A vehicle-mounted intelligent reflective surface-assisted backscatter communication system

Abstract

The present invention discloses a backscatter communication system assisted by an on-board intelligent reflective surface. The system comprises: a radio frequency source, a receiver, a centralized on-board intelligent reflective surface, and multiple tags (devices). The vehicle equipped with the intelligent reflective surface can move on the road with a certain degree of flexibility, and there is mutual interference between the tag backscatter links. The present invention aims to find the optimal phase shift matrix of the on-board intelligent reflective surface at any position while ensuring the signal-to-interference-and-noise ratio (SINR) of the tag signal received by the receiver, with the goal of maximizing system energy efficiency, and further determine the optimal deployment position of the on-board intelligent reflective surface within its range of movement.

Description

Technical field:

[0001] The invention relates to a vehicle-mounted intelligent reflective surface-assisted backscattering communication system, belonging to the technical field of wireless communication networks. Background technology:

[0002] With the rapid development of IoT technology, the number of sensors it incorporates will grow exponentially. Maintaining the viability of these energy-constrained IoT sensors has become a major challenge. Low-power and low-complexity backscatter communication, which relies solely on passive reflection and modulation of incident radio frequency (RF) waves, is being used to address this challenge.

[0003] Backscatter tags are powered by RF energy and do not require any active RF components. Tags communicate by passively modulating RF signals, eliminating the need for an active RF transmission link. A bistatic backscatter architecture, consisting of a carrier transmitter (CE) emitting a sinusoidal signal and a separately located receiver, allows for extended communication range for tags placed near an RF source. However, this typically requires the carrier transmitter to transmit at high power or be located near the tag.

[0004] Smart Reflective Surfaces (RIS), with their ability to control wireless channels, low power consumption, and low cost, are being used to support low-power, IoT-based networks, significantly improving communication system performance. These RISs consist of numerous reflective elements, each of which applies a controllable phase shift to the incident signal. These phase shifts can be jointly optimized, yielding significant gains at the receiver.

[0005] Smart reflective surfaces have been demonstrated to aid backscatter communications, addressing the bottlenecks of short transmission distance, limited coverage, and low data rates in backscatter communications. However, existing literature considers fixed smart surface scenarios and ignores the impact of the smart reflective surface's deployment location on the backscatter communication system. Summary of the invention:

[0006] In light of this, the present invention provides a vehicle-mounted intelligent reflective surface-assisted backscatter communication system. This system uses the signal-to-interference-and-noise ratio (SIR) requirement for each backscatter tag received by the receiver as a constraint, and aims to maximize the energy efficiency of the RF source. The system determines the deployment location of the vehicle-mounted intelligent reflective surface and the phase shift of the reflective elements within it. To achieve the aforementioned technical objectives, the present invention employs the following solutions:

[0007] A backscatter communication system assisted by an on-board intelligent reflective surface comprises a radio frequency source, a receiver, an on-board intelligent reflective surface, and K backscatter tags. The radio frequency source transmits a carrier frequency signal to the K backscatter tags. The carrier frequency signal can be transmitted to the backscatter tags via the intelligent reflective surface or directly from the radio frequency source. The K backscatter tags respectively transmit tag signals to the on-board intelligent reflective surface. The tag signals can be reflected by the on-board intelligent reflective surface or directly received by the receiver, thereby completing the backscatter communication.

[0008] A vehicle equipped with an on-board intelligent reflective surface moves on a designated road section in units of vehicle body length, with L representing the current position of the on-board intelligent reflective surface and length representing the vehicle body length.

[0009] Furthermore, taking the signal-to-interference-and-noise ratio requirement of the tag signal received by the receiver from each backscattering tag as a constraint and maximizing the energy efficiency of the RF source as the goal, the deployment position of the on-board smart reflective surface and the phase shift of the reflective unit therein are determined.

[0010] Furthermore, the backscatter links of the K backscatter tags share time and frequency resources, and thus interfere with each other.

[0011] Furthermore, the channel coefficients of the links between the backscattering tag and the smart reflective surface, between the radio frequency source and the backscattering tag, and between the smart reflective surface and the receiver in the system all obey the Rice fading model.

[0012] Furthermore, the expression of the signal-to-interference-and-noise ratio of the tag signal received by the receiver from the backscatter tag k is:

[0013]

[0014] Wherein, w represents the linear precoding vector of the carrier signal sent by the RF source to all backscatter tags at the RF source; represents the linear precoding used by the receiver to decode the tag signal from the backscattering tag s, k; is the power of the noise that obeys the Rayleigh distribution; b s (t), b k (t) is the baseband signal at the backscattered tags s and k; α s ∈[0,1],α k ∈[0,1] represents the splitting coefficient at the backscattered tags s and k; represents the reflection coefficient matrix at the vehicle-mounted intelligent reflective surface, θ n ∈[0,2π] represents the phase shift of the nth reflection unit; Denote the channel coefficient matrices between backscatter tag k and smart reflective surface, between backscatter tag k and receiver, between RF source and backscatter tag k, between smart reflective surface and receiver, and between RF source and smart reflective surface respectively; They represent the channel coefficient matrices between the backscatter tag s and the smart reflective surface, between the backscatter tag s and the receiver, and between the RF source and the backscatter tag s respectively; C represents the number of antennas equipped with the RF source, N represents the number of reflective units of the vehicle-mounted smart reflective surface, and M represents the number of antennas equipped with the receiver.

[0015] Furthermore, in the carrier signal sent by the RF source, the fraction α k The carrier signal is loaded with the backscatter tag's own signal and then reflected out, with a fraction of 1-α k The carrier frequency signal is used for power supply, and the expression of the circuit constraint at the backscatter tag k is obtained as follows:

[0016]

[0017] Where β∈[0,1] represents the energy conversion efficiency; ξ k represents the energy required to backscatter the circuit at tag k.

[0018] Furthermore, the expression of RF source energy efficiency is:

[0019]

[0020] in, is the transmission power of the RF source.

[0021] Furthermore, taking the signal-to-interference-to-noise ratio requirement of the tag signal received by the receiver from each backscatter tag as a constraint and maximizing the RF source energy efficiency as the goal, the following RF source energy efficiency maximization model is established to determine the deployment location of the on-board smart reflective surface and the phase shift of the reflective unit therein:

[0022]

[0023] Among them, η L represents the energy efficiency of the RF source when the position of the vehicle-mounted intelligent reflective surface is L; γ k,L,th It represents the lower limit of the signal-to-interference-noise ratio (SINR) of the tag signal received by the receiver from the backscatter tag k when the position of the vehicle-mounted intelligent reflective surface is L;

[0024] The RF source energy efficiency maximization model is rewritten as:

[0025]

[0026] in,

[0027] Furthermore, the slack variable {δ k} represents the achievable value of the circuit constraint at label k and its required value ξ k The difference between them is converted into the following problem using the MM algorithm:

[0028]

[0029] in, express The main diagonal elements of are 1, express is a positive semidefinite matrix, I is the identity matrix, is the Lipschitz constant,

[0030]

[0031] Furthermore, for any position L, the corresponding RF source energy efficiency maximization model and the problem after model conversion are solved to obtain the optimal phase shift matrix and maximum RF source energy efficiency of the vehicle-mounted intelligent reflective surface at any position L; the maximum value of the maximum RF source energy efficiencies corresponding to all obtained positions is taken, and the position corresponding to this maximum value is used as the optimal deployment position of the vehicle-mounted intelligent reflective surface for the entire road section.

[0032] Furthermore, the specific steps for solving the corresponding RF source energy efficiency maximization model and the problem after model conversion are as follows:

[0033] S1. Let the outer iteration number i = 1, the inner iteration number j = 1, As the starting vector of the iteration; initialize the current position L of the vehicle-mounted intelligent reflective surface to 0, and the maximum energy efficiency of the entire road section η max =0, optimal deployment position L for vehicle-mounted intelligent reflective surface * =0;

[0034] S2. Calculate T k and U k ;

[0035] S3. Use the CVX toolbox to solve the problem and obtain The best value

[0036] S4.Yes Perform eigenvalue decomposition, that is where Λ is a matrix The matrix is ​​composed of the eigenvectors of , and D is a diagonal matrix with eigenvalues ​​as the elements on the diagonal;

[0037] S5. Initialize the loop sequence number q = 1, and generate the vector according to the randA random search technology

[0038] S6. Calculate intermediate variables delete The last element of

[0039] S7. If q≤ρ, continue the loop, then q=q+1 and return to S5; if q>ρ, end the loop and go to S8; where ρ is the pre-specified random number of loops;

[0040] S8. Gather In the equation, the inequality constraint The element on the left that exceeds the right the most is used as the starting vector for the next iteration, where

[0041] S9. Repeat S2 to S8 until Converge and obtain the optimal phase shift of the current position L and maximum energy efficiency

[0042] S10. If but L * ←L;

[0043] S11. Update the vehicle's current position L = L + length, if L ≤ L max , then return to S2; if L>L max , the optimal phase shift matrix and maximum RF source energy efficiency of the vehicle-mounted intelligent reflective surface at any position are output; where length represents the length of the vehicle body.

[0044] The present invention provides a backscatter communication system assisted by an on-vehicle intelligent reflective surface. The system takes into account the impact of the deployment location of the intelligent reflective surface on the performance of the communication system, uses the signal-to-interference-and-noise ratio requirement of the tag signal received by the receiver from each backscatter tag as a constraint, and aims to maximize the energy efficiency of the radio frequency source. The deployment location of the on-vehicle intelligent reflective surface and the phase shift of the reflective unit therein are determined. Description of the drawings:

[0045] Figure 1 Schematic diagram of a vehicle-mounted intelligent reflective surface-assisted backscatter communication system according to an embodiment of the present invention;

[0046] Figure 2Establish a model for maximizing the energy efficiency of a backscatter communication system assisted by a vehicle-mounted smart reflective surface;

[0047] Figure 3 A method for solving the energy efficiency maximization model of the backscatter communication system assisted by vehicle-mounted smart reflective surfaces. Specific implementation methods:

[0048] In order to make the purpose and technical solution of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention.

[0049] According to an embodiment of the present invention, a vehicle-mounted intelligent reflective surface-assisted backscatter communication system is provided. The system model of this example is as follows: Figure 1 As shown in Figure 1, the system consists of a radio frequency source, a receiver, a vehicle-mounted smart reflective surface, and K backscatter tags. The radio frequency source is equipped with C antennas, the smart reflective surface is equipped with N reflective units, and the receiver is equipped with M antennas.

[0050] The vehicle equipped with the smart reflective surface can move on the road with a certain degree of flexibility to find the deployment location that optimizes the performance of the backscatter communication system; the vehicle is on the designated road section [0,L max ] moves forward in units of vehicle body length, with L representing the current position and length representing the vehicle body length. In the present invention, F, T, I, and R are used to represent the RF source, tag, smart reflective surface, and receiver, respectively.

[0051] like Figure 1 As shown, the solid line portion represents the carrier signal, and the dotted line portion represents the tag signal. Figure 1 This paper shows all the propagation paths considered by the present invention, that is, the present invention does not consider signals that have been reflected twice or more (except those reflected by smart reflective surfaces). The RF source sends a carrier signal to K backscatter tags. The carrier signal can be transmitted to the backscatter tags via the smart reflective surface or directly from the RF source to the tags. The K backscatter tags respectively send tag signals to the on-board smart reflective surface. The tag signals can be reflected by the on-board smart reflective surface or directly received by the receiver, completing the backscatter communication. The backscatter links of the K backscatter tags share time and frequency resources, and there is mutual interference.

[0052] A method for establishing a model to maximize the energy efficiency of a backscatter communication system assisted by an on-board intelligent reflective surface, such as Figure 2 The specific steps are as follows:

[0053] Step 201: Consider a fixed scenario where the vehicle is at position L. Assume that linear precoding is used at the RF source, that is, the precoding vector w is used to transmit the signal s(t) to all tags. The transmitted signal can be written as x C=ws(t). Therefore, the RF source transmission power P = ||w|| 2 The signal received at tag k is composed of the RF source-tag line-of-sight link and the forwarding link of the smart reflective surface, and is given by the following formula:

[0054]

[0055] Since no signal processing is performed, the noise factor is not considered. represents the reflection coefficient matrix at the smart reflective surface, θ n ∈[0,2π] represents the phase shift of the nth reflection unit; Here, we assume that all channel coefficients obey the Rice fading model. All of the above parameters will change with the position L.

[0056] Step 202: Define α k ∈[0,1] represents the splitting coefficient at tag k, that is, the fraction of the incident signal to be reflected by the tag, and the remaining 1-α k The signal energy is used to power the circuit, and the signal reflected by the tag is partly Given, where b k (t) is the baseband signal of the tag, and the remaining part is used to power the circuit. The energy is Given, where β∈[0,1] is the energy conversion efficiency. For simplicity, β is a constant that is equal in all labels. Therefore, the expression of the circuit power constraint is as follows:

[0057]

[0058] where ξ k represents the energy required at label k.

[0059] Step 203: When the vehicle is at position L, the receiver knows the signal of the RF source and can directly decode and eliminate it. The received signal at the receiver can be expressed as

[0060]

[0061] where n R =[n R,1 ,...,n R,M ] T is the noise vector at the receiver, obeying distributed; represents the linear precoding used by the receiver to decode the signal from tag k, g kCollection of express.

[0062] Step 204: The signal-to-interference-and-noise ratio (SINR) of the tag signal received by the receiver from the backscatter tag k is expressed as:

[0063]

[0064] L represents the current position of the vehicle-mounted intelligent reflective surface; represents the linear precoding used by the receiver to decode the tag signal from the backscattering tag s, k; is the power of the noise that obeys the Rayleigh distribution; b s (t), b k (t) is the baseband signal at the backscattered tags s and k; α s ∈[0,1],α k ∈[0,1] represents the splitting coefficient at the backscattered tags s and k; They represent the channel coefficient matrices between the backscatter tag s and the smart reflective surface, between the backscatter tag s and the receiver, and between the RF source and the backscatter tag s, respectively.

[0065] Step 205: The energy efficiency of the radio frequency source is defined as the ratio of the ergodic achievable rate to the transmit power, that is, the ratio of the total data rate to the total energy consumption. The specific expression of the energy efficiency of the radio frequency source is:

[0066]

[0067] in, is the transmission power of the RF source.

[0068] Step 206: According to an embodiment of the present invention, the vehicle-mounted intelligent reflective surface-assisted backscatter communication system uses the signal-to-interference-and-noise ratio requirement of the tag signal received by the receiver from each backscatter tag as a constraint and maximizes the radio frequency source energy efficiency as a goal. The following radio frequency source energy efficiency maximization model is established to determine the deployment location of the vehicle-mounted intelligent reflective surface and the phase shift of the reflective unit therein:

[0069]

[0070] Among them, η L represents the energy efficiency of the RF source when the position of the vehicle-mounted intelligent reflective surface is L, γ k,L,th It represents the lower limit of the signal-to-interference-noise ratio (SINR) of the tag signal received by the receiver from the backscatter tag k when the position of the vehicle-mounted intelligent reflective surface is L.

[0071] Step 207: The linear precoding vector w of the carrier signal sent by the radio frequency source to all backscatter tags at the radio frequency source can be obtained by the following model that minimizes the square norm of the precoding vector:

[0072]

[0073] Step 208: Simplify the model (M1) and rewrite it as:

[0074]

[0075] in Since the constraints here and tr(WH k,1 (Θ)) are only related to the square term in Θ, according to That is, the result is 1, regardless of its θ n Therefore, the SDP problem model can be solved using the CVX toolbox to obtain the optimal solution W SDR ,according to Decompose W SDR Then the optimal linear precoding vector w can be obtained SDR , which will be abbreviated as w in the subsequent solutions.

[0076] Step 209: After w is solved, the objective function of the RF source energy efficiency maximization model is only related to Θ. Therefore, the optimization of the phase shift Θ can be performed in the form of a feasibility problem. The RF source energy efficiency maximization model can be rewritten as:

[0077] (S1): findΘ

[0078]

[0079] Θ in The quartic function in Θ makes this problem highly non-convex.

[0080] Step 210: Use the minimization-maximization MM algorithm to obtain feasibility Θ, transforming the above problem into a more tractable approximation problem, approximating each quartic constraint with a simpler minimization constraint in each iteration. Using v and To express The square-order expression in can be expanded as follows:

[0081]

[0082] in

[0083] Find the following lower bound function instead

[0084]

[0085] in is the intersection point between f(x) and the lower bound function, is the gradient operator, is the Lipschitz constant (i.e. the maximum curvature of f(x)).

[0086] Will After substituting f(x), the lower bound function on the right side of the inequality is Then the original constraint can be rewritten as:

[0087]

[0088] in I is the identity matrix of variable size, v0 is the intersection point between the original constraint and the lower bound function, which is also the reference point of the lower bound function selected in each iteration.

[0089] In addition, another constraint in problem (S1), the circuit constraint The same can be rewritten as:

[0090]

[0091] in,

[0092] Through these transformations, we get A feasibility problem in which and its rank is 1. Then, the problem can be transformed into SDP by relaxing the rank constraint. By further transforming the objective into an explicit optimization form, a more favorable solution can be obtained.

[0093] Step 211: Introduce slack variables {δ k} represents the achievable value of the circuit constraint and its required value ξ k The difference between them can ensure that the signal to interference noise ratio constraint is met while giving priority to the k The more restrictive circuit constraints of the term are obtained and the problem is as follows:

[0094]

[0095] Step 212: Figure 3As shown, by solving problems (S1) and (S2) for each position L, we can obtain the optimal phase shift and maximum energy efficiency at any position. Furthermore, based on the maximum energy efficiency, we can obtain the optimal deployment position of the vehicle-mounted intelligent reflective surface on the entire road section. The specific solution method is as follows:

[0096] S1. Initialize the precoding vector w (i+1) , label splitting coefficient α (i) , combined vector G i , where i is the number of external iterations, initialized to 1; select As the starting point, the number of iterations within j is initialized to 1; and the current position of the vehicle-mounted intelligent reflective surface is initialized to L = 0, and the maximum energy efficiency of the entire road section is η max =0, optimal deployment position L for vehicle-mounted intelligent reflective surface * =0;

[0097] S2. Get T for each tag k and U k ;

[0098] S3. Use the CVX toolbox to solve problem (S2) and obtain The best value

[0099] S4.Yes Perform eigenvalue decomposition, that is where Λ is a matrix The matrix composed of the eigenvectors of , D is a diagonal matrix, and the elements on its diagonal are the eigenvalues;

[0100] S5. Initialize the loop sequence number q = 1, and generate the vector according to the randA random search technology

[0101] S6. Calculation according to Delete Vector The last element of

[0102] S7. Repeat S5 to S6. The number of cycles is specified by a random number ρ. If q≤ρ, continue the cycle. q increases by 1 each time the cycle is repeated. If q>ρ, the cycle ends and each cycle is recorded.

[0103] S8. will make the inequality constraint The left side exceeds the right side by the most As the starting vector for the next iteration in

[0104] S9. Repeat S2 to S8 until Convergence, j is the iteration number, each iteration j increases by 1, and the optimal phase shift of the current position L is obtained and maximum energy efficiency

[0105] S10. If but L * ←L;

[0106] S11. Update the vehicle's current position L = L + length. If L ≤ L max , then return to S2; if L>L max , the algorithm ends; length represents the length of the vehicle body;

[0107] S12. Output the optimal phase shift matrix for each position and maximum energy efficiency Further, the optimal deployment position L of the vehicle-mounted intelligent reflective surface within its moving range is obtained * and the corresponding maximum energy efficiency η max .

[0108] In summary, the present invention considers the impact of the mobility and deployment location of smart reflective surfaces on system performance. Through the proposed beamforming method, energy efficiency is maximized under the constraint of signal-to-interference-noise ratio, thereby improving the performance of the communication system.

[0109] It should be pointed out that the description of the above embodiments is only used to help understand the method of this application and its core idea. For ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to this application, and these improvements and modifications are also within the scope of protection of the claims of this application.

Claims

1. A vehicle-mounted intelligent reflective surface-assisted backscatter communication system, characterized in that: The system consists of a radio frequency source, a receiver, an on-board intelligent reflective surface, and K backscattering tags. The radio frequency source sends a carrier signal to the K backscattering tags. The carrier signal can be transmitted to the backscattering tags via the intelligent reflective surface or directly from the radio frequency source. The K backscattering tags respectively send tag signals to the on-board intelligent reflective surface. The tag signals can be reflected by the on-board intelligent reflective surface or directly received by the receiver, completing backscattering communication. A vehicle equipped with an on-board intelligent reflective surface moves on a designated road section in units of vehicle body length, with L representing the current position of the on-board intelligent reflective surface and length representing the vehicle body length. Taking the signal-to-interference-and-noise ratio requirement of the tag signal received by the receiver from each backscatter tag as the constraint and maximizing the energy efficiency of the RF source as the goal, the following RF source energy efficiency maximization model is established to determine the deployment location of the on-board smart reflective surface and the phase shift of the reflective unit therein: Among them, η L It represents the energy efficiency of the RF source when the position of the vehicle-mounted intelligent reflective surface is L; is the transmission power of the RF source; w represents the linear precoding vector of the carrier signal sent by the RF source to all backscatter tags at the RF source; γ k,L,th represents the lower limit of the signal-to-interference-noise ratio (SINR) of the tag signal received by the receiver from the backscattering tag k when the position of the vehicle-mounted intelligent reflective surface is L; β∈[0,1] represents the energy conversion efficiency; ξ k represents the energy required by the circuit at backscatter tag k; γ k,L represents the signal-to-interference-to-noise ratio (SINR) of the tag signal received by the receiver from the backscatter tag k; α k ∈[0,1] represents the splitting coefficient at the backscatter tag k; represents the channel coefficient matrix between the RF source and the smart reflective surface; represents the channel coefficient matrix between the backscatter tag k and the smart reflective surface; represents the channel coefficient matrix between the RF source and the backscatter tag k; θ n ∈[0,2π] represents the phase shift of the nth reflection unit; N represents the number of reflection units of the vehicle-mounted intelligent reflection surface; represents the linear precoding used by the receiver to decode the tag signal from the backscattered tag k.

2. The vehicle-mounted intelligent reflective surface-assisted backscatter communication system according to claim 1, characterized in that: The backscatter links of K backscatter tags share time and frequency resources.

3. The vehicle-mounted intelligent reflective surface-assisted backscatter communication system according to claim 1, characterized in that: In this system, the channel coefficients of the links between the backscatter tag and the smart reflective surface, between the RF source and the backscatter tag, and between the smart reflective surface and the receiver all obey the Rice fading model.

4. The vehicle-mounted intelligent reflective surface-assisted backscatter communication system according to claim 1, characterized in that: The expression of the signal-to-interference-to-noise ratio of the tag signal received by the receiver from the backscatter tag k is: in, represents the linear precoding used by the receiver to decode the tag signal from the backscatter tag s; is the power of the noise that obeys the Rayleigh distribution; b s (t), b k (t) is the baseband signal at the backscattered tags s and k; α s ∈[0,1] represents the splitting coefficient at the backscatter tag s; Represents the reflection coefficient matrix at the vehicle-mounted intelligent reflective surface; denote the channel coefficient matrices between the backscatter tag k and the receiver, and between the smart reflective surface and the receiver, respectively; They represent the channel coefficient matrices between the backscatter tag s and the smart reflective surface, between the backscatter tag s and the receiver, and between the RF source and the backscatter tag s respectively; C represents the number of antennas equipped with the RF source, and M represents the number of antennas equipped with the receiver.

5. The vehicle-mounted intelligent reflective surface-assisted backscatter communication system according to claim 4, characterized in that: The RF source energy efficiency maximization model is rewritten as: in, Furthermore, the slack variable {δ k } represents the achievable value of the circuit constraint at label k and its required value ξ k The difference between them is converted into the following problem using the MM algorithm: in, express The main diagonal elements of are 1, express is a positive semidefinite matrix, I is the identity matrix, l is the Lipschitz constant, 6. The vehicle-mounted intelligent reflective surface-assisted backscatter communication system according to claim 1, characterized in that: For any position L, solve the corresponding RF source energy efficiency maximization model and the problem after model transformation, and obtain the optimal phase shift matrix and maximum RF source energy efficiency of the on-board smart reflective surface at any position L; The maximum value of the maximum RF source energy efficiency corresponding to all the obtained positions is taken, and the position corresponding to the maximum value is used as the optimal deployment position of the vehicle-mounted intelligent reflective surface for the entire road section.

7. The vehicle-mounted intelligent reflective surface-assisted backscatter communication system according to claim 6, characterized in that: The specific steps for solving the corresponding RF source energy efficiency maximization model and the problem after model conversion are as follows: S1. Let the outer iteration number i = 1, the inner iteration number j = 1, As the starting vector of the iteration; initialize the current position L of the vehicle-mounted intelligent reflective surface to 0, and the maximum energy efficiency of the entire road section η max =0, optimal deployment position L for vehicle-mounted intelligent reflective surface * =0; S2. Calculate T k and U k ; S3. Use the CVX toolbox to solve the problem and obtain The best value S4.Yes Perform eigenvalue decomposition, that is where Λ is a matrix The matrix is ​​composed of the eigenvectors of , and D is a diagonal matrix with eigenvalues ​​as the elements on the diagonal; S5. Initialize the loop sequence number q = 1, and generate the vector according to the randA random search technology S6. Calculate intermediate variables delete The last element of S7. If q≤ρ, continue the loop, then q=q+1 and return to S5; if q>ρ, end the loop and go to S8; where ρ is the pre-specified random number of loops; S8. Gather In the equation, the inequality constraint The element on the left that exceeds the right the most is used as the starting vector for the next iteration, where S9. Repeat S2 to S8 until Converge and obtain the optimal phase shift of the current position L and maximum energy efficiency S10. If but L * ←L; S11. Update the vehicle's current position L = L + length, if L ≤ L max , then return to S2; if L>L max , the optimal phase shift matrix and maximum RF source energy efficiency of the vehicle-mounted intelligent reflective surface at any position are output; where length represents the length of the vehicle body.

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