A novel communication-aware symbiotic digital beam design method

Abstract

The present invention provides a novel communication-perception symbiotic digital beam design method, which belongs to the field of communication-perception integration. The method comprises the following steps: constructing a vehicle network communication-perception integration scenario, and establishing a communication signal model and a perception signal model based on the scenario; based on the communication signal model and the perception signal model, using CRLB as a measure of perception performance, constructing a beamforming matrix optimization problem under the CRLB constraint of target angle estimation, and obtaining a simplified optimization problem; solving the simplified optimization problem using a penalized dual decomposition algorithm, solving the optimal solution of auxiliary variables α and β based on an alternating optimization method, and continuously updating the auxiliary variables α and β, the auxiliary matrix Ω, and F. ξ Alternately perform the gradient ascent algorithm and the projection operation, iteratively calculate the simplified light-enhancing Lagrangian function until the objective function converges; and continuously iterate to output the optimized beamforming matrix W. The present invention can effectively improve the perception accuracy of the target and the communication rate.

Description

Technical Field

[0001] The present invention belongs to the field of communication and perception integration, and in particular refers to a novel communication and perception symbiotic digital beam design method. Background Art

[0002] The sixth-generation mobile communication network (6G) will be a diverse, intelligent system with wide coverage and large bandwidth, capable of providing both high-quality wireless connectivity and high-precision perception. However, the diversification of communication services has led to a shortage of limited spectrum resources. The operation of perception and communication subsystems on independent physical devices exacerbates spectrum waste and interference between multiple devices, exacerbating the contradiction between growing demand and resource shortages. However, the technological development of wireless communications and radar perception is both based on electromagnetic wave theory. The similarities in their research theories provide a theoretical basis for the feasibility of integrating communication and perception. Simultaneously, phased array multiple-input, multiple-output (MIMO) radar and massive MIMO communication technologies have been proposed and are evolving towards millimeter-wave frequency bands and large-scale antenna arrays. This has led to increasing similarities between communication and perception in terms of hardware architecture, channel characteristics, and signal processing. Consequently, integrating sensing and communication (ISAC) technology has gradually become a focus of attention and research.

[0003] There are two types of ISAC systems: radar and communication coexistence (RCC) and full-functional radar and communication (DFRC). RCC systems aim to leverage prior information to eliminate interference between radar and communication systems, thus enabling coexistence with minimal changes to existing hardware and architecture. Common coexistence approaches include time-division multiplexing (TDM) and frequency-division multiplexing (FDM). For example, J.M. Peha et al. used TDM and FDM to transmit communication signals on time-frequency resources unoccupied by radar, preventing spectral or temporal collisions between communication and radar signals. Furthermore, RCC systems can be jointly designed by optimizing the performance metrics of both systems. For example, B. Li et al. jointly optimized the radar beamforming matrix and the communication covariance matrix to minimize the effective interference power of the radar while maintaining average capacity. However, the communication and radar systems remain relatively independent, resulting in problems such as low hardware reuse, severe energy consumption, and mutual interference between multiple devices.

[0004] The DFRC system transmits integrated communication and perception signals, simultaneously performing both communication and radar functions. Transmitter resources in this system are fully shared, effectively reducing hardware costs and improving energy efficiency. The key technology of the DFRC system lies in the appropriate communication and perception fusion signal waveform to reduce interference from transmitted signal leakage on the receiver. A. Sabharwal et al. studied the application of full-duplex technology in DFRC systems and proposed a reasonable implementation solution, but it still cannot support simultaneous high-throughput communication and long-range perception. Furthermore, the DFRC system differs from existing base station hardware architecture, making it difficult to implement in the short term. Therefore, a new operating method for the integrated communication and perception system is needed. Summary of the Invention

[0005] The main purpose of this invention is to propose a new communication-aware symbiotic digital beam design method, which can effectively improve the perception accuracy of the target and the communication rate.

[0006] The present invention is achieved through the following technical solutions:

[0007] A novel communication-aware symbiotic digital beam design method includes the following steps:

[0008] Step S1: Constructing an integrated IoV communication and perception scenario, and establishing a communication signal model and a perception signal model for the IoV system based on the scenario. The scenario includes a base station, multiple vehicle users equipped only with receiving antennas, and perception targets. Parameters of the perception targets and vehicle users are estimated using the communication signals sent by the base station. The base station's perception results of the vehicle users are used to assist in beam alignment.

[0009] Step S2: Based on the communication signal model and the perception signal model established in step S1, CRLB is used as a measure of perception performance. A beamforming matrix optimization problem is constructed under the CRLB constraint of target angle estimation to maximize the communication rate between the base station and the vehicle user while meeting the perception accuracy requirement. The optimization problem is transformed using a fractional programming method and simplified according to the Schur complement condition to obtain the simplified optimization problem:

[0010]

[0011] stTr(Ω -1 )≤η0

[0012]

[0013] Ω≥0

[0014] F ξ =J ξ (W)

[0015] ∥W∥2 ≤P

[0016] Among them, α and β are auxiliary variables, W represents the beamforming matrix, Ω is the auxiliary semi-positive definite matrix, F ξ is the auxiliary matrix, in J ξ is the Fisher information matrix of the estimated vector ξ, η0 represents the threshold of the two-dimensional arrival angle estimation CRLB, F ΘΘ =F ξ (1:2; 1:2), F ΘΨ =F ξ (1:2; 3:5), F ΨΨ =F ξ (3:5; 3:5), P represents the maximum transmission power of the base station, Indicates taking the real part of a complex number, β=[β1,…,β K ] T , h k represents the baseband equivalent channel from the base station to the kth vehicle user, which is set as a flat fading channel model consisting of a large-scale fading component and a small-scale fading component. is the variance of the additive white Gaussian noise in the channel, |·| represents the absolute value, diag(·) represents the diagonal operation of the matrix, and ∥·∥ represents the Frobenius norm of the matrix;

[0017] Step S3: Use the penalty dual decomposition algorithm to solve the simplified optimization problem obtained in step S2, introduce the augmented Lagrangian function, fix the beamforming matrix W, and solve the optimal solution of the auxiliary variables α and β based on the alternating optimization method, and continuously update the auxiliary variables α and β, as well as the auxiliary matrices Ω and F. ξ ;

[0018] Step S4: Based on the auxiliary variables α and β obtained in step S3, and the auxiliary matrices Ω and F ξ , simplify the augmented Lagrangian function, alternately perform the gradient ascent algorithm and projection operation, and iteratively calculate the objective function of the simplified augmented Lagrangian function to converge;

[0019] Step S5: Determine the communication sum rate r in step S2 k Is it less than the convergence accuracy threshold, or has the maximum number of iterations been reached? If so, the iteration ends and the optimized beamforming matrix W is output. Otherwise, execute steps S3 and S4 to iterate.

[0020] Furthermore, the step S1 specifically includes the following steps:

[0021] Step S11: Construct a vehicle network communication and perception integration scenario including a base station, K vehicle users and a perception node. The base station is a full-duplex communication and perception integration base station with M antennas forming a linear array. The passive perception node has N h ×N v Uniform planar antenna, vehicle users use a single antenna;

[0022] Step S12: Establish a communication signal model: assign a beam to each vehicle user, and the base station's transmission signal is expressed as The received signal of the kth vehicle user is expressed as Where W=[w1,w2,…,w k ] is the beamforming matrix, s k (t) is the transmission data of vehicle user k at time slot t, h k represents the baseband equivalent channel from the base station to the kth vehicle user, represents the additive white Gaussian noise in the channel;

[0023] Step S13: Establish a perception signal model: According to the communication signal model, establish an echo signal model for the perception target reflection as in, Indicates the distance-Doppler signal transmitted by the base station, L represents the length of the transmission signal, H s,k represents the reflection link between the base station and the vehicle user, H s,t represents the reflection link between the base station and the sensing target, N s Represents the noise in the radar return.

[0024] Furthermore, the step S2 specifically includes the following steps:

[0025] Step S21: Set the vehicle user's received signal to y r =y0+n r , the received signal obeys Gaussian distribution. According to the received signal, according to the CRLB theorem, the Fisher information matrix of the vector to be estimated ξ is obtained According to J ξ The CRLB matrix for estimating the target two-dimensional arrival angle is Among them, ξ includes the horizontal angle and vertical angle between the base station and the sensing target, Indicates taking the real part of a complex number, Tr{·} indicates taking the trace of a matrix, Indicates J ξ It is a 5×5 matrix, which is divided into four blocks in the form of 2 rows and 2 columns, and 3 rows and 3 columns, so we get J ΘΘ =J ξ (1:2; 1:2), J ΘΨ =J ξ(1:2; 3:5), J ΨΨ =J ξ (3:5; 3:5);

[0026] Step S22: construct a beamforming matrix optimization problem under the target CRLB constraint to maximize the communication rate between the base station and the vehicle user. The optimization problem is expressed as:

[0027]

[0028] sttr(CRLB Θ )≤η0,

[0029]

[0030] Among them, γ k represents the ratio of the received signal to interference plus noise of VUEs, the subsequent polynomials represent the constraints, W represents the beamforming matrix, Then it is its feasible region, maxR sum represents the maximum communication sum rate under the beamforming matrix, and η0 represents the threshold of the two-dimensional arrival angle estimation CRLB;

[0031] Step S23: Apply the fractional programming method to transform the optimization problem constructed in step S22, and obtain the transformed optimization problem as follows:

[0032]

[0033] sttr(CRLB Θ )≤η0;

[0034] ∥W∥ 2 ≤P

[0035] Step S24: Introduce auxiliary positive semidefinite matrix and the auxiliary matrix F ξ , the optimization problem converted in step S23 is simplified according to the Schur complement condition, and the simplified optimization problem is obtained as follows:

[0036]

[0037] stTr(Ω -1 )≤η0

[0038]

[0039] Ω≥0

[0040] F ξ =J ξ (W)

[0041] ∥W∥2 ≤P

[0042] in,{·} -1 Indicates taking the inverse of a matrix.

[0043] Furthermore, the step S3 specifically includes the following steps:

[0044] Step S31: Based on the penalty dual decomposition algorithm framework, define the augmented Lagrangian function:

[0045]

[0046] stTr(Ω -1 )≤η0

[0047]

[0048] Ω≥0

[0049] F ξ =J ξ (w)

[0050] ∥w∥ 2 ≤p

[0051] Among them, ρ1 is the penalty parameter, Z represents the equation F ξ =j ξ (w) constraint-related dual variables;

[0052] Step S32: Fixed variables Ω, Z, F ξ ,W, solve the optimal solution of auxiliary variables α and β, and transform the objective function r k (α, β, W) is derived with respect to the auxiliary variables α and β, that is The optimal solutions of auxiliary variables α and β are as follows:

[0053]

[0054]

[0055] in, represents the set of all vehicle users;

[0056] Step S33: Based on the optimal solution of auxiliary variables α and β obtained in step S32, fix variables Z and W, further simplify the simplified optimization problem into a semi-positive definite programming problem, and use the interior point method to solve the auxiliary matrices Ω and F. ξ :

[0057]

[0058] stTr(Ω -1 )≤η0

[0059]

[0060] Ω≥0

[0061] Furthermore, the step S4 specifically includes the following steps:

[0062] Step S41: Auxiliary variables α and β obtained in step S3, and auxiliary matrices Ω and F ξ , bring in the solution J ξ (W), the augmented Lagrangian function in step S31 can be simplified to:

[0063]

[0064] in, express No. The row and column q elements, Representation matrix Middle The element in the row and column q;

[0065] Step S42: Use the gradient ascent projection method to continuously update the iterative beamforming matrix through gradient ascent. in, represents the beamforming matrix W at the i-th iteration, λ represents the iteration step size, The symbol represents the calculation of matrix gradient;

[0066] Step S43: Based on the solution of step S42, ensure that the beamforming matrix W satisfies the transmit power constraint condition ∥W∥ 2 ≤P, project the calculated beamforming matrix W into the feasible region: Among them, Proj w It is a projection operation, and the calculation method is

[0067] Step S44: Iterate step S42 and step S43 until the simplified augmented Lagrange function converges to obtain the beamforming matrix W, and express the degree of constraint violation: Among them, n represents the number of outer layer iterations, ∥·∥ ∞ Represents the infinite norm of the matrix, updates ρ1 and Z according to the degree of constraint violation, and through convergence iteration penalty parameters, ensures that the constraints are satisfied and the penalty parameters are reduced to zero. From the above description of the present invention, it can be seen that compared with the prior art, the present invention has the following beneficial effects:

[0068] 1. The present invention first constructs a communication signal model and a perception signal model based on the integrated communication and perception scenario of the Internet of Vehicles. Then, CRLB is used as a measure of perception performance. A beamforming matrix optimization problem is constructed under the CRLB constraint of target angle estimation. This maximizes the communication rate between the base station and the vehicle user while meeting the perception accuracy requirements, thereby effectively improving the system's perception accuracy of the target. The simplified optimization problem is then solved using a penalized dual decomposition algorithm. The augmented Lagrangian function is introduced, the beamforming matrix W is fixed, and the optimal solution of the auxiliary variables α and β is solved based on the alternating optimization method. The auxiliary variables α and β, as well as the auxiliary matrices Ω and F, are continuously updated. ξ , and bring in the solution result and simplify the light-enhancing Lagrangian function, alternately perform the gradient ascent algorithm and projection operation, and iteratively calculate the objective function of the simplified light-enhancing Lagrangian function until it converges. That is, based on the penalty dual decomposition algorithm framework, the communication rate maximization problem is decomposed into several sub-problem variables for solution, and the auxiliary variables are jointly designed and iteratively optimized. Finally, the beamforming matrix w is output through multiple optimization iterations, which effectively improves the communication rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0070] Figure 1 Flowchart of the present invention.

[0071] Figure 2 This is a flowchart of the problem transformation using the fractional programming method of the present invention.

[0072] Figure 3 This is a flow chart of the present invention using the penalty dual decomposition algorithm to transform the problem and solve the auxiliary parameters α and β.

[0073] Figure 4 Flowchart of the gradient ascent algorithm and projection operation iteratively optimizing the beamforming matrix W of the present invention.

[0074] Figure 5 Schematic diagram of the infrastructure BS, VUEs and sensing target locations of the present invention.

[0075] Figure 6 These are the beam patterns under different CRLB thresholds of the present invention.

[0076] Figure 7 This is a waveform simulation result diagram of the relationship between base station transmission power and communication speed of the present invention. DETAILED DESCRIPTION

[0077] The present invention is further described below through specific embodiments.

[0078] like Figure 1As shown in FIG, a novel communication-aware symbiotic digital beam design method includes the following steps:

[0079] Step S1: Constructing an integrated IoV communication and perception scenario, and establishing a communication signal model and a perception signal model for the IoV system based on the scenario. The scenario includes a base station, multiple vehicle users equipped only with receiving antennas, and perception targets. Parameters of the perception targets and vehicle users are estimated using the communication signals sent by the base station. The base station's perception results of the vehicle users are used to assist in beam alignment.

[0080] from Figure 2 It can be seen that the present invention establishes a communication perception integration model for the communication and perception environment between BS and VUEs in the vehicle networking scenario, and derives the target CRLB constraint CRLB Θ and power constraints, construct the beamforming matrix optimization problem, and apply the FP algorithm to simplify the beamforming matrix optimization problem. Finally, introduce auxiliary variables α and β as well as auxiliary semi-positive matrix Ω and auxiliary matrix F ξ Simplify the question format, including:

[0081] Step S11: Construct a vehicle network communication and perception integration scenario including a base station (BS), K vehicle users (VUEs) and a perception target point. The base station is a full-duplex communication and perception integration base station with M antennas forming a linear array. The passive perception node has N h ×N v A full-duplex communication and sensing integrated base station with a uniform planar antenna, and a single antenna for vehicle users;

[0082] Step S12: Establish a communication signal model: Allocate a beam to each vehicle user, and the base station's transmission signal is expressed as:

[0083]

[0084] The received signal of the kth vehicle user is expressed as:

[0085]

[0086] Where W=[w1,w2,…,w k ] is the beamforming matrix, s k (t) is the transmission data of vehicle user k at time slot t, h k represents the baseband equivalent channel from the base station to the kth vehicle user, which is set as a flat fading channel model consisting of a large-scale fading component and a small-scale fading component. represents the additive white Gaussian noise in the channel;

[0087] Step S13: Establish a perception signal model: Based on the communication signal model, establish an echo signal model reflected from the perception target:

[0088]

[0089] Among them, the first two terms in the formula come from the echoes of the vehicle user and the perception target respectively, Indicates the distance-Doppler signal transmitted by the base station, L represents the length of the transmission signal, H s,k represents the reflection link between the base station and the vehicle user, H s,t represents the reflection link between the base station and the sensing target, N s Represents the noise in the radar echo, and each echo beam noise obeys Gaussian white noise Indicates that the mean is μ and the variance is σ 2 By establishing the integrated communication and perception model between BS, VUEs and sensing targets in the IoV scenario as shown in formulas (1), (2), and (3), accurate sensing and positioning of sensing targets can be achieved while high-speed communication is performed between BS and VUEs.

[0090] Step S2: Based on the communication signal model and the perception signal model established in step S1, CRLB is used as a measure of perception performance. A beamforming matrix optimization problem is constructed under the CRLB constraint of target angle estimation to maximize the communication rate between the base station and the vehicle user while meeting the perception accuracy requirement. The optimization problem is transformed using the fractional programming (FP) method, and the transformed optimization problem is simplified according to the Schur complement condition to obtain the simplified optimization problem:

[0091]

[0092] stTr(Ω -1 )≤η0

[0093]

[0094] Ω≥0

[0095] F ξ =J ξ (W)

[0096] ∥W∥ 2 ≤P

[0097] Among them, α and β = [β1,...,β K ] T is an auxiliary variable, W represents the beamforming matrix, Ω is an auxiliary positive semidefinite matrix, Fξ is the auxiliary matrix, J ξ is the Fisher information matrix of the estimated vector ξ, η0 represents the threshold of the two-dimensional arrival angle estimation CRLB, F ΘΘ =F ξ (1:2; 1:2), F ΘΨ =F ξ (1:2; 3:5), F ΨΨ =F ξ (3:5; 3:5), P represents the maximum transmission power of the base station, Indicates taking the real part of a complex number, h k represents the baseband equivalent channel from the base station to the kth vehicle user, which is set as a flat fading channel model consisting of a large-scale fading component and a small-scale fading component. is the variance of the additive white Gaussian noise in the channel, |·| represents the absolute value, diag(·) represents the diagonal operation of the matrix, and ∥·∥ represents the Frobenius norm of the matrix;

[0098] The specific steps include:

[0099] Step S21: Set the vehicle user's received signal to y r =y0+n r , the received signal obeys Gaussian distribution, that is The received signal after interference elimination is rearranged by column vectorization. According to the CRLB theorem, the Fisher information matrix of the vector to be estimated ξ is obtained:

[0100]

[0101] In this formula, ξ is the parameter value to be estimated, including the horizontal angle and vertical angle between the BS and the perception target, Indicates taking the real part of a complex number, Tr{·} indicates taking the trace of a matrix, Indicates J ξ It is a 5×5 matrix, which is divided into four blocks in the form of 2 rows and 2 columns, and 3 rows and 3 columns, so we get J ΘΘ =J ξ (1:2; 1:2), J ΘΨ =J ξ (1:2; 3:5), J ΨΨ =J ξ (3:5; 3:5);

[0102] According to the above formula, the CRLB matrix for estimating the target two-dimensional arrival angle (2D-AOA) is:

[0103]

[0104] Step S22: construct a beamforming matrix optimization problem under the target CRLB constraint to maximize the communication rate between the base station and the vehicle user. The optimization problem is expressed as:

[0105]

[0106] sttr(CRLB Θ )≤η0 (6a)

[0107]

[0108] Among them, γ k represents the SINR (Signal to Interference plus Noise Ratio) received by VUEs, the subsequent polynomials (6a, 6b) represent the constraints, W represents the beamforming matrix, Then it is its feasible region, maxR sum represents the maximum communication sum rate under the beamforming matrix, and η0 represents the threshold of the two-dimensional arrival angle estimation CRLB;

[0109] Step S23: Apply the fractional programming method to transform the optimization problem constructed in step S22, and obtain the transformed optimization problem as follows:

[0110]

[0111] sttr(CRLB Θ )≤η0 (7a)

[0112] ∥W∥ 2 ≤P (7b)

[0113] Where P represents the maximum transmission power of the base station, α=[α1,...,α K ] T and β=[β1,...,β K ] T is an auxiliary variable;

[0114] Step S24: Introduce auxiliary positive semidefinite matrix and the auxiliary matrix F ξ , the optimization problem converted in step S23 is simplified according to the Schur complement condition, and the simplified optimization problem is obtained as follows:

[0115]

[0116] stTr(Ω -1)≤η0 (8a)

[0117]

[0118] Ω≥0 (8c)

[0119] F ξ =J ξ (W) (8d)

[0120] ||W|| 2 ≤P (8e)

[0121] in,{·} -1 Indicates taking the inverse of a matrix.

[0122] Step S3: Use the penalty dual decomposition (PDD) algorithm to solve the simplified optimization problem obtained in step S2. The augmented Lagrangian function is introduced, the beamforming matrix W is fixed, and the optimal solution of the auxiliary variables α and β is solved based on the alternating optimization method. The auxiliary variables α and β, as well as the auxiliary matrices Ω and F are continuously updated. ξ ;

[0123] like Figure 3 As shown, the specific steps include:

[0124] Step S31: Based on the penalty dual decomposition algorithm framework, define the augmented Lagrangian function (AL):

[0125]

[0126] st(8a)-(8e) (9a)

[0127] Among them, ρ1 is the penalty parameter, Z represents the equation F ξ =J ξ (W) constraint-related dual variables;

[0128] Step S32: Fixed variables Ω, Z, F ξ ,W, solve the optimal solution of auxiliary variables α and β, and transform the objective function r k (α, β, W) is derived with respect to the auxiliary variables α and β, that is The optimal solutions of auxiliary variables α and β are obtained as follows, where K represents the set of all vehicle users:

[0129]

[0130]

[0131] Step S33: Based on the optimal solution of auxiliary variables α and β obtained in step S32, fix variables Z and W, further simplify the simplified optimization problem into a semi-positive definite programming problem, and use the interior point method to solve the auxiliary matrices Ω and F. ξ :

[0132]

[0133] st(8a)-(8c) (12a)

[0134] Step S4: Based on the auxiliary variables α and β obtained in step S3, and the auxiliary matrices Ω and F ξ , bring in the solution and simplify the brightening Lagrangian function, alternately perform the gradient ascent algorithm and projection operation, and iterate to obtain the convergence of the objective function of the simplified brightening Lagrangian function;

[0135] like Figure 4 As shown, specifically including:

[0136] Step S41: Auxiliary variables α and β obtained in step S3, and auxiliary matrices Ω and F ξ , bring in the solution J ξ (W), the augmented Lagrangian function in step S31 can be simplified to:

[0137]

[0138] in, express No. The row and column q elements, Representation matrix Middle The element in the row and column q;

[0139] Step S42: Observe step S41, remove the terms irrelevant to W, and use the gradient ascent projection method to continuously update the iterative beamforming matrix W through gradient ascent:

[0140]

[0141] in, represents the beamforming matrix W at the i-th iteration, λ represents the iteration step size, The symbol represents the calculation of matrix gradient;

[0142] Step S43: Based on the solution of step S42, ensure that the transmit power constraint condition ∥W∥ is satisfied. 2 ≤P, project the calculated beamforming matrix W into the feasible region:

[0143]

[0144] in, It is a projection operation, and the calculation method is:

[0145]

[0146] In step S44, the iterative optimization algorithm in steps S42 and S43 is iterated repeatedly until the target value of the simplified augmented Lagrange function (i.e., formula (13)) in step S41 converges, and the beamforming matrix W is obtained. The degree of constraint violation is expressed according to the variables, matrices, and PDD algorithm solved in the above steps:

[0147]

[0148]

[0149] Where n represents the number of outer layer iterations, ||·|| ∞ Represents the infinite norm of the matrix, updates ρ1 and Z according to the degree of constraint violation, and iterates the penalty parameters through convergence to ensure that the constraints are satisfied and the penalty parameters will drop to zero.

[0150] Step S5, step S3 and step S4 are iterated repeatedly to jointly optimize the beamforming matrix W to achieve the maximum communication sum rate between BS and VUEs under the constraints of low power and perception accuracy, achieve high-speed communication and accurate perception, and determine the communication sum rate r in step S2. k Is it less than the convergence accuracy threshold, or has the maximum number of iterations been reached? If so, the iteration ends and the optimized beamforming matrix W is output. Otherwise, execute steps S3 and S4 to iterate.

[0151] Figure 5 In the scenario shown, there is a BS with a communication and perception integrated system and four VUEs, including a passive perception node, which are distributed in a three-dimensional Cartesian coordinate system as shown in Figure 5 As shown in the figure, the RCS of VUEs and passively perceived weak targets are set to ζ k =20dBsm and ζ t =0dBsm, the number of BS transmitting antennas and the number of receiving antennas are both M=N=64.

[0152] Figure 6 In the figure, the horizontal axis is the two-dimensional target estimation angle, in degrees, and the vertical axis is the beam power gain, in dBi, using Figure 5In the scenario shown, the BS maximum transmit power is set to 30dBm, the channel gain per unit distance is set to -50dBm, and the CRLB thresholds are -40dB and -50dB. As can be seen from the figure, the traditional maximum-ratio transmission (MRT) method concentrates the beam in the direction of the VUEs and ignores the direction of the perceived target. However, the present invention radiates beam power toward both the VUEs and the perceived target. Furthermore, the smaller the CRLB threshold, the more concentrated the beam is toward the perceived target.

[0153] Figure 7 In the figure, the horizontal axis is the base station transmission power, which is 25, 27, 29, 31, 33, 35 in sequence, in dBm, and the vertical axis is the communication rate, in bit / s / Hz, from Figure 7 It is observed that in the cases where the number of VUEs K = 2 and K = 4, the communication sum rate increases with the increase of power, and the more the number of VUEs, the greater the sum rate, which is consistent with Figure 6 Combined with the observation, it can be obtained that the present invention maximizes the user's communication and rate while ensuring the perception of the target, thereby improving the effectiveness of communication and the reliability of perception.

[0154] In the present invention, the terms "first", "second", "third", etc. are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. In the description, the directions or positional relationships indicated by "upper", "lower", "left", "right", "front", and "back" are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention, and are not intended to indicate or imply that the device referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they cannot be understood as limiting the scope of protection of the present invention. For those skilled in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0155] In this application, unless otherwise specified, "plurality" refers to two or more. "And / or" describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. The character " / " generally indicates that the related objects are in an "or" relationship.

[0156] The above is only a specific implementation of the present invention, but the design concept of the present invention is not limited to this. Any non-substantial changes to the present invention using this concept shall be deemed as an infringement of the protection scope of the present invention.

Claims

1. A novel communication-aware symbiotic digital beam design method, characterized by: The steps include: Step S1: Constructing an integrated IoV communication and perception scenario, and establishing a communication signal model and a perception signal model for the IoV system based on the scenario. The scenario includes a base station, multiple vehicle users equipped only with receiving antennas, and perception targets. Parameters of the perception targets and vehicle users are estimated using the communication signals sent by the base station. The base station's perception results of the vehicle users are used to assist in beam alignment. Step S2: Based on the communication signal model and the perception signal model established in step S1, CRLB is used as a measure of perception performance. A beamforming matrix optimization problem is constructed under the CRLB constraint of target angle estimation to maximize the communication rate between the base station and the vehicle user while meeting the perception accuracy requirement. The optimization problem is transformed using a fractional programming method and simplified according to the Schur complement condition to obtain the simplified optimization problem: Ω≥0 Among them, α and β are auxiliary variables, W represents the beamforming matrix, Ω is the auxiliary semi-positive matrix, F ξ is the auxiliary matrix, in J ξ is the Fisher information matrix of the estimated vector ξ, η0 represents the threshold of the two-dimensional arrival angle estimation CRLB, F ΘΘ =F ξ (1:2; 1:2), F ΘΨ =F ξ (1:2; 3:5), F ΨΨ =F ξ (3:5; 3:5), P represents the maximum transmission power of the base station, Indicates taking the real part of a complex number, β=[β1,…,β K ] T , h k represents the baseband equivalent channel from the base station to the kth vehicle user, which is set as a flat fading channel model consisting of a large-scale fading component and a small-scale fading component. is the variance of the additive white Gaussian noise in the channel, |·| represents the absolute value, diag(·) represents the diagonal operation of the matrix, and ∥·∥ represents the Frobenius norm of the matrix; Step S3: Use the penalty dual decomposition algorithm to solve the simplified optimization problem obtained in step S2, introduce the augmented Lagrangian function, fix the beamforming matrix W, and solve the optimal solution of the auxiliary variables α and β based on the alternating optimization method, and continuously update the auxiliary variables α and β, as well as the auxiliary matrices Ω and F. ξ ; Step S4: Based on the auxiliary variables α and β obtained in step S3, and the auxiliary matrices Ω and F ξ , simplify the augmented Lagrangian function, alternately perform the gradient ascent algorithm and projection operation, and iteratively calculate the objective function of the simplified augmented Lagrangian function to converge; Step S5: Determine the communication sum rate r in step S2 k Is it less than the convergence accuracy threshold, or has the maximum number of iterations been reached? If so, the iteration ends and the optimized beamforming matrix W is output. Otherwise, execute steps S3 and S4 to iterate.

2. The novel communication-aware symbiotic digital beam design method according to claim 1, characterized in that: The step S1 specifically includes the following steps: Step S11: Construct a vehicle network communication and perception integration scenario including a base station, K vehicle users and a perception node. The base station is a full-duplex communication and perception integration base station with M antennas forming a linear array. The passive perception node has N h ×N v Uniform planar antenna, vehicle users use a single antenna; Step S12: Establish a communication signal model: assign a beam to each vehicle user, and the base station's transmission signal is expressed as The received signal of the kth vehicle user is expressed as Where W=[w1,w2,…,w k ] is the beamforming matrix, s k (t) is the transmission data of vehicle user k at time slot t, h k represents the baseband equivalent channel from the base station to the kth vehicle user, represents the additive white Gaussian noise in the channel; Step S13: Establish a perception signal model: According to the communication signal model, establish an echo signal model for the perception target reflection: in, Indicates the distance-Doppler signal transmitted by the base station, L represents the length of the transmission signal, H s,k represents the reflection link between the base station and the vehicle user, H s,t represents the reflection link between the base station and the sensing target, N s Represents the noise in the radar return.

3. The novel communication-aware symbiotic digital beam design method according to claim 2, characterized in that: The step S2 specifically includes the following steps: Step S21: Set the vehicle user's received signal to y r =y0+n r , the received signal obeys Gaussian distribution. According to the received signal, according to the CRLB theorem, the Fisher information matrix J of the vector to be estimated ξ is obtained ξ = According to J ξ The CRLB matrix for estimating the target two-dimensional arrival angle is Among them, ξ includes the horizontal angle and vertical angle between the base station and the sensing target, Indicates taking the real part of a complex number, Tr{·} indicates taking the trace of a matrix, Indicates J ξ It is a 5×5 matrix, which is divided into four blocks in the form of 2 rows and 2 columns, and 3 rows and 3 columns, so we get J ΘΘ =J ξ (1:2; 1:2), J ΘΨ =J ξ (1:2; 3:5), J ΨΨ =J ξ (3:5; 3:5); Step S22: construct a beamforming matrix optimization problem under the target CRLB constraint to maximize the communication rate between the base station and the vehicle user. The optimization problem is expressed as: Among them, γ k represents the ratio of the received signal to interference plus noise of VUEs, the subsequent polynomials represent the constraints, W represents the beamforming matrix, Then it is its feasible region, maxR sum represents the maximum communication sum rate under the beamforming matrix, and η0 represents the threshold of the two-dimensional arrival angle estimation CRLB; Step S23: Apply the fractional programming method to transform the optimization problem constructed in step S22, and obtain the transformed optimization problem as follows: Step S24: Introduce auxiliary positive semidefinite matrix and the auxiliary matrix F ξ , the optimization problem converted in step S23 is simplified according to the Schur complement condition, and the simplified optimization problem is obtained as follows: Ω≥0 in,{·} -1 Indicates taking the inverse of a matrix.

4. The novel communication-aware symbiotic digital beam design method according to claim 3 is characterized by: The step S3 specifically includes the following steps: Step S31: Based on the penalty dual decomposition algorithm framework, define the augmented Lagrangian function: Ω≥0 Among them, ρ1 is the penalty parameter, Z represents the equation F ξ =J ξ (W) constraint-related dual variables; Step S32: Fixed variables Ω, Z, F ξ ,W, solve the optimal solution of auxiliary variables α and β, and transform the objective function r k (α, β, W) is derived with respect to the auxiliary variables α and β, that is The optimal solutions of auxiliary variables α and β are as follows: in, represents the set of all vehicle users; Step S33: Based on the optimal solution of auxiliary variables α and β obtained in step S32, fix variables Z and W, further simplify the simplified optimization problem into a semi-positive definite programming problem, and use the interior point method to solve the auxiliary matrices Ω and F. ξ : Ω≥0.

5. The novel communication-aware symbiotic digital beam design method according to claim 4 is characterized by: The step S4 specifically includes the following steps: Step S41: Auxiliary variables α and β obtained in step S3, and auxiliary matrices Ω and F ξ , bring in the solution J ξ (W), the augmented Lagrangian function in step S31 can be simplified to: Among them, A l,q express The element in row l and column q, Representation matrix The element in row l and column q; Step S42: Use the gradient ascent projection method to continuously update the iterative beamforming matrix W through gradient ascent: in, represents the beamforming matrix W at the i-th iteration, λ represents the iteration step size, The symbol represents the calculation of matrix gradient; Step S43: Based on the solution of step S42, ensure that the beamforming matrix W satisfies the transmit power constraint condition ∥W∥ 2 ≤P, project the calculated beamforming matrix W into the feasible region: in, It is a projection operation, and the calculation method is Step S44: Iterate step S42 and step S43 until the simplified augmented Lagrange function converges to obtain the beamforming matrix W, and express the degree of constraint violation: Among them, n represents the number of outer layer iterations, ∥·∥ ∞ Represents the infinite norm of the matrix, updates ρ1 and Z according to the degree of constraint violation, and iterates the penalty parameters through convergence to ensure that the constraints are satisfied and the penalty parameters will drop to zero.

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