Phase shift configuration method and system of intelligent reflective surface in vehicle-to-everything system

Abstract

The embodiment of the application provides a phase shift configuration method and system of an intelligent reflective surface in a vehicle networking system, the method comprising: determining a maximum selectable time slot length of phase shift configuration of the IRS, wherein, in each time slot, it is assumed that a channel environment remains unchanged, a number of vehicles and a motion state of the vehicles remain unchanged, and the channel environment comprises: a channel environment between the RSU and the IRS, a channel environment between the RSU and the vehicle, and a channel environment between the IRS and the vehicle; dividing a complete time of a service road section of any vehicle passing through the IRS into N time slots according to a target time slot length, and configuring phase shifts of the IRS in the N time slots in turn, wherein the target time slot length is smaller than the maximum selectable time slot length. The problem that the calculation intensity of a real-time dynamic control intelligent reflective surface strategy is too large and the energy consumption is large in the related art is solved.

Description

Technical Field

[0001] This application relates to the field of communication technology, and more specifically, to a phase-shift configuration method and system for an intelligent reflective surface in a vehicle networking system. Background Technology

[0002] The Internet of Things (IoT) is considered the third wave of development in the global information industry, following computers and the Internet of Vehicles (IoV). The IoV represents a significant intersection between the IoT and intelligent vehicles within the strategic emerging industries. IoV aims to solve traffic problems and effectively prevent traffic collisions. IoV is an information exchange network comprised of vehicle location, speed, and routes; it's a vehicle-network integrated technology developing towards information communication, environmental protection, energy conservation, and safety. Through electronic devices such as Pedestrian Re-identification (REID), cameras, sensors, GPS, and image processing, it collects information on vehicles, roads, and the traffic environment; and conducts wireless communication or information exchange between vehicles, roads, people, networks, the environment, and infrastructure according to certain communication protocols and standards. IoV is a crucial component of future intelligent transportation.

[0003] Intelligent Reflecting Surfaces (IRS) are a key technology in 6G. With their ability to control wireless channels, low power consumption, and low cost, they are widely used to assist wireless communication, significantly improving communication system performance. They also provide additional links when line-of-sight links are obstructed by obstacles. An IRS consists of many reflective elements, each applying a controllable phase shift to the incident signal. These phase shifts can be jointly optimized for beamforming.

[0004] Smart reflective surfaces have been used to assist vehicle communication, providing more reliable and widespread connectivity. However, the high-speed movement of vehicles in the Internet of Vehicles (IoV) results in a network topology characterized by high-speed dynamics and spatiotemporal complexity, introducing greater Doppler frequency shift and a complex, rapidly changing wireless propagation environment. Current technologies employ smart reflective surface strategies that dynamically adjust the phase shift for each incoming beam, achieving beamforming to make the received signal clearer. However, this leads to excessive computational intensity and high energy consumption, making practical deployment difficult.

[0005] There is currently no effective solution to the problem of excessive computational intensity and high energy consumption in real-time dynamic control intelligent reflective surface strategies in related technologies. Summary of the Invention

[0006] This application provides a phase shift configuration method and system for an intelligent reflective surface (IRS) in a vehicle networking system, which at least solves the problem of excessive computational intensity and high energy consumption of real-time dynamic control intelligent reflective surface strategies in related technologies.

[0007] In one embodiment of this application, a phase-shift configuration method for an intelligent reflective surface (IRS) in a vehicle-to-everything (V2X) system is proposed. The system comprises a roadside unit (RSU), an IRS deployed on a building, and multiple vehicles. The IRS is equipped with M reflective elements, each vehicle is equipped with Q antennas, and the RSU has C channels for the vehicles. The method includes: determining the maximum selectable time slot length for the phase-shift configuration of the IRS, wherein, within each time slot, the channel environment remains unchanged, the number of vehicles and the vehicle motion state remain unchanged, and the channel environment includes: the channel environment between the RSU and the IRS, the channel environment between the RSU and the vehicles, and the channel environment between the IRS and the vehicles; dividing the complete time of any vehicle v passing through the service segment of the IRS into N time slots according to a target time slot length, and configuring the phase shift of the IRS sequentially within the N time slots, wherein the target time slot length is less than the maximum selectable time slot length.

[0008] In one embodiment of this application, a phase shift configuration system for an intelligent reflective surface (IRS) within a vehicle-to-everything (V2X) system is also proposed. The system comprises a roadside unit (RSU), an IRS deployed on a building, and multiple vehicles. The IRS is equipped with M reflective elements, each vehicle is equipped with Q antennas, and the RSU has C channels for each vehicle. The system further includes: a determination module for determining the maximum selectable time slot length for the phase shift configuration of the IRS, wherein within each time slot, the channel environment remains unchanged, the number of vehicles and their motion states remain unchanged, and the channel environment includes: the channel environment between the RSU and the IRS, the channel environment between the RSU and the vehicles, and the channel environment between the IRS and the vehicles; and a configuration module for dividing the complete time of any vehicle v passing through the service segment of the IRS into N time slots according to a target time slot length, and sequentially configuring the phase shift of the IRS within the N time slots, wherein the target time slot length is less than the maximum selectable time slot length.

[0009] In one embodiment of this application, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to execute the steps of any of the above method embodiments at runtime.

[0010] In one embodiment of this application, an electronic device is also provided, including a memory and a processor, characterized in that the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0011] The phase shift configuration method for an intelligent reflective surface (IRS) in a vehicle-to-everything (V2X) system provided in this application determines the maximum selectable time slot length for IRS phase shift configuration. Within each time slot, the channel environment, the number of vehicles, and their motion state remain unchanged. Based on the target time slot length, the complete time of any vehicle v passing through the service segment of the IRS is divided into N time slots, and the phase shift of the IRS is configured sequentially within these N time slots. This method solves the problem of excessive computational intensity and high energy consumption in real-time dynamic control of intelligent reflective surface strategies in related technologies. Phase shift can be configured only once within the same time slot, significantly reducing the computational intensity of the intelligent reflective surface strategy and thus reducing energy consumption. Attached Figure Description

[0012] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0013] Figure 1 This is a flowchart of an optional phase-shift configuration method for an intelligent reflective surface in a vehicle networking system according to an embodiment of this application;

[0014] Figure 2 This is a schematic diagram of the structure of an optional phase-shift configuration system for an intelligent reflective surface in a vehicle networking system according to an embodiment of this application;

[0015] Figure 3 This is a flowchart of another optional phase-shift configuration method for a smart reflective surface in a vehicle networking system according to an embodiment of this application. Detailed Implementation

[0016] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0018] This application provides a phase-shift configuration method for an intelligent reflective surface (IRS) in a vehicle networking system. Figure 1This is a flowchart of an optional phase-shift configuration method for a smart reflective surface in a vehicle networking system according to an embodiment of this application, such as... Figure 1 As shown, the method includes:

[0019] Step S102: Determine the maximum selectable time slot length for the phase shift configuration of the IRS, wherein, within each time slot, the channel environment remains unchanged, the number of vehicles and the motion state of the vehicles remain unchanged, and the channel environment includes: the channel environment between the RSU and the IRS, the channel environment between the RSU and the vehicle, and the channel environment between the IRS and the vehicle;

[0020] Step S104: Divide the complete time of any vehicle v passing through the service segment of the IRS into N time slots according to the target time slot length, and configure the phase shift of the IRS in the N time slots in sequence, wherein the target time slot length is less than the maximum selectable time slot length.

[0021] It should be noted that the target time slot length can be selected according to actual needs. As long as the selected time slot length is smaller than the maximum selectable time slot length, it can be used. However, the computational intensity will be different. The optimal target time slot length can be selected according to actual needs, which can meet the needs of IRS service vehicles while minimizing computational intensity.

[0022] In one embodiment, the complete time of any vehicle v passing through the service segment of the IRS is divided into N time slots according to the target time slot length, and the phase shift of the IRS is configured sequentially within the N time slots, including:

[0023] Determine the channel matrix within any time slot n out of N time slots;

[0024] Within time slot n, the signal-to-noise ratio (SNR) at vehicle v is obtained according to the channel matrix;

[0025] The instantaneous bit rate of vehicle v is determined based on the signal-to-noise ratio (SNR) at vehicle v.

[0026] A model maximizing the average bit rate of the vehicle is established based on the instantaneous bit rate of the vehicle v.

[0027] The phase shift configuration of the IRS that maximizes the average bit rate of vehicles within time slot n in a multi-vehicle scenario is obtained by using the deep reinforcement learning DRL algorithm and the block coordinate descent BCD algorithm.

[0028] In one embodiment, determining the channel matrix within any time slot n out of N time slots includes: obtaining the channel gain between the RSU and IRS using the following formula.

[0029]

[0030] Where ρ is the average path loss power gain at a reference distance d0 = 1m, and K is the Rayleigh factor. The Euclidean distance between the IRS and RSU. It is a deterministic Loss component, defined as follows:

[0031]

[0032] in It is the cosine of the angle of arrival of the signal from RSU to IRS, and λ is the original signal wavelength;

[0033] The channel matrix between the IRS and vehicle v is obtained using the following formula. for:

[0034]

[0035] in Let n be the Euclidean distance between the IRS and vehicle v within time slot n. It is a deterministic Loss component, defined as follows:

[0036]

[0037] in It is the cosine of the angle of arrival of the signal from the IRS to vehicle v; the wavelength of the signal received by vehicle v from the IRS within time slot n is: Where c represents the speed of light;

[0038] The channel matrix between the RSU and vehicle v is obtained using the following formula. for:

[0039]

[0040] in Let be the Euclidean distance between the RSU and vehicle v within time slot n. It is a deterministic Loss component, defined as follows:

[0041]

[0042] in It is the cosine of the angle of arrival of the signal from the RSU to vehicle v; the wavelength of the signal received by vehicle v from the RSU in time slot n is:

[0043] In one embodiment, obtaining the signal-to-noise ratio (SNR) at vehicle v based on the channel matrix within time slot n includes:

[0044] The SNRγ is determined by the following formula. v,n:

[0045]

[0046] in These represent the channel matrices from RSU to IRS, from IRS to vehicle v, and from RSU to vehicle v within time slot n, respectively. θ represents the reflection coefficient matrix at the intelligent reflective surface. m,n ∈[0,2π] represents the phase shift of the m-th reflecting unit within the n-th time slot; P is the transmit power of the RSU; σ 2 This represents noise power.

[0047] In one embodiment, determining the instantaneous bit rate of vehicle v based on the signal-to-noise ratio (SNR) at vehicle v includes: determining the instantaneous bit rate I of vehicle v using the following formula. v,n :

[0048]

[0049] Where j v,n ∈[0,1] is a decision variable used to allocate RSU resources to vehicle v,j v,n =1 indicates that vehicle v is served in time slot n, otherwise it is 0.

[0050] In one embodiment, establishing a target model that maximizes the average bit rate of the vehicle v based on the instantaneous bit rate of the vehicle v includes:

[0051] The target model for maximizing the average bit rate of the vehicle is established using the following formula:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] Where N represents the total number of time slots; V n This represents the total number of vehicles on the road segment served by the intelligent reflective surface in time slot n; C represents the number of service lanes of the roadside unit (RSU).

[0058] In one embodiment, a deep reinforcement learning algorithm and a block coordinate descent algorithm are used to solve the target model to obtain the phase shift configuration of the IRS that maximizes the average bit rate of vehicles within time slot n in a multi-vehicle scenario, including:

[0059] S1, initialize angle parameters θ and φ, threshold value ε, and configure strategy π;

[0060] S2, for each vehicle v∈{0,1,2,,V n}Execute operations S3 to S8;

[0061] S3, Observation state f v,n S v,n x v,n y v,n , z v,n I v,n from Select action space a n The channel is allocated to the vehicle v scheduled for service. Where f v,n S represents the average bit rate from the current vehicle to the first vehicle in time slot n; v,n This represents the speed of vehicle v in time slot n; (x v,n ,y v,n ,z v,n () represents the coordinates of vehicle v in time slot n;

[0062] S4. According to the BCD algorithm, each m in m = 1, ..., M is corrected sequentially, and the angle value is calculated.

[0063] S5, repeat S4 until... convergence;

[0064] S6, within time slot n, if vehicle v is the first vehicle, then set a reward r. n =I v,n If the bit rate of vehicle v is I v,n <f v,n Then set the reward r n =f v,n -I v,n ;

[0065] S7, calculate the advantage estimate for all vehicles. Optimize the agent loss function using the Adam optimizer;

[0066] S8, Update the configuration policy

[0067] In one embodiment, determining the maximum selectable slot length for the phase shift configuration of the IRS includes: establishing a formula to maximize the average bit rate B received by vehicle v on the service segment of the smart reflector surface. v Reference model:

[0068]

[0069]

[0070] in This represents the average bit rate received by a single vehicle v on the service segment of the smart reflective surface;

[0071] The reference model is solved using a deep reinforcement learning algorithm and a block coordinate descent algorithm to obtain the phase shift configuration of the IRS that maximizes the average bit rate of vehicles within time slot n in a multi-vehicle scenario.

[0072] In one embodiment, determining the maximum selectable time slot length for the phase shift configuration of the IRS includes: based on the positioning error Δ, solving for the maximum positioning error that can maintain system performance Z at a preset ratio p, and establishing the following model:

[0073] maxΔ

[0074] stZ0≥pZ,

[0075] This model is a univariate model, with the only variable being the positioning error Δ. All other parameters, such as the phase shift setting of the intelligent reflective surface, remain consistent with the model when there is no error Δ = 0.

[0076] The above model is solved using the deep learning DRL algorithm to obtain the maximum vehicle positioning error Δ while maintaining the performance of a preset ratio p. max ;

[0077] The maximum selectable time slot length for the phase shift configuration of the IRS is determined to be:

[0078] Where S max Speed ​​limits are set for this section of road.

[0079] According to another embodiment of this application, a phase-shift configuration system for an intelligent reflective surface IRS in a vehicle-to-everything (V2X) system is also provided, for implementing the phase-shift configuration method of the intelligent reflective surface IRS in the above-described V2X system. The system includes modules for implementing any of the above method embodiments. The system comprises: a roadside unit (RSU), an IRS deployed on a building, and multiple vehicles. The IRS is equipped with M reflective elements, each vehicle is equipped with Q antennas, and the RSU has C channels for the vehicles. The system further includes:

[0080] The determining module is used to determine the maximum selectable time slot length of the phase shift configuration of the IRS, wherein, within each time slot, the channel environment remains unchanged, the number of vehicles and the motion state of the vehicles remain unchanged, and the channel environment includes: the channel environment between the RSU and the IRS, the channel environment between the RSU and the vehicle, and the channel environment between the IRS and the vehicle;

[0081] The configuration module is used to divide the complete time of any vehicle v passing through the service segment of the IRS into N time slots according to the target time slot length, and to configure the phase shift of the IRS in the N time slots in sequence, wherein the target time slot length is less than the maximum selectable time slot length.

[0082] The following is combined with Figures 2 to 3 This application describes a method for time slot allocation and configuration in a vehicle-to-everything (V2X) network assisted by a smart reflective surface, as described in an embodiment of this application. Figure 2 This is a schematic diagram of a smart reflective surface-assisted vehicle-to-everything (V2X) system according to one embodiment of this application. The system consists of a roadside unit (RSU), a smart reflective surface deployed on a building, and multiple vehicles. The smart reflective surface is equipped with M reflective elements, each vehicle is equipped with Q antennas, and the roadside unit (RSU) has C channels allocated for multiple vehicles. In multi-vehicle scenarios, the RSU must perform resource scheduling to avoid interference between vehicles. In this application, subscripts v, I, and R are used to represent vehicles, the smart reflective surface, and the roadside unit, respectively.

[0083] Assuming the service segment length of this intelligent reflective surface is L, the time taken for a vehicle to travel on this segment is T, and the time T is divided into N time slots, i.e., [1,2,3,...,n,,N], therefore the length of one time slot is... The speed limit on this section of road is S. max The average speed of vehicle v is S v,n Let v represent the speed of vehicle v in time slot n. And S max ≥S n (x) R ,y R ,z R ), (x I ,y I ,z I ), (x v,n ,y v,n ,z v,n ) represent the location information of the roadside unit (RSU), the intelligent reflective surface (IRS), and the vehicle v within the nth time slot, respectively.

[0084] The method for configuring vehicle-to-everything (V2X) time slots with intelligent reflective surfaces assisted in the embodiments of this application, such as... Figure 3 As shown, the specific steps are as follows:

[0085] Step 201: According to the intelligent reflective surface-assisted vehicle network time-slot configuration method of the present application embodiment, in each time slot, all channel environments no longer change due to vehicle movement, including the number of vehicles and their movement status, which remain consistent with the start time of the time slot, and are updated when the next time slot arrives. Figure 2This illustrates all propagation paths considered in the embodiments of this application; that is, signals that have undergone two or more reflections are not considered (except for those reflected by intelligent reflective surfaces). It is assumed that line-of-sight (LoS) links are dominant between the RSU and IRS, and between the IRS and vehicle v. Therefore, these channels experiencing small-scale fading are modeled as pure LoS Rayleigh fading. The channel gain between the RSU and IRS is also shown. It can be modeled as:

[0086]

[0087] Where ρ is the average path loss power gain at a reference distance d0 = 1m, and K is the Rayleigh factor. This represents the Euclidean distance between the IRS and RSU. A deterministic Loss component can be defined as follows:

[0088]

[0089] in λ is the cosine of the angle of arrival of the signal from RSU to IRS, and λ is the original signal wavelength. Similarly, the channel matrix between IRS and vehicle v within time slot n can be obtained. The model is as follows:

[0090]

[0091] in Let be the Euclidean distance between the IRS and vehicle v within time slot n. A deterministic Loss component can be defined as follows:

[0092]

[0093] in It is the cosine of the angle of arrival of the signal from the IRS to vehicle v; the wavelength of the signal received by vehicle v from the IRS in time slot n is: Where c represents the speed of light.

[0094] Step 202: Considering the potential obstacle interference between the RSU and vehicle v, therefore its channel... It can be modeled as:

[0095]

[0096] in Let be the Euclidean distance between the RSU and vehicle v within time slot n. A deterministic Loss component can be defined as follows:

[0097]

[0098] in It is the cosine of the angle of arrival of the signal from the RSU to vehicle v; the wavelength of the signal received by vehicle v from the RSU in time slot n is:

[0099] Step 203: Within time slot n, the received signal at vehicle v consists of the RSU-vehicle v line-of-sight link and the IRS forwarding link, as given by the following formula:

[0100]

[0101] in These represent the channel matrices from RSU to IRS, from IRS to vehicle v within time slot n, and from RSU to vehicle v within time slot n, respectively. θ represents the reflection coefficient matrix at the intelligent reflective surface. m,n ∈[0,2π] represents the phase shift of the m-th reflection unit within the n-th time slot; It is a noise vector that follows the pattern CN(0,σ). 2 I) Distribution; P is the transmit power at RSU.

[0102] Step 204: Within time slot n, the signal-to-noise ratio (SNR) at vehicle v can be expressed as:

[0103]

[0104] Where P is the transmit power of the RSU; σ 2 This represents noise power.

[0105] Step 205: Within time slot n, the instantaneous bit rate of vehicle v is expressed as:

[0106]

[0107] Where j v,n ∈[0,1] is a decision variable used to allocate RSU resources to vehicle v,j v,n =1 indicates that vehicle v is served in time slot n, otherwise it is 0.

[0108] Step 206: According to the intelligent reflective surface-assisted vehicle-to-everything (V2X) time-slot configuration method of this application embodiment, within each time slot, all channel environments remain unchanged, including the number of vehicles and their motion states, maintaining consistency with the start time of the time slot, and are updated when the next time slot arrives. Similarly, the intelligent reflective surface undergoes a phase-shift configuration once within each time slot. To solve for the specific configuration of the intelligent reflective surface in time slot n, a model maximizing the average bit rate of vehicles is established as follows:

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] Where V n This represents the total number of vehicles on the road segment served by the intelligent reflective surface in time slot n. Correspondingly, the second constraint states that the number of vehicles served is less than the number of independent lanes owned by the RSU. Therefore, the average bit rate per vehicle only considers the actual number of vehicles served.

[0115] Step 207: The above system model can be solved using Deep Reinforcement Learning (DRL) and Block Coordinate Descent (BCD) algorithms to obtain the intelligent reflective surface configuration that maximizes the average bit rate of vehicles within n time slots in a multi-vehicle scenario. The specific steps are as follows:

[0116] S1. Initialize angle parameters θ and φ, threshold value ε, and strategy π;

[0117] S2. For each vehicle v∈{0,1,2,...,V n}Execute operations S3 to S8;

[0118] S3. Observation state f v,n S v,n x v,n y v,n , z v,n I v,n from Select action space a n The channel is allocated to the vehicle v scheduled for service. Where f v,n S represents the average bit rate from the current vehicle to the first vehicle in time slot n; v,n This represents the speed of vehicle v in time slot n;

[0119] S4. According to the BCD algorithm, correct each m in m = 1, ..., M in turn, and calculate the angle value.

[0120] S5. Repeat S4 until... convergence;

[0121] S6. Within time slot n, if vehicle v is the first vehicle, then set a reward r. n =I v,n If the bit rate of vehicle v is I v,n <f v,n Then set the reward rn =f v,n -I v,n ;

[0122] S7. Calculate the advantage estimates for all vehicles. Optimize the agent loss function using the Adam optimizer;

[0123] S8. Update Strategy

[0124] According to an embodiment of this application, a method for configuring time slots in a vehicle-to-everything (V2X) network assisted by a smart reflective surface is proposed. Within each time slot, the number of vehicles, their movement status, and all channel environments remain unchanged. However, in reality, vehicles are still moving within each time slot, resulting in a positioning error Δ. If the divided time slot length is too large, the configuration of the smart reflective surface may become ineffective; conversely, the smaller the divided time slot, the greater the computational intensity of the smart reflective surface. Too small a time slot contradicts the original intention of this application embodiment to reduce the computational intensity of dynamically adjusting the smart reflective surface. Therefore, this application embodiment proposes a corresponding smart reflective surface-assisted V2X time slot division method, such as... Figure 3 As shown, the specific steps are as follows:

[0125] Step 301: Considering that frequent adjustments to the time slot length are not feasible in practical applications of intelligent reflective surfaces, and that the higher the vehicle speed, the greater the potential positioning error Δ within a single time slot, resulting in poorer system performance, this embodiment selects a road segment speed limit S to ensure that a single time slot setting meets most of the requirements of actual road conditions. max As the vehicle's moving speed in the time slot division method, the positioning error is: and

[0126] Step 302: Based on step 205, the average bit rate received by a single vehicle v on the service segment of the smart reflective surface can be expressed as:

[0127]

[0128] Assuming a positioning error Δ = 0, establish a system to maximize the average bit rate B received by vehicle v on the service segment of the smart reflective surface. v The model is as follows:

[0129]

[0130]

[0131] Step 303: Referring to step 206, the established average bit rate B received by vehicle v on the service segment of the smart reflective surface is maximized. vThe model can also be solved using deep reinforcement learning and block coordinate descent methods, with the specific steps as follows:

[0132] S1. Initialize angle parameters θ and φ, threshold value ε, and strategy π;

[0133] S2. Perform operations S3 to S8 for each time slot n∈{0,1,2,...,N};

[0134] S3. Observation state f n x v,n y v,n , z v,n I v,n from Select action space a n Where f n This represents the average bit rate of vehicle v from the current time slot to the first time slot;

[0135] S4. According to the BCD algorithm, correct each m in m = 1, ..., M in turn, and calculate the angle value θ. m,n =argmaxI v,n ;

[0136] S5. Repeat S4 until I. v,n convergence;

[0137] S6. Within time slot n, if vehicle v is the first vehicle, then set a reward r. n =I v,n If the bit rate of vehicle v is I v,n <f n Then set the reward r n =f n -I v,n ;

[0138] S7. Calculate the advantage estimates for all vehicles. Optimize the agent loss function using the Adam optimizer;

[0139] S8. Update Strategy

[0140] Step 304: According to the intelligent reflective surface-assisted vehicle-to-everything (V2X) time slot allocation method of this application embodiment, the system performance Z with a positioning error Δ = 0 obtained in step 303 is used as the error-free control case. Since it is impossible to derive a closed-form expression for the relationship between system performance and positioning error, it can be demonstrated by the impact of positioning error on channel gain. The specific steps are as follows:

[0141]

[0142] The portion of the channel gain related to vehicle positioning mentioned above is defined as... When it has no error, it can be expressed as:

[0143]

[0144] in Furthermore, when there is an error It can be represented as:

[0145]

[0146] Therefore, the effect of error on phase shift can be simplified as follows:

[0147]

[0148] Subsequently, the impact of the vehicle's positioning error Δ on the channel gain can be clearly demonstrated, thereby affecting the actual performance of the system. Substituting this into the system model shown in step 302, it can be observed that the larger the positioning error, the worse the resulting system performance Z.

[0149] Step 305: Given that positioning errors significantly reduce system performance Z, based on the intelligent reflective surface-assisted vehicle-to-everything (V2X) time slot allocation method of this application, i.e., solving for the maximum positioning error that can maintain a certain proportion p (e.g., 90%) of system performance Z, the model is established as follows:

[0150] maxΔ

[0151] stZ0≥pZ

[0152] This model is a univariate model, with the only variable being the positioning error Δ. All other parameters, such as the phase shift setting of the intelligent reflective surface, remain consistent with the model when there is no error Δ = 0.

[0153] Step 306: The above model can be solved using the deep learning DRL algorithm to obtain the maximum vehicle positioning error Δ under a certain system condition. max Then the maximum possible length of the divided time slots is... The intelligent reflective surface-assisted vehicle-to-everything (V2X) time slot partitioning method described in the embodiments of this application is completed.

[0154] Optionally, in this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0155] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0156] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0157] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0158] In the several embodiments provided in this application, it should be understood that the disclosed client can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0159] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0160] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0161] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A phase-shift configuration method for intelligent reflective surfaces in a vehicle networking system, characterized in that, The vehicle-to-everything (V2X) system comprises: a roadside unit (RSU), an intelligent reflective surface (IRS) deployed on a building, and multiple vehicles. The IRS is equipped with... M Each of the vehicles is equipped with a reflective unit. Q The RSU has one antenna. C A channel for the vehicle, the method includes: The maximum selectable time slot length for the phase shift configuration of the IRS is determined, wherein, within each time slot, the channel environment remains unchanged, the number of vehicles and the motion state of the vehicles remain unchanged, and the channel environment includes: the channel environment between the RSU and the IRS, the channel environment between the RSU and the vehicle, and the channel environment between the IRS and the vehicle; Based on the target time slot length, any vehicle v The complete time segment of the service route through the IRS is divided into N Each time slot, sequentially in the... N The phase shift of the IRS is configured within a time slot, wherein the target time slot length is less than the maximum selectable time slot length, including: Sure N The channel matrix within any time slot n in the time slot; In the time slot n Within the system, the signal-to-noise ratio (SNR) at vehicle v is obtained based on the channel matrix. According to the vehicle v The signal-to-noise ratio (SNR) at a given location determines the instantaneous bit rate of the vehicle v. According to the vehicle v A model is established to maximize the vehicle's average bit rate based on the instantaneous bit rate. The Deep Reinforcement Learning (DRL) algorithm and the Block Coordinate Descent (BCD) algorithm are used to solve the problem and obtain the time slots in a multi-vehicle scenario. n The phase-shift configuration of the IRS that maximizes the average bit rate of the vehicle.

2. The method according to claim 1, characterized in that, The determination of any one of the N time slots n The channel matrix within includes: The channel gain between RSU and IRS is obtained using the following formula. : in, α It is the path loss index, used to determine the signal attenuation rate. For the set of complex numbers, M It refers to the number of reflective units equipped in the IRS. For reference distance Average path loss power gain K It is the Rayleigh factor. The Euclidean distance between the IRS and RSU, ( x I , y I ,z I ) represents the three-dimensional coordinates of the intelligent reflective surface IRS. x R , y R ,z R () represents the three-dimensional coordinates of the roadside unit (RSU); It is a deterministic Loss component, defined as follows: in, It is the cosine of the angle of arrival of the signal from RSU to IRS. The original signal wavelength; IRS and vehicle information can be obtained using the following formula. v Channel matrix between for: in, for n Within the time slot, IRS and vehicles v The Euclidean distance between them Q The number of antennas installed on each vehicle. It is a deterministic Loss component, defined as follows: in, It is a signal from the IRS to the vehicle. v The cosine of the angle of arrival; n Vehicles within the time slot v The wavelength of the signal received by the IRS is: ,in c Represents the speed of light; RSUs and vehicles are obtained using the following formula. v Channel matrix between for: in, for n During the time slot, RSU and vehicles v The Euclidean distance between them, x v,n , y v,n ,z v,n ) for vehicles v exist n Three-dimensional coordinates within a time slot It is a deterministic Loss component, defined as follows: in, It is a signal from the RSU to the vehicle. v The cosine of the angle of arrival; n Vehicles in time slots v The wavelength of the signal received by the RSU is: ; in S vn Indicates vehicle v exist n The travel speed within the time slot.

3. The method according to claim 1, characterized in that, The step of obtaining the signal-to-noise ratio (SNR) at vehicle v based on the channel matrix within time slot n includes: The SNR is determined by the following formula. v,n : in, , , They represent n RSU to IRS within the time slot n IRS to vehicle within time slot v , n RSU to vehicle within time slot v The channel matrix; This represents the reflection coefficient matrix at the intelligent reflective surface. Indicates in n Within the time slot, the first Phase shift of each reflecting unit; P This refers to the transmit power of the RSU; This represents noise power.

4. The method according to claim 3, characterized in that, The step of determining the instantaneous bit rate of vehicle v based on the signal-to-noise ratio (SNR) at vehicle v includes: The instantaneous bit rate of vehicle v is determined by the following formula. I v,n : in It is a decision variable used to allocate RSU resources to vehicles. v , Indicates vehicle v In the time slot n If served, then 0.

5. The method according to claim 4, characterized in that, According to the vehicle v The instantaneous bit rate is used to establish a model that maximizes the vehicle's average bit rate, including: The target model for maximizing the average bit rate of the vehicle is established using the following formula: in, N Indicates the total number of time slots; Indicates in n The total number of vehicles on the road segment served by the intelligent reflective surface during the time slot; C This indicates the number of service lanes for the Roadside Unit (RSU).

6. The method according to claim 5, characterized in that, The target model is solved using deep reinforcement learning and block coordinate descent algorithms to obtain the time slots in a multi-vehicle scenario. n The phase shift configuration of the IRS that maximizes the average bit rate of the vehicle includes: S1, Initialize angle parameters and Threshold value Configuration strategy ; S2, for each vehicle Execute operations S3 to S8; S3, Observation Status , , , , , from Select Action Space Allocate lanes to vehicles scheduled for service v ,in express n The average bit rate from the current vehicle to the first vehicle in the time slot; Indicates vehicle v In the time slot n speed; express n Time-slot vehicles v The coordinates; S4, according to the BCD algorithm, Each of them m Corrections are made sequentially, and angle values ​​are calculated. ; S5, repeat S4 until... convergence; S6, in time slot n Inside, if the vehicle v If it's the first vehicle, then a reward will be set. If the vehicle v bit rate Then set rewards ; S7, calculate the advantage estimate for all vehicles. The Adam optimizer is used to optimize the agent loss function; S8. Update the configuration policy. .

7. The method according to claim 1, characterized in that, Determining the maximum selectable slot length for the phase shift configuration of the IRS includes: The following formula is used to establish the maximum vehicle... v Average bit rate received on the service segment of the smart reflective surface Reference model: in Indicates a single vehicle v Average bit rate received on the service segment of the smart reflective surface; Indicates in n Within the time slot, the first Phase shift of each reflecting unit; The reference model is solved using a deep reinforcement learning algorithm and a block coordinate descent algorithm to obtain the phase shift configuration of the IRS that maximizes the average bit rate of vehicles within time slot n in a multi-vehicle scenario.

8. A phase-shift configuration system for an intelligent reflective surface within a vehicle networking system, characterized in that, The vehicle-to-everything (V2X) system comprises: a roadside unit (RSU), an intelligent reflective surface (IRS) deployed on a building, and multiple vehicles. The IRS is equipped with... M Each of the vehicles is equipped with a reflective unit. Q The RSU has one antenna. C A channel for the vehicle, the phase shift configuration system includes: The determining module is used to determine the maximum selectable time slot length for the phase shift configuration of the IRS, wherein, within each time slot, it is assumed that the channel environment remains unchanged, the number of vehicles and the motion state of the vehicles remain unchanged, and the channel environment includes: the channel environment between the RSU and the IRS, the channel environment between the RSU and the vehicle, and the channel environment between the IRS and the vehicle; The configuration module is used to configure any vehicle according to the target time slot length. v The complete time segment of the service route through the IRS is divided into N Each time slot, sequentially in the... N The phase shift of the IRS is configured within a time slot, wherein the target time slot length is less than the maximum selectable time slot length; including: Sure N The channel matrix within any time slot n in the time slot; In the time slot n Within the system, the signal-to-noise ratio (SNR) at vehicle v is obtained based on the channel matrix. According to the vehicle v The signal-to-noise ratio (SNR) at a given location determines the instantaneous bit rate of the vehicle v. According to the vehicle v A model is established to maximize the vehicle's average bit rate based on the instantaneous bit rate. The Deep Reinforcement Learning (DRL) algorithm and the Block Coordinate Descent (BCD) algorithm are used to solve the problem and obtain the time slots in a multi-vehicle scenario. n The phase-shift configuration of the IRS that maximizes the average bit rate of the vehicle.

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