the problem into a convex quadratic positive semidefinite programming problem, which is then solved using a convex optimization package. For the transmit and reflect beamforming problem, the objective problem is first rearranged into a quadratic problem, then an optimization minimization method is used to find an ideal substitute function, and a positive semidefinite method is used to transform the problem into a quadratic optimization problem. Finally, the SROCR algorithm is used to obtain the optimal solution.

[0025] The beneficial effects of this invention are as follows:

[0026] First, this invention enables simultaneous communication and target perception at a base station within a simultaneously transmissive and reflective reconfigurable smart surface system. By designing the beamforming matrix for communication vehicles at the base station, the beamforming matrix for sensing vehicles at the base station, and the transmission and reflection beamforming at the simultaneously transmissive and reflective reconfigurable smart surface, the signal-to-noise ratio of all communication vehicles is optimized, thereby improving the overall performance of the system.

[0027] Second, this invention takes into account Doppler frequency shift and MIMO technology, making it more suitable for vehicle networking scenarios and enhancing the system's adaptability and reliability.

[0028] Third, this invention proposes alternating optimization to decouple variables and transform them into two easily manageable subproblems. The two subproblems are solved using a positive semidefinite relaxation algorithm and an optimization minimization method, respectively, to obtain better results.

[0029] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0030] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0031] Figure 1 This is a flowchart of the alternating optimization algorithm of the present invention;

[0032] Figure 2 The method flowchart of the present invention. Detailed Implementation

[0033] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0034] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0035] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0036] Please see Figures 1-2 This is a joint beamforming method for assisted vehicles with integrated sensing based on graph neural networks that allows for simultaneous transmission and reflection of reconfigurable smart surfaces.

[0037] Figure 1 This is a schematic diagram of the alternating optimization method of the present invention; as shown. Figure 2 As shown, the objective function is decomposed into two sub-problems: transmit beamforming and transmission / reflection beamforming. Transmit beamforming is solved using a positive semidefinite relaxation algorithm, while transmission / reflection beamforming is solved using an optimization minimization method. The two problems are optimized alternately until the objective function is maximized, at which point the algorithm terminates.

[0038] The present invention provides a graph neural network-based method for simultaneous transmission and reflection reconfigurable smart surfaces to assist vehicle sensing integration and joint beamforming, comprising the following steps:

[0039] S1, Initialization

[0040] Based on a vehicle-to-everything (V2X) integrated sensing system, a first channel model and a second channel model are constructed between the integrated communication and sensing device, the simultaneously transmissive and reflective reconfigurable smart surface, and the sensing vehicle. Since the direct channel, i.e., the base station-communication vehicle link, is severely blocked by obstacles, it is ignored; only the auxiliary channel of the simultaneously transmissive and reflective reconfigurable smart surface, i.e., the base station-simultaneously transmissive and reflective reconfigurable smart surface-communication vehicle link, is considered. The transmission frame duration is T, divided into U subframes, with each subframe duration denoted as T / U. The vehicle's position is unlikely to change significantly within a subframe; therefore, it is assumed that the vehicle's position is constant and fixed within a subframe, but changes from one subframe to another. Therefore, the first channel model is: the received signal of the communication vehicle; where the received signal of the communication vehicle consists of two parts. The first part is the product of the base station beamforming matrix and the base station-simultaneously transmissive and reflective reconfigurable smart surface-communication vehicle channel and the base station-vehicle channel; the second part is additive white Gaussian noise. The second channel model is: the received signal of the sensing vehicle; where the received signal of the sensing vehicle is a sum of two parts. The first part is the product of the base station beamforming matrix and the base station-simultaneous transmission and reflection reconfigurable smart surface-sensing vehicle channel and the base station-sensing vehicle channel. The second part is additive white Gaussian noise.

[0041] S2. Calculate the communication signal-to-noise ratio and the perceived signal-to-noise ratio.

[0042] The signal-to-noise ratio (SNR) of the communicating vehicle and the sensing vehicle are obtained based on the first and second channel models, respectively. The SNR of the communicating vehicle is defined as the ratio of its channel gain to the channel gains of other communicating vehicles, the channel gain of the sensing vehicle, and its noise level. The radar SNR of the sensing vehicle is defined as the ratio of its channel gain to the channel gain and noise level of the communicating vehicle.

[0043] S3. Construct optimization objectives

[0044] The communication signal-to-noise ratio (SNR) and sensing signal-to-noise ratio (SNR) are determined based on the communication signal and sensing signal, with the goal of maximizing the SNR of the communication vehicle while simultaneously satisfying the minimum SNR of the sensing vehicle, the unit modulus of the components, and energy constraints.

[0045] S4. Basic Process of Constructing Alternating Optimization Methods

[0046] Alternating optimization is employed to decouple the variables and transform transmission and reflection into two easily tractable subproblems: the transmission beamforming subproblem and the transmission and reflection beamforming subproblem. Specifically, the transmission beamforming problem uses a positive semidefinite relaxation method, while the transmission and reflection beamforming problem uses an optimization minimization method. Alternating optimization of the two subproblems yields the optimal solution.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for integrated vehicle-to-everything (V2X) communication and perception based on alternating optimization intelligent reflective surfaces, characterized in that: The method includes the following steps: S1. Based on the vehicle-to-everything (V2X) integrated sensing system, construct a first channel model between the integrated communication and sensing device, a reconfigurable smart surface that transmits and reflects simultaneously, and a second channel model between the integrated communication and sensing device and the reconfigurable smart surface that transmits and reflects simultaneously and a sensing vehicle. S2. Obtain the signal-to-noise ratio of the communication signal and the signal-to-noise ratio of the sensing signal of the vehicle-to-everything (V2X) communication and sensing integrated system based on the first channel model and the second channel model; S3. Determine the communication signal-to-noise ratio and the sensing signal-to-noise ratio based on the communication signal and the sensing signal, with the goal of maximizing the communication vehicle signal-to-noise ratio while satisfying the minimum signal-to-noise ratio of the sensing vehicle; S4. Alternating optimization is used to decouple the variables and transform them into two easily tractable subproblems: the transmit and transmit / reflect beamforming subproblems. Specifically, the transmit beamforming problem uses a positive semidefinite relaxation problem; the transmit / reflect beamforming problem uses an optimization minimization method; the optimal solution is obtained by alternately optimizing the two subproblems.

2. The method for integrated vehicle-to-everything (V2X) communication and perception based on alternating optimization intelligent reflective surface as described in claim 1, characterized in that: In step S1, a first channel model between the integrated sensing device, the simultaneously transmissive and reflective reconfigurable smart surface, and the communication vehicle are constructed, along with a second channel model between the integrated sensing device, the simultaneously transmissive and reflective reconfigurable smart surface, and the sensing vehicle. The first channel model is: the signal received by the communication vehicle; The received signal of the communication vehicle consists of two parts: the first part is the product of the base station beamforming matrix and the base station-simultaneous transmission and reflection reconfigurable smart surface-communication vehicle channel and the base station-communication vehicle channel; the second part is additive white Gaussian noise. The second channel model is: the received signal of the sensing vehicle; The received signal of the sensing vehicle consists of two parts: the first part is the product of the base station beamforming matrix and the base station-simultaneous transmission and reflection reconfigurable smart surface-sensing vehicle channel and the base station-sensing vehicle channel; the second part is additive white Gaussian noise.

3. The method for integrated vehicle-to-everything (V2X) communication and perception based on alternating optimization intelligent reflective surface as described in claim 2, characterized in that: In S2, the first channel model and the second channel model acquire the transmission signal and sensing signal of the vehicle-to-everything (V2X) sensing integrated system. Define communication vehicle The signal-to-noise ratio is: communication vehicle The ratio of the channel gain of the vehicle to the channel gain of other communication vehicles, the channel gain of sensing vehicles, and noise. The radar signal-to-noise ratio of a sensing vehicle is defined as the ratio of the channel gain of the sensing vehicle to the channel gain and noise of the communication vehicle.

4. The integrated method for intelligent reflective surface-assisted vehicle-to-everything (V2X) communication and perception based on alternating optimization as described in claim 3, characterized in that: In step S3, the communication signal-to-noise ratio (SNR) and the sensing SNR are determined based on the communication signal and the sensing signal, with the goal of maximizing the communication vehicle SNR while simultaneously satisfying the minimum SNR of the sensing vehicle. Define the maximum communication signal-to-noise ratio and the communication vehicle beamforming matrix relative to the base station, the sensing vehicle beamforming matrix at the base station, and the transmission beamforming and reflection beamforming at the simultaneously transmitted and reflected reconfigurable smart surface.

5. The integrated method for vehicle-to-everything (V2X) communication and perception based on alternating optimization intelligent reflective surface as described in claim 4, characterized in that: In S4, alternating optimization is used to decouple the variables and transform them into two easily tractable subproblems, namely, the emission and transmission / reflection beamforming subproblems, specifically: Maximizing the signal-to-noise ratio (SNR) of the communication vehicle is decoupled into two sub-problems: transmit and transmission / reflection beamforming. Specifically, in transmit beamforming, the beamforming matrices of the communication vehicle and the sensing vehicle are jointly optimized to maximize the SNR of the communication vehicle, while keeping the transmission and reflection beamforming matrices fixed. Then, while keeping the communication vehicle and sensing vehicle beamforming matrices constant, the transmission and reflection beamforming matrices are optimized. For the transmit beamforming problem, a positive semidefinite relaxation method is used to transform the problem into a convex quadratic positive semidefinite programming problem, which is then solved using a convex optimization package. For the transmit and reflect beamforming problem, the objective problem is first rearranged into a quadratic problem, then a substitution function is obtained using an optimization minimization method, the problem is transformed into a quadratic optimization problem using a positive semidefinite method, and finally the optimal solution is obtained using the SROCR algorithm.

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