RIS-assisted DFRC Internet of Vehicles energy efficiency optimization method and device, and storage medium

Abstract

The invention discloses an RIS-assisted DFRC Internet of Vehicles energy efficiency optimization method and device and a storage medium, and belongs to the technical field of Internet of Vehicles. The method comprises the following steps: acquiring an output signal; fourier transform is carried out on the output signal to obtain a frequency peak value, a maximum power echo signal is confirmed according to the frequency peak value, and the distance from the base station to the RIS and the distance from the RIS to the target vehicle are confirmed according to the maximum power echo signal; traversing a pre-generated codebook to find an azimuth angle corresponding to the phase shift matrix which enables the echo signal power to be maximum; and based on the channel state information of the direct channel and the reflection channel, maximizing the perception energy efficiency index under the condition of satisfying the signal-to-noise ratio constraint of a communication user and a radar detection signal-to-noise ratio threshold. According to the invention, the technical problem that the utilization efficiency of resources is low because the estimation overhead is increased due to the fact that traditional channel state information excessively depends on pilot signals can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Vehicles, and in particular to an energy efficiency optimization method, device and storage medium for a RIS (Reconfigurable Intelligent Surface)-assisted DFRC (Dual-Functional Radar-Communication) Internet of Vehicles. Background Art

[0002] With the rapid development of 5G technology, the Internet of Vehicles (IoV), as a core component of intelligent transportation systems, is gradually promoting the realization of autonomous driving and intelligent traffic management. However, in complex urban environments, signal propagation is often blocked by obstacles such as buildings, resulting in communication link interruptions or signal attenuation, seriously affecting the reliability and performance of the system. For this reason, RIS technology, as an emerging wireless communication enhancement means, has received extensive attention. RIS consists of a large number of programmable reflection elements, which can dynamically regulate the phase and amplitude of electromagnetic waves, optimize the signal propagation environment. Especially in non-line-of-sight (NLoS) scenarios, RIS-assisted integrated sensing and communication (ISAC) systems, where RIS reflects signals to vehicle targets by creating a directional path, performs active sensing to detect targets, and simultaneously transmits information symbols to multiple single-antenna users to improve sensing and communication performance.

[0003] In the Internet of Vehicles, DFRC systems are favored because they can simultaneously implement radar sensing and communication functions. However, traditional DFRC systems face problems such as signal attenuation and multipath effects in complex environments, resulting in a decline in radar detection accuracy and communication performance. Traditional channel state information (CSI) is overly dependent on pilot signals, leading to an increase in estimation overhead, and thus low resource utilization efficiency, making it difficult to meet the needs of practical applications.

[0004] Therefore, there is an urgent need for a RIS-assisted DFRC Internet of Vehicles energy efficiency optimization method, device and storage medium to solve the above technical problems. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a RIS-assisted DFRC Internet of Vehicles energy efficiency optimization method, device and storage medium, which can solve the technical problem that traditional channel state information is overly dependent on pilot signals, resulting in an increase in estimation overhead and thus low resource utilization efficiency.

[0006] To achieve the above object, the present invention is implemented by the following technical solutions:

[0007] In a first aspect, the present invention provides a RIS-assisted DFRC vehicle-to-everything energy efficiency optimization method, including:

[0008] Obtain the transmitted signal and echo signal of the base station;

[0009] Perform pulse compression on the echo signal and transmitted signal to obtain an output signal;

[0010] Perform Fourier transform on the output signal to obtain a frequency peak, confirm the maximum power echo signal according to the frequency peak, and confirm the distance from the base station to the RIS and the distance from the RIS to the target vehicle according to the maximum power echo signal;

[0011] By traversing the pre-generated codebook, find the azimuth angle corresponding to the phase shift matrix that maximizes the power of the echo signal;

[0012] Determine the vehicle position according to the distance from the base station to the RIS, the distance from the RIS to the target vehicle, and the azimuth angle, and construct a direct channel and a reflection channel based on the pre-measured base station position, the pre-measured RIS position, and the target vehicle position;

[0013] Maximize the sensing energy efficiency index based on the channel state information of the direct channel and the reflection channel under the conditions of satisfying the signal-to-noise ratio constraint of the communication user and the signal-to-noise ratio threshold of radar detection.

[0014] Further, performing pulse compression on the echo signal and the transmitted signal to obtain an output signal includes:

[0015] ,

[0016] wherein, represents the output signal, represents the conjugate of the transmitted signal, represents the echo signal.

[0017] Further, performing Fourier transform on the output signal to obtain the spectrum of the output signal, and obtaining the frequency peak according to the spectrum of the output signal. The expression of the frequency peak is:

[0018] ,

[0019] wherein, is the frequency peak, c is the speed of light, r is the target distance from the RIS to the target vehicle, is the measured Gaussian noise.

[0020] Furthermore, the azimuth angles of all target vehicles and the RIS in the pre-generated codebook correspond to the optimal phase shift matrix;

[0021] By traversing all the optimal phase shift matrices in the codebook, the azimuth angles of the target vehicles and the RIS are found based on the echo signal with the maximum power;

[0022] The optimization steps of the optimal phase shift matrix include:

[0023] The problem expression Q1 for optimizing the phase shift matrix to maximize the echo signal power is:

[0024] ,

[0025] , ,

[0026] ,

[0027] , ,

[0028] Let , is equivalent to , Q1 is equivalent to Q2:

[0029] ,

[0030] By using the semi-definite relaxation method to relax Q2, Q2 is reformulated as Q3:

[0031] ,

[0032] ,

[0033] ,

[0034] ,

[0035] Use the CVX toolbox to solve Q3 to obtain the solution matrix , by performing eigenvalue decomposition on the solution matrix to obtain the unitary matrix and the diagonal matrix:

[0036] ,

[0037] Generate a sub-optimal solution based on the unitary matrix and the diagonal matrix through the randomization method:

[0038] ,

[0039] Select the sub-optimal solution that maximizes the echo signal power as the optimal solution of Q3;

[0040] Repeat the optimization for each azimuth angle to generate a codebook containing the optimal phase shift matrix; Among them, is the conjugate transpose of, is the propagation path from the RIS to the target vehicle, is the angle of the target vehicle relative to the RIS, is the phase shift matrix, n is the index variable, N is the number of RIS reflection units, is the phase of each diagonal element, A is the propagation path from the base station to the RIS, is the receiving steering vector of the RIS, is the transmitting steering vector of the BS, is the arrival azimuth angle of the RIS, is the departure azimuth angle of the BS, is the diagonal element of the RIS phase shift matrix, is the solution matrix, is the unitary matrix, is the diagonal matrix, is the suboptimal solution, is a random vector that follows a circularly symmetric complex Gaussian distribution.

[0041] Furthermore, constructing the direct channel and the reflection channel based on the predicted base station position, the predicted RIS position, and the target vehicle position includes:

[0042] Calculate the path elevation angle from the m-th antenna of the base station to the n-th unit of the RIS according to the predicted base station position and the predicted RIS position :

[0043] ,

[0044] Calculate the phase shift angle of the n-th reflection unit in the RIS according to the predicted RIS position and the target vehicle position :

[0045] ,

[0046] Among them, represents the position of the m-th antenna of the BS in the three-dimensional Cartesian coordinate system, M is the total number of antennas, represents the position of the n-th unit of the RIS in the three-dimensional Cartesian coordinate system, N is the total number of RIS units, is the target vehicle position, is the Euclidean norm;

[0047] Construct the LoS component according to the path elevation angle and the phase shift angle:

[0048] ,

[0049] ,

[0050] wherein, is the large-scale path loss coefficient from the m-th BS antenna to the n-th RIS unit, is the large-scale path loss coefficient from the n-th RIS unit to the target vehicle, is the carrier wavelength;

[0051] Based on the LoS component, a direct channel and a reflection channel are constructed:

[0052] ,

[0053] ,

[0054] wherein, is the direct channel, is the reflection channel, is the Rice factor, and are the Rayleigh fading components in the non-line-of-sight.

[0055] Furthermore, under the conditions of satisfying the signal-to-noise ratio constraint of communication users and the signal-to-noise ratio threshold of radar detection, the expression M1 for maximizing the sensing energy efficiency metric includes:

[0056] ,

[0057] ,

[0058] ,

[0059] ,

[0060] wherein, is the beamforming vector of user k, is the phase shift matrix, is the sensing signal-to-noise ratio threshold required by the k-th communication user, is the base station power budget, is the baseband equivalent channel from the BS to user k, is the signal-to-noise ratio bound threshold required by the k-th communication user, K is the total number of users, is the azimuth angle of arrival of the target vehicle on the propagation path from the RIS to the target vehicle, is the azimuth angle of departure of the target vehicle on the propagation path from the RIS to the target vehicle, is the sensing noise variance, is the communication noise variance, is the circuit power, represents the l-th amplitude reflection coefficient of the RIS, is the number of amplitude reflection coefficients, indicates that all the amplitude reflection coefficients of the RIS are normalized to 1.

[0061] Furthermore, solving the maximized sensing energy efficiency metric includes:

[0062] According to the Dinkelbach algorithm, the maximized sensing energy efficiency metric problem M1 is reformulated as M2:

[0063] ,

[0064] ,

[0065] , ,

[0066] ,

[0067] Introduce the auxiliary optimization variable matrix Reformulate M2 as M3:

[0068]

[0069] ,

[0070] ,

[0071] ,

[0072] where , , and are all intermediate variables to reduce the formula length, is the auxiliary variable, is the user beamforming vector, is the set of K;

[0073] Optimize the beamforming vector and update the auxiliary variable , remove the rank constraint , transform M3 into a relaxed semidefinite programming problem, solve the relaxed semidefinite programming problem using the CVX toolbox, obtain the optimized , and recover from the optimized by the Gaussian randomization method. According to the Maximize the perceived energy efficiency metric.

[0074] In a second aspect, the present invention provides a RIS-assisted DFRC vehicle-to-everything (V2X) energy efficiency optimization device, comprising:

[0075] A signal acquisition module, configured to acquire the transmitted signal and the echo signal of the base station;

[0076] A pulse compression module, configured to perform pulse compression on the echo signal and the transmitted signal to obtain an output signal;

[0077] A distance confirmation module, configured to perform Fourier transform on the output signal to obtain a frequency peak, confirm the maximum power echo signal according to the frequency peak, and confirm the distance from the base station to the RIS and the distance from the RIS to the target vehicle according to the maximum power echo signal;

[0078] A traversal module, configured to find the azimuth angle corresponding to the phase shift matrix that maximizes the echo signal power by traversing a pre-generated codebook;

[0079] A channel construction module, configured to determine the vehicle position according to the distance from the base station to the RIS, the distance from the RIS to the target vehicle, and the azimuth angle, and construct a direct channel and a reflected channel based on the pre-measured base station position, the pre-measured RIS position, and the target vehicle position;

[0080] A maximize perceived energy efficiency module, configured to maximize the perceived energy efficiency metric based on the channel state information of the direct channel and the reflected channel, under the conditions of satisfying the signal-to-noise ratio (SNR) constraint of the communication user and the radar detection SNR threshold.

[0081] In a third aspect, the present invention provides an electronic terminal, comprising a processor and a memory connected to the processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the method described in any one of the above are executed.

[0082] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0083] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0084] The present invention first proposes an algorithm for radar sensing in a RIS-assisted DFRC system. On the basis of realizing radar sensing, location-based channel estimation is introduced. According to the obtained location-based channel state information, for an integrated sensing and communication (ISAC) system centered on sensing, a sensing SNR is proposed as a performance metric for the sensing-centered energy efficiency (EE), realizing the maximization of the perceived energy efficiency, reducing the dependence on pilot signals and the channel estimation overhead, and improving the resource utilization efficiency.

[0085] The RIS-assisted radar system can achieve high-precision target detection in NLoS scenarios, providing reliable support for autonomous driving and intelligent transportation management. By optimizing the phase shift matrix of the RIS and the beamforming vector of the base station, the system energy consumption can be minimized while meeting the communication and sensing performance requirements. Description of the Drawings

[0086] Figure 1 is a flowchart of an RIS-assisted DFRC vehicle-to-everything energy efficiency optimization method provided by an embodiment of the present invention;

[0087] Figure 2 is a relationship diagram in an RIS-assisted DFRC vehicle-to-everything energy efficiency optimization method provided by an embodiment of the present invention;

[0088] Figure 3 is a three-dimensional coordinate simulation diagram in an RIS-assisted DFRC vehicle-to-everything energy efficiency optimization method provided by an embodiment of the present invention;

[0089] Figure 4 is a schematic diagram of echo energy at different azimuth angles in an RIS-assisted DFRC vehicle-to-everything energy efficiency optimization method provided by an embodiment of the present invention;

[0090] Figure 5 is a distance estimation schematic diagram in an RIS-assisted DFRC vehicle-to-everything energy efficiency optimization method provided by an embodiment of the present invention;

[0091] Figure 6 is a convergence speed schematic diagram in an RIS-assisted DFRC vehicle-to-everything energy efficiency optimization method provided by an embodiment of the present invention;

[0092] Figure 7 is a schematic diagram of the influence of the number of RISs on the sensing EE in an RIS-assisted DFRC vehicle-to-everything energy efficiency optimization method provided by an embodiment of the present invention.

[0093] Figure 8 is a schematic diagram of the relationship between the sensing EE and the total transmit power of the base station in an RIS-assisted DFRC vehicle-to-everything energy efficiency optimization method provided by an embodiment of the present invention. Detailed Embodiments

[0094] The technical solution of the present invention will be described in detail below with reference to the drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present application and the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0095] In the present invention, the term "and / or" only describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, both A and B exist simultaneously, and B exists alone. In addition, in the present invention, the character " / " generally represents an "or" relationship between the front and back associated objects.

[0096] Embodiment 1:

[0097] Figure 1 is a flowchart of the RIS-assisted DFRC vehicle-to-everything (V2X) energy efficiency optimization method in Embodiment 1 of the present invention. This flowchart only shows the logical sequence of the method described in this embodiment. On the premise of non-conflict, in other possible embodiments of the present invention, the steps shown or described can be completed in a different Figure 1 order from that shown.

[0098] The RIS-assisted DFRC V2X energy efficiency optimization method provided in this embodiment can be applied to a terminal and can be executed by a mechanical equipment fault identification device. This device can be implemented in software and / or hardware, and can be integrated into the terminal, such as: any smartphone, tablet computer or computer device with communication functions. Refer to Figures 1 to 8 as shown, the method of this embodiment specifically includes the following steps:

[0099] Step 1: As Figure 2 shown, obtain the transmission signal of the base station and the echo signal that the transmission signal is reflected by the RIS to the target vehicle, then reflected back to the RIS by the target vehicle, and finally reflected back to the base station;

[0100] The BS is equipped with an m-element uniform linear array (ULA), and by simultaneously transmitting the transmission signal and the dedicated radar signal , it conducts downlink communication with K users and performs radar sensing on the target vehicle. The transmission signal is written as:

[0101] ,

[0102] where is the beamforming vector of user k, satisfies the normalized power constraint, that is . The dual-functional signal x is used for communication and radar sensing, where the data signals sent to users are independent of each other and are also not correlated with . By defining , the transmission power constraint at the BS is , where is the maximum transmission power budget.

[0103] Step 2: Perform pulse compression on the echo signal and the transmitted signal to obtain an output signal:

[0104] ,

[0105] where, denotes the output signal, denotes the conjugate of the transmitted signal, denotes the echo signal;

[0106] Regarding the radar model, the receive steering vector of the RIS and the transmit steering vector of the BS are and , is the angle of arrival (AoA) of the RIS, is the angle of departure (AoD) of the BS. The receive vector and the transmit steering vector can be expressed as:

[0107] ,

[0108] ,

[0109] where, d and are the antenna spacing and the carrier wavelength respectively;

[0110] The target response matrix , and the expression is:

[0111] ,

[0112] ,

[0113] and are the angle of arrival (AoA) and the angle of departure (AoD) on the target side respectively. Since the present invention considers a monostatic radar setup, AoA and AoD are the same, so = = .

[0114] Therefore, the echo signal from the target to the BS can be written as:

[0115]

[0116] where, is the total time delay, and . Corresponds to the total transmit power of the BS. For r = + The fading coefficient related to 2r denotes complex additive white Gaussian noise with a mean of and a variance of zero. Doppler frequency shift is not considered in the present invention.

[0117] Step 3: Perform a Fourier transform on the output signal to obtain the spectrum of the output signal, and obtain the frequency peak according to the spectrum of the output signal. The expression of the frequency peak is:

[0118] ,

[0119] where is the frequency peak, c is the speed of light, r is the target distance from the RIS to the target vehicle, is the measured Gaussian noise;

[0120] Confirm the maximum power echo signal according to the frequency peak, and confirm the distance from the base station to the RIS and the distance from the RIS to the target vehicle according to the maximum power echo signal;

[0121] Step 4: By traversing the pre-generated codebook, find the azimuth angle corresponding to the phase shift matrix that maximizes the echo signal power:

[0122] where all the azimuth angles between the target vehicle and the RIS in the pre-generated codebook correspond to the optimal phase shift matrix;

[0123] By traversing all the optimal phase shift matrices in the codebook, find the azimuth angle between the target vehicle and the RIS according to the echo signal with the maximum power, as Figure 3 shown, the azimuth angle is ;

[0124] The optimization steps of the optimal phase shift matrix include:

[0125] The problem expression Q1 for optimizing the phase shift matrix to maximize the echo signal power is:

[0126] ,

[0127] , ,

[0128] ,

[0129] , ,

[0130] Let , is equivalent to , Q1 is equivalent to Q2:

[0131] ,

[0132] Relax Q2 by the semidefinite relaxation method to re - formulate Q2 as Q3:

[0133] ,

[0134] ,

[0135] ,

[0136] ,

[0137] Solve Q3 using the CVX toolkit to obtain the solution matrix , and obtain the unitary matrix and diagonal matrix by eigenvalue decomposition of the solution matrix :

[0138] ,

[0139] Generate a sub - optimal solution by a randomization method based on the unitary matrix and diagonal matrix:

[0140] ,

[0141] Select the sub - optimal solution that maximizes the echo signal power as the optimal solution of Q3;

[0142] Repeat the optimization for each azimuth angle to generate a codebook containing the optimal phase - shift matrix; where, is the conjugate transpose of , is the propagation path from the RIS to the target vehicle, is the angle of the target vehicle relative to the RIS, is the phase - shift matrix, n is the index variable, N is the number of RIS reflection elements, is the phase of each diagonal element, A is the propagation path from the base station to the RIS, is the receiving steering vector of the RIS, is the transmitting steering vector of the BS, is the azimuth angle of arrival at the RIS, is the azimuth angle of departure from the BS, is the diagonal element of the RIS phase - shift matrix, is the solution matrix, is the unitary matrix, is the diagonal matrix, is the sub - optimal solution, is a random vector obeying a circularly symmetric complex Gaussian distribution.

[0143] Step 5: Determine the vehicle position based on the distance from the base station to the RIS, the distance from the RIS to the target vehicle, and the azimuth angle, and construct the direct channel and the reflected channel based on the predicted base station position, the predicted RIS position, and the target vehicle position:

[0144] Calculate the path elevation angle from the m-th antenna of the base station to the n-th unit of the RIS according to the predicted base station position and the predicted RIS position :

[0145] ,

[0146] Calculate the phase shift angle of the n-th reflection unit in the RIS according to the predicted RIS position and the target vehicle position :

[0147] ,

[0148] where represents the position of the m-th antenna of the BS in the three-dimensional Cartesian coordinate system, and M is the total number of antennas, represents the position of the n-th unit of the RIS in the three-dimensional Cartesian coordinate system, and N is the total number of RIS units, is the target vehicle position, is the Euclidean norm;

[0149] Construct the LoS component according to the path elevation angle and the phase shift angle:

[0150] ,

[0151] ,

[0152] where is the large-scale path loss coefficient from the m-th BS antenna to the n-th RIS unit, is the large-scale path loss coefficient from the n-th RIS unit to the target vehicle, is the carrier wavelength. Construct the direct channel and the reflected channel based on the LoS component:

[0153] ,

[0154] ,

[0155] where is the direct channel, is the reflected channel, is the Rice factor, and are the Rayleigh fading components of the non-line-of-sight.

[0156] Step 6: Based on the channel state information of the direct channel and the reflected channel, jointly optimize and solve the optimal user k beamforming vector and the optimal phase shift matrix. Under the conditions of satisfying the signal-to-noise ratio constraint of the communication user and the signal-to-noise ratio threshold of radar detection, maximizing the sensing energy efficiency metric includes:

[0157] The expression M1 for maximizing the sensing energy efficiency metric is:

[0158] ,

[0159] ,

[0160] ,

[0161] ,

[0162] where, is the user k beamforming vector, is the phase shift matrix, is the sensing signal-to-noise ratio threshold required by the k-th communication user, is the base station power budget, is the baseband equivalent channel from the BS to user k, is the signal-to-noise ratio bound threshold required by the k-th communication user, and K is the total number of users, is the arrival azimuth angle of the target vehicle on the propagation path from the RIS to the target vehicle, is the departure azimuth angle of the target vehicle on the propagation path from the RIS to the target vehicle, is the sensing noise variance, is the communication noise variance, is the circuit power, represents the amplitude reflection coefficient of the l-th RIS, is the number of amplitude reflection coefficients, indicates that all the amplitude reflection coefficients of the RIS are normalized to 1;

[0163] Solving the maximization of the sensing energy efficiency metric includes:

[0164] According to the Dinkelbach algorithm, reformulate the maximization of the sensing energy efficiency metric problem M1 as M2:

[0165] ,

[0166] ,

[0167] , ,

[0168] ,

[0169] Introduce an auxiliary optimization variable matrix Reformulate M2 as M3:

[0170]

[0171] ,

[0172] ,

[0173] ,

[0174] wherein, 、 、 and are all intermediate variables for reducing the formula length, is an auxiliary variable, is the user beamforming vector, is the set of K;

[0175] Optimize the beamforming vector according to Dinkelbach's extended algorithm and update the auxiliary variable , remove the rank constraint , transform M3 into a relaxed semidefinite programming problem, solve the relaxed semidefinite programming problem using the CVX toolbox, and obtain the optimized , and recover from the optimized by the Gaussian randomization method, and maximize the sensing energy efficiency index according to the .

[0176] wherein, optimizing the beamforming vector according to Dinkelbach's extended algorithm and updating the auxiliary variable includes:

[0177] Given optimize , for a given , transform by matrix transformation to obtain:

[0178] ,

[0179] ,

[0180] ,

[0181] ,

[0182] wherein, F is a Hermitian matrix, and are both intermediate quantities. Further, we have:

[0183] ,

[0184] wherein, is 's diagonal term, and we get:

[0185]

[0186] ,

[0187] wherein, is also an intermediate quantity. However, and are of different dimensions and cannot be solved in the same problem. Next, based on the Majorization-Minimization (MM) method, we further process :

[0188] For any given point in the feasible region, the objective inequality is:

[0189] ,

[0190] , ,

[0191] wherein, is the given point, T is the transpose, denotes 's real part, denotes 's imaginary part;

[0192] Maximizing is approximated as maximizing the term at the local point . Introducing to reconstruct the objective function, that is:

[0193] ,

[0194] , ,

[0195] wherein, is 's reconstruction matrix, is the real part sign-taking;

[0196] 's objective function (at the local point ) is approximated as:

[0197] ,

[0198] ,

[0199] Let , M3 becomes M4:

[0200] ,

[0201]

[0202] where represents the -th term of vector v;

[0203] Introduce an auxiliary variable t to transform M4 into a QCQP problem, and rewrite M4 as M5:

[0204]

[0205] ,

[0206]

[0207] , , , , ;

[0208] The constraint in M5 is a unit norm constraint. By defining , M5 can be reformulated as M6:

[0209]

[0210] where represents the diagonal elements of matrix ;

[0211] M6 can be solved by the semidefinite relaxation (SDR) method. By removing the rank constraint, M6 is a semidefinite programming problem and can be effectively solved. After solving, by calling the Gaussian randomization technique, a sub-optimal solution that satisfies is found. It should be noted that the solution method here is similar to the problem Q1 of solving the optimal phase shift matrix to maximize the echo signal power mentioned above, so it will not be elaborated here. Finally, the optimal phase shift matrix is obtained.

[0212] Given Optimize :

[0213]

[0214] Among them, , this situation can be directly solved to finally obtain the optimal beamforming vector.

[0215] In this embodiment, the effectiveness of the proposed method is verified through simulation. A three-dimensional coordinate system as Figure 3 shown is established, where the BS and RIS are located on the x-axis and the y-z plane respectively. The first antenna of the BS is located at (24 m, 0, 20 m), and the first element of the RIS is located at (0, 32 m, 20 m). Then the distance between the BS and the RIS is fixed at r = 40 m. It is assumed that the vehicle is in the x-y plane, the carrier frequency is , the signal wavelength is , there is a half-wavelength interval between adjacent antennas / elements, the Rice factor KR = 5, M = 8, N = 64, and PT = 10 dBm.

[0216] A. Vehicle position estimation

[0217] The performance of the RIS assisting radar sensing in the DFRC vehicle network is as Figure 4 and Figure 5 shown. According to the positions of the BS and RIS described above, assuming the angle of departure (AOD) of the target side of the BS , and the angle of arrival (AoA) of the target side of the RIS .

[0218] Therefore, the azimuth position of the target vehicle relative to the RIS is within , ). Let , , . In addition, the angular quantization number K in the codebook is 180, which means the accuracy of the codebook is .

[0219] The estimation of the azimuth angle is as Figure 4 shown. By traversing all the phase shift matrices in the codebook, the echo signals of different candidate angles are obtained. The angle corresponding to the echo signal with the highest energy is considered as the estimated vehicle azimuth angle related to the RIS. As Figure 4 shown, the received signal power at is significantly higher than that of other candidate angles, where the ordinate represents the ratio of the received signal power to the transmitted signal power. Therefore, the vehicle azimuth angle is estimated at

[0220] The distance estimation is as Figure 5As shown, to reduce the impact of noise on distance estimation, the highest energy echo signal of pulse compression is selected. Since the signal processing is carried out at the BS, the measured distance of the echo signal is the distance from the BS to the RIS plus the distance from the RIS to the vehicle, that is . In the settings of this simulation 40m, ideally . As Figure 5 shown, the amplitude is the largest at . Then the distance from the RIS to the vehicle is obtained

[0221] B. Perception-centered energy efficiency design

[0222] Consider a DFRC system with one RIS, where the BS serves 2 user communications and performs single-target detection. The DFRC base station is equipped with M = 16 transceiver antennas. The total transmit power , the circuit power , the amplifier efficiency = 0.35, and the noise variance . The signal-to-noise ratio thresholds for user communication and target detection are respectively: , . The target angle (position relative to the RIS) is 70° (i.e., φ = 70°), and the RIS angle (position relative to the BS) is 45°.

[0223] The algorithm (BCD) proposed in this application is compared with the following benchmark algorithms:

[0224] Successive lower bound maximization (SLBM, Simplified Lattice Boltzmann Method, SLBM): To optimize , the SLBM method mainly seeks the lower bound of the objective function without considering the difference between the objective function being real and complex.

[0225] Phase Fixing (PF): The phase vector of the RIS is set to align with the target as much as possible.

[0226] No-RIS: There is no RIS in the studied DFRC system.

[0227] The convergence performance of the energy efficiency optimization method disclosed in the present invention is as Figure 6 shown. Figure 6 shows the relationship between the optimized perception energy efficiency (Energy Efficiency, EE) and the number of iterations in the case of N = 8, and the algorithm has a fast convergence speed.

[0228] Figure 7It shows the impact of the number of RIS elements on the perceived EE. As the number of RIS elements increases, the perceived EE also increases because there are more and more degrees of freedom to meet the perceived signal-to-noise ratio. Figure 8 A graph of the perceived EE versus the total transmit power of the base station is plotted. The signal-to-noise ratio increases with the increase of the total power. For the SLBM method, the performance improves slowly when the total power increases. In addition, when the total power is large enough, the PF method is better than the SLBM method because the PF method sets the RIS phase vector to be aligned with the target.

[0229] Example 2:

[0230] The second embodiment of the present invention provides a RIS-assisted DFRC vehicle network energy efficiency optimization device, including:

[0231] A signal acquisition module for acquiring the transmit signal and echo signal of the base station;

[0232] A pulse compression module for performing pulse compression on the echo signal and transmit signal to obtain an output signal;

[0233] A distance confirmation module for performing Fourier transform on the output signal to obtain a frequency peak, confirming the maximum power echo signal according to the frequency peak, and confirming the distance from the base station to the RIS and the distance from the RIS to the target vehicle according to the maximum power echo signal;

[0234] A traversal module for finding the azimuth angle corresponding to the phase shift matrix that maximizes the echo signal power by traversing a pre-generated codebook;

[0235] A channel construction module for determining the vehicle position according to the distance from the base station to the RIS, the distance from the RIS to the target vehicle, and the azimuth angle, and constructing a direct channel and a reflected channel based on the pre-measured base station position, the pre-measured RIS position, and the target vehicle position;

[0236] A maximum perceived energy efficiency module for maximizing the perceived energy efficiency index based on the channel state information of the direct channel and the reflected channel under the conditions of meeting the signal-to-noise ratio constraint of the communication user and the radar detection signal-to-noise ratio threshold.

[0237] The RIS-assisted DFRC vehicle network energy efficiency optimization device provided by the second embodiment of the present invention can execute the RIS-assisted DFRC vehicle network energy efficiency optimization method provided by the first embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0238] Example 3:

[0239] Embodiment 3 of the present invention further provides an electronic terminal, including a processor and a memory connected to the processor. A computer program is stored in the memory, and the processor is configured to operate according to the instructions to execute the steps of the method described in Embodiment 1.

[0240] The electronic terminal provided in Embodiment 3 of the present invention can execute the RIS-assisted DFRC vehicle networking energy efficiency optimization method provided in Embodiment 1 of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0241] Embodiment 4:

[0242] Embodiment 4 of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method described in Embodiment 1, and has corresponding functional modules and beneficial effects for executing the method.

[0243] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a device, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0244] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (apparatuses), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0245] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0246] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the process Figure 1 in one process or a plurality of processes and / or boxes Figure 1 or steps for implementing the functions specified in one box or a plurality of boxes.

[0247] The foregoing is only a preferred embodiment of the present invention, and it should be pointed out that for those of ordinary skill in the art, several improvements and modifications can be made without departing from the technical principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A RIS-assisted DFRC vehicle networking energy efficiency optimization method, characterized in that: include: Obtain the transmission signal and echo signal of the base station; Performing pulse compression on the echo signal and the transmission signal to obtain an output signal; Performing Fourier transform on the output signal to obtain a frequency peak, confirming a maximum power echo signal according to the frequency peak, and confirming a distance from the base station to the RIS and a distance from the RIS to the target vehicle according to the maximum power echo signal; By traversing the pre-generated codebook, the azimuth angle corresponding to the phase shift matrix that maximizes the echo signal power is found; Determine the vehicle position according to the distance from the base station to the RIS, the distance from the RIS to the target vehicle and the azimuth, and construct a direct channel and a reflected channel based on the predicted base station position, the predicted RIS position and the target vehicle position; Based on the channel state information of the direct channel and the reflected channel, the perception energy efficiency index is maximized while satisfying the communication user signal-to-noise ratio constraint and the radar detection signal-to-noise ratio threshold.

2. The RIS-assisted DFRC vehicle networking energy efficiency optimization method according to claim 1 is characterized in that: The output signal obtained by pulse compressing the echo signal and the transmission signal includes: ， in, Represented as the output signal, represents the conjugate of the transmitted signal, Indicates the echo signal.

3. The RIS-assisted DFRC vehicle networking energy efficiency optimization method according to claim 2 is characterized in that: The output signal is subjected to Fourier transform to obtain the frequency spectrum of the output signal, and the frequency peak is obtained according to the frequency spectrum of the output signal. The expression of the frequency peak is: ， in, is the frequency peak, c is the speed of light, r is the target distance from RIS to the target vehicle, To measure Gaussian noise.

4. The RIS-assisted DFRC vehicle networking energy efficiency optimization method according to claim 1 is characterized in that: The azimuth angles of all target vehicles and RIS in the pre-generated codebook correspond to the optimal phase shift matrix; By traversing all the optimal phase shift matrices in the codebook, the azimuth angle between the target vehicle and the RIS is found according to the echo signal with the maximum power; The optimization step of the optimal phase shift matrix includes: The problem expression Q1 of optimizing the phase shift matrix to maximize the echo signal power is: ， ， ， ， ， ， make , Equivalent to , Q1 is equivalent to Q2: ， Relax Q2 by semi-definite relaxation method, so that Q2 can be reformulated as Q3: ， ， ， ， Use the CVX toolkit to solve Q3 and get the solution matrix , by eigenvalue decomposition of the solution matrix Get the unitary and diagonal matrices: ， A suboptimal solution is generated by a randomization method based on the unitary matrix and the diagonal matrix: ， Select the suboptimal solution that maximizes the echo signal power as the optimal solution for Q3; Repeat the optimization based on each azimuth angle to generate a codebook containing an optimal phase shift matrix; in, for The conjugate transpose of is the propagation path from RIS to the target vehicle, is the angle of the target vehicle relative to the RIS, is the phase shift matrix, n is the index variable, N is the number of RIS reflection units, is the phase of each diagonal element, A is the propagation path from the base station to the RIS, is the receiving steering vector of RIS, is the transmission steering vector of the BS, is the arrival azimuth of RIS, is the departure azimuth of the BS, are the diagonal elements of the RIS phase shift matrix, To solve the matrix, is a unitary matrix, is a diagonal matrix, is a suboptimal solution, is a random vector that follows a circularly symmetric complex Gaussian distribution.

5. The RIS-assisted DFRC vehicle networking energy efficiency optimization method according to claim 1 is characterized in that: Constructing a direct channel and a reflected channel based on the predicted base station position, the predicted RIS position and the target vehicle position includes: Calculate the path elevation angle from the mth antenna of the base station to the nth unit of the RIS according to the predicted base station position and the predicted RIS position. : ， The phase shift angle of the nth reflection unit in the RIS is calculated according to the predicted RIS position and the target vehicle position. : ， in, represents the position of the mth antenna of the BS in the three-dimensional Cartesian coordinate system, M is the total number of antennas, represents the position of the nth unit of RIS in the three-dimensional Cartesian coordinate system, N is the total number of RIS units, is the target vehicle position, is the Euclidean norm; The LoS component is constructed based on the path elevation angle and phase shift angle: ， ， in, is the large-scale path loss coefficient from the mth BS antenna to the nth RIS unit, is the large-scale path loss coefficient from the nth RIS unit to the target vehicle, is the carrier wavelength; Construct the direct channel and the reflected channel based on the LoS components: ， ， in, For direct channels, is the reflection channel, is the Rice factor, and It is the non-line-of-sight Rayleigh fading component.

6. The RIS-assisted DFRC vehicle networking energy efficiency optimization method according to claim 5 is characterized in that: Under the conditions of satisfying the communication user signal-to-noise ratio constraint and the radar detection signal-to-noise ratio threshold, the expression M1 for maximizing the perception energy efficiency index includes: ， ， ， ， in, is the beamforming vector for user k, is the phase shift matrix, is the required perceived signal-to-noise ratio threshold for the kth communication user, is the base station power budget, is the baseband equivalent channel from BS to user k, is the signal-to-noise ratio threshold required by the kth communication user, K is the total number of users, is the arrival azimuth of the target vehicle on the propagation path from RIS to the target vehicle, is the departure azimuth of the target vehicle on the propagation path from RIS to the target vehicle, is the perceptual noise variance, is the communication noise variance, is the circuit power, represents the lth RIS amplitude reflection coefficient, is the number of amplitude reflection coefficients, All amplitude reflection coefficients representing RIS are normalized to 1.

7. The RIS-assisted DFRC vehicle networking energy efficiency optimization method according to claim 6 is characterized in that: Solving the maximum perceived energy efficiency index includes: According to the Dinkelbach algorithm, the problem M1 of maximizing the perceived energy efficiency index is reformulated as M2: ， ， ， ， ， Introducing auxiliary optimization variable matrix Re-express M2 as M3: ， ， ， in, , , and They are all intermediate variables that reduce the length of the formula. is an auxiliary variable, For users Beamforming vector, is the set of K; Optimizing beamforming vectors according to Dinkelbach's extended algorithm and update auxiliary variables , remove the rank constraint , transform M3 into a relaxed semi-positive programming problem, use the CVX toolkit to solve the relaxed semi-positive programming problem, and obtain the optimized , through the Gaussian randomization method from the optimized Recover from , according to the Maximize the perceived energy efficiency index.

8. A RIS-assisted DFRC vehicle networking energy efficiency optimization device, characterized in that: include: A signal acquisition module, used to acquire the transmission signal and echo signal of the base station; A pulse compression module, used for performing pulse compression on the echo signal and the transmission signal to obtain an output signal; A distance confirmation module, used for performing Fourier transform on the output signal to obtain a frequency peak, confirming a maximum power echo signal according to the frequency peak, and confirming a distance from the base station to the RIS and a distance from the RIS to the target vehicle according to the maximum power echo signal; A traversal module, used to find the azimuth angle corresponding to the phase shift matrix that maximizes the echo signal power by traversing the pre-generated codebook; A channel construction module, used to determine the vehicle position according to the distance from the base station to the RIS, the distance from the RIS to the target vehicle and the azimuth, and to construct a direct channel and a reflection channel based on the predicted base station position, the predicted RIS position and the target vehicle position; The module for maximizing the perceived energy efficiency is used to maximize the perceived energy efficiency index based on the channel state information of the direct channel and the reflected channel, while satisfying the communication user signal-to-noise ratio constraint and the radar detection signal-to-noise ratio threshold.

9. An electronic terminal, characterized in that: The method comprises a processor and a memory connected to the processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are executed.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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