Intelligent reflecting surface auxiliary Internet of Vehicles communication perception integration method based on alternate optimization

Abstract

The invention relates to an intelligent reflecting surface assisted Internet of Vehicles communication perception integration method based on alternate optimization, and belongs to the field of Internet of Vehicles. According to the method, maximization of the signal-to-noise ratio of a communication vehicle is taken as a target, the minimum signal-to-noise ratio of a sensing vehicle is met, and the problem of joint design of active beam forming and passive beam forming in a simultaneous transmission and reflection reconfigurable intelligent surface assisted Internet of Vehicles communication and sensing integrated system is solved. The variables are decoupled and converted into two sub-problems that are easy to handle, i.e., emission and transmission and reflection beamforming sub-problems, by employing alternating optimization. Specifically, a positive semidefinite relaxation problem is used as a transmitting beamforming problem. An optimization minimization method is used for the transmission and reflection beam forming problem. And alternately optimizing the two sub-problems to obtain an optimal solution. Doppler frequency shift and multiple-input-multiple-output in an Internet of Vehicles scene are considered at the same time, joint beamforming optimization is carried out on communication and target sensing, and the system performance is improved.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle networking, and relates to an intelligent reflective surface-assisted vehicle networking communication perception integration method based on alternating optimization. Background Art

[0002] Communication-perception integration, which efficiently enables spectrum and hardware sharing between radar sensing and communication, has become a promising technology, significantly improving spectral and energy efficiency. Compared to traditional independent radar and communication system designs, communication-perception integration not only offers the advantages of lower cost and reduced interference, but also enables synergistic gains between radar and communication functions. Specifically, perception capabilities can enhance the performance of communication systems, while communication functions can also enhance perception. Therefore, communication-perception 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. Distinct from conventional wireless networks, where the environment is uncontrollable, reconfigurable smart surfaces present a new communication paradigm that can flexibly adjust 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, resulting in spatially incomplete wireless coverage. Compared to existing reflective-only reconfigurable smart surfaces, simultaneously transmissive and reflective reconfigurable smart surfaces have recently been developed to achieve a fully spatially intelligent radio environment, where base stations and vehicles can be located on either side of the simultaneously transmissive and reflective reconfigurable smart surface.

[0004] The realization of future autonomous vehicles requires robust connectivity and high quality of service between vehicles (i.e., vehicle-to-vehicle and vehicle-to-vehicle) and infrastructure (i.e., vehicle-to-infrastructure). The aforementioned advantages of SRS technology make SRS-assisted vehicle communication 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 intelligent reflective surface-assisted vehicle network communication perception integration method based on alternating optimization.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A vehicle communication and perception integrated optimization method supporting simultaneously transmissive and reflective reconfigurable smart surfaces, the method comprising the following steps:

[0008] S1. Based on the vehicle network inter-sensory integration system, a first channel model between the communication and perception integrated device, the simultaneously transmissive and reflective reconfigurable smart surface and the communication vehicle, and a second channel model between the communication and perception integrated device, the simultaneously transmissive and reflective reconfigurable smart surface and the perception vehicle are constructed respectively;

[0009] S2. Obtaining a communication signal-to-noise ratio and a perception signal-to-noise ratio of the vehicle network communication and perception integrated system based on the first channel model and the second channel model;

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

[0011] S4. Alternating optimization is used to decouple the variables and convert them into two tractable subproblems: the transmission and the transmission and reflection beamforming subproblems. Specifically, a semidefinite relaxation is used for the transmission beamforming problem, while an optimal minimization method is used for the transmission and reflection beamforming problem. The optimal solution is obtained by alternating optimization of the two subproblems.

[0012] Furthermore, in S1, a first channel model between the communication and perception integrated device, the simultaneously transmissive and reflective reconfigurable smart surface, and the communication vehicle, and a second channel model between the communication and perception integrated device, the simultaneously transmissive and reflective reconfigurable smart surface, and the perception vehicle are constructed respectively:

[0013] The first channel model is: the receiving signal of the communication vehicle;

[0014] The received signal of the communication vehicle is the sum of two parts. The first part is the product of the base station beamforming matrix, the base station-simultaneously transmitting and reflecting reconfigurable smart surface-communication vehicle channel and the base station-communication vehicle channel, and 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 of the sensing vehicle is the sum of two parts. The first part is the product of the base station beamforming matrix, the base station-simultaneously transmitting and reflecting 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 obtain the transmission signal and the perception signal of the vehicle network inter-sensory integration system:

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

[0019] The radar signal-to-noise ratio of the 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 said S3, a communication signal-to-noise ratio and a perception signal-to-noise ratio are determined based on the communication signal and the perception signal, with the goal of maximizing the communication vehicle signal-to-noise ratio while satisfying the minimum signal-to-noise ratio of the perception vehicle;

[0021] Defining a beamforming matrix for maximizing the communication signal-to-noise ratio and the communication vehicle at the base station, a sensing vehicle at the base station, and transmission beamforming and reflection beamforming at the simultaneously transmissive and reflective reconfigurable smart surface;

[0022] Furthermore, in S4, alternating optimization is used to decouple the variables and convert them into two tractable subproblems: the emission and transmission and reflection beamforming subproblems. Specifically, the emission beamforming problem uses a semidefinite relaxation method. The transmission and reflection beamforming problem uses an optimization minimization method. Alternating optimization of the two subproblems yields the optimal solution.

[0023] To solve the problem, maximizing the communication vehicle signal-to-noise ratio (SNR) is decoupled into two subproblems: the transmission and transmission and reflection beamforming subproblems. Specifically, in the transmission beamforming, the communication vehicle beamforming matrix and the sensing vehicle beamforming matrix are jointly optimized to maximize the communication vehicle SNR while keeping the transmission beamforming matrix and the reflection beamforming matrix fixed. Then, the transmission beamforming matrix and the reflection beamforming matrix are optimized while keeping the communication vehicle beamforming matrix and the sensing vehicle beamforming matrix constant.

[0024] For the transmit beamforming problem, a semidefinite relaxation method is used to transform the problem into a convex quadratic semidefinite programming problem, which is then solved using a convex optimization package. For the transmission and reflection beamforming problems, the target problem is first reformulated into a quadratic form. Then, an optimal minimization method is used to find an ideal replacement function. A semidefinite method is used to transform the problem into a quadratic optimization problem, and finally, the SROCR algorithm is used to obtain the optimal solution.

[0025] The beneficial effects of the present invention are:

[0026] First, the present invention achieves simultaneous base station communication and target perception in a system based on a simultaneously transmissive and reflective reconfigurable smart surface. By designing the beamforming matrix for the communicating vehicles at the base station, the beamforming matrix for the 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 (SNR) of all communicating vehicles is optimized, thereby improving the overall system performance.

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

[0028] Third, the present invention proposes alternating optimization to decouple variables and convert them into two easily tractable sub-problems, which are solved using a semi-definite relaxation algorithm and an optimization minimization method respectively to obtain better results.

[0029] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

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

[0032] Figure 2 Flowchart of the method of the present invention. DETAILED DESCRIPTION

[0033] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways 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 illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0034] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0035] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships 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 direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0036] See also Figures 1 and 2 , which is a joint beamforming method for simultaneously transmitting and reflecting reconfigurable intelligent surfaces to assist vehicle synaesthesia based on graph neural networks.

[0037] Figure 1 Schematic diagram of the alternating optimization method of the present invention; Figure 2 As shown, the objective function is decomposed into two sub-problems: transmit beamforming and transmission and transmit beamforming. Transmit beamforming is solved using a semi-definite relaxation algorithm, while transmission and 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 ends.

[0038] The graph neural network-based simultaneous transmission and reflection reconfigurable intelligent surface assisted vehicle synaesthesia integrated joint beamforming method of the present invention comprises the following steps:

[0039] S1. Initialization

[0040] Based on the integrated telematics system of the Internet of Vehicles (IoV), the first channel model between the integrated communication and perception device, the SRS, and the communicating vehicle, and the second channel model between the integrated communication and perception device, the SRS, and the sensing vehicle were constructed. Because the direct channel (i.e., the base station-to-communicating vehicle link) is severely obstructed by obstacles, the direct channel is ignored. Only the SRS auxiliary channel (i.e., the base station-to-SRS-to-communicating vehicle link) is considered. The transmission frame duration is T, which is divided into U subframes, with the duration of each subframe denoted as T / U. The vehicle's position is unlikely to change significantly within a subframe, so it is assumed to be constant and fixed within a subframe, but it may vary from one subframe to another. Therefore, the first channel model is: the received signal of the communicating vehicle; the received signal of the communicating vehicle is the sum of two components. The first component is the product of the base station beamforming matrix, the base station-to-SRS-to-communicating vehicle channel, and the base station-to-communicating vehicle channel; the second component is additive white Gaussian noise. The second channel model is the received signal from the sensing vehicle. The received signal from the sensing vehicle is the sum of two components: the first component is the product of the base station beamforming matrix, the base station-simultaneously transmissive and reflective reconfigurable smart surface-sensing vehicle channel, and the base station-sensing vehicle channel. The second component is additive white Gaussian noise.

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

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

[0043] S3. Constructing optimization goals

[0044] The communication signal-to-noise ratio and the perception signal-to-noise ratio are determined based on the communication signal and the perception signal, with the goal of maximizing the signal-to-noise ratio of the communication vehicle while satisfying the minimum signal-to-noise ratio, component unit mode, and energy constraints of the perception vehicle.

[0045] S4. Constructing the basic process of alternating optimization method

[0046] Alternating optimization is used to decouple the variables and transform the transmission and reflection problems into two tractable subproblems: the transmission problem and the transmission and reflection beamforming problem. Specifically, a semidefinite relaxation method is used for the transmission beamforming problem, while an optimal minimization method is used for the transmission and reflection beamforming problem. The optimal solution is obtained by alternating optimization of these two subproblems.

[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 limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A smart reflective surface-assisted vehicle networking communication perception integration method based on alternating optimization, characterized in that: The method comprises the following steps: S1. Based on the vehicle networking inter-sensory integration system, a first channel model between a communication perception integrated device, a simultaneously transmitting and reflecting reconfigurable smart surface and a communication vehicle and a second channel model between a communication perception integrated device, a simultaneously transmitting and reflecting reconfigurable smart surface and a perception vehicle are constructed respectively; S2. Obtaining a communication signal signal-to-noise ratio and a perception signal signal-to-noise ratio of the vehicle networking communication and perception integrated system based on the first channel model and the second channel model; S3, determining a communication signal-to-noise ratio and a perception signal-to-noise ratio based on the communication signal and the perception signal, with the goal of maximizing the communication vehicle signal-to-noise ratio while satisfying the minimum signal-to-noise ratio of the perception vehicle; S4. Alternating optimization is used to decouple the variables and convert them into two tractable sub-problems, namely, the emission and transmission and reflection beamforming sub-problems. Specifically, the emission beamforming problem uses a semi-positive definite relaxation problem; the transmission and reflection beamforming problem uses an optimization minimization method. Alternating optimization of the two sub-problems yields the optimal solution.

2. According to claim 1, a smart reflective surface-assisted vehicle networking communication perception integration method based on alternating optimization is characterized by: In S1, a first channel model between the synaesthesia integrated device, the simultaneously transmissive and reflective reconfigurable smart surface and the communication vehicle and a second channel model between the synaesthesia integrated device, the simultaneously transmissive and reflective reconfigurable smart surface and the perception vehicle are constructed respectively: The first channel model is: the receiving signal of the communication vehicle; Among them, the receiving signal of the communication vehicle is the 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 intelligent surface-communication vehicle channel and the base station-communication vehicle channel, and the second part is additive Gaussian white noise; The second channel model is: the received signal of the sensing vehicle; Among them, the received signal of the sensing vehicle is the 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 intelligent surface-sensing vehicle channel and the base station-sensing vehicle channel, and the second part is additive Gaussian white noise.

3. The method for integrating communication and perception of Internet of Vehicles using intelligent reflective surfaces assisted by alternating optimization according to claim 2 is characterized by: In S2, the first channel model and the second channel model obtain the transmission signal and the perception signal of the vehicle network interawareness integration system; The signal-to-noise ratio of communication vehicle k is defined as: the ratio of the channel gain of communication vehicle k to the channel gain of other communication vehicles, the channel gain of the sensing vehicle, and the noise; The radar signal-to-noise ratio of the 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 method for integrating communication and perception of Internet of Vehicles using intelligent reflective surfaces assisted by alternating optimization according to claim 3 is characterized by: In S3, the communication signal-to-noise ratio and the perception signal-to-noise ratio are determined based on the communication signal and the perception signal, with the goal of maximizing the communication vehicle signal-to-noise ratio while satisfying the minimum signal-to-noise ratio of the perception vehicle: The communication signal-to-noise ratio is maximized and the communication vehicle beamforming matrix at the base station, the sensing vehicle beamforming matrix at the base station, and the transmission beamforming and reflection beamforming at the simultaneously transmission and reflection reconfigurable smart surface are defined.

5. The method for integrating communication and perception of connected vehicles using intelligent reflective surfaces assisted by alternating optimization according to claim 4 is characterized by: In S4, alternating optimization is used to decouple the variables and convert them into two tractable sub-problems, namely, the emission and transmission and reflection beamforming sub-problems, specifically: The maximization of the communication vehicle signal-to-noise ratio and decoupling are divided into two sub-problems, namely, the transmission and transmission and reflection beamforming sub-problems; specifically, in the transmission beamforming, the communication vehicle beamforming matrix and the perception vehicle beamforming matrix are jointly optimized to maximize the communication vehicle signal-to-noise ratio, while keeping the transmission beamforming matrix and the reflection beamforming matrix fixed, and then, keeping the communication vehicle beamforming matrix and the perception vehicle beamforming matrix constant, optimizing the transmission beamforming matrix and the reflection beamforming matrix; further studying the overall iterative algorithm to produce the optimal solution; For the transmit beamforming problem, a semi-definite relaxation method is used to transform the problem into a convex quadratic semi-definite programming problem, which is then solved using a convex optimization package. For the transmission and reflection beamforming problems, the target problem is first organized into a quadratic problem, and then an optimal minimization method is used to find an ideal replacement function. A semi-definite method is used to transform the problem into a quadratic optimization problem, and finally the SROCR algorithm is used to obtain the optimal solution.

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