Communication sensing integrated beamforming method and system for suppressing distance-angle sidelobe

Abstract

The invention provides a communication perception integrated beamforming method and system for suppressing distance-angle side lobes, and the method comprises the steps: enabling a base station to communicate with a plurality of users under a digital beamforming ISAC architecture, and carrying out the detection of an air target in a self-transmitting and self-receiving mode at the same time; determining a sensing channel model and a communication channel model to obtain a communication receiving signal at each user position and a sensing receiving signal scattered back to the base station from the target; calculating to obtain the SINR of each user; a distance-angle-Doppler three-dimensional perception fuzzy function based on a digital beam forming ISAC architecture is obtained; extracting an expression of an integral sidelobe of a distance-angle dimension; according to the communication index, the sensing index and the integral sidelobe value in the preset distance-angle sidelobe area, a beam forming matrix of the ISAC system is designed in a joint optimization mode; auxiliary variables are introduced to convert the problem of optimizing the beam forming matrix into the problem of optimizing a covariance matrix of the beam forming matrix, and a feasible solution of the joint design problem is obtained.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a communication-aware integrated beamforming method and system for suppressing range-angle side lobes. Background Art

[0002] Future wireless networks need to not only provide superior wireless connectivity, but also have high-precision and robust perception capabilities to support emerging applications such as autonomous driving and extended reality. To achieve these goals, integrated communications and perception (ISAC) has become an important research direction.

[0003] In the ISAC system, waveform design is one of the key technologies. Early research focused on the design of waveforms in the time-frequency domain, using two main approaches: one is to improve existing waveforms to achieve both communication and perception functions; the other is to use the inherent perception potential of communication signals to perform perception tasks. Such signals usually contain pilots and data, such as constant modulus phase modulation and orthogonal frequency division multiplexing technology.

[0004] In the spatial domain, beamforming has become an advanced and effective means of ISAC waveform design. By adjusting the weighting factors of the antenna array to adjust the directivity of the transmitted signal, existing studies have proposed a variety of beamforming design methods to achieve a balance between perception and communication performance. However, most of the existing ISAC waveform design studies, whether based on the time-frequency domain or the spatial domain, mainly focus on analyzing the second-order statistical characteristics of the observation data containing the target position information, such as the covariance matrix and the Fisher information matrix, to improve the accuracy of parameter estimation or signal processing gain. For example, the beamforming design is optimized by minimizing the Cramer-Rao bound (CRB) to improve the estimation accuracy of the target parameters, or the signal-to-interference-plus-noise ratio (SINR) of the sensing receiver is maximized through beamforming technology to improve the signal detectability in interference and noise environments.

[0005] Despite the progress, existing beamforming design metrics mainly evaluate the performance of the system at the target location, while giving limited consideration to the system response at non-target locations. In contrast, the ambiguity function, as a measure of signal response in the delay-Doppler domain, can fully describe the signal characteristics, including the main peak at the target location and the sidelobes at non-target locations. Reducing the sidelobe level can improve the detection capability of weak targets, suppress sidelobe interference, and enhance the environmental reconstruction effect. However, few studies have exploited the spatial degrees of freedom (DoFs) brought by antenna arrays to achieve effective sidelobe suppression. Even though a recent study proposed a symbol-level beamforming method based on autocorrelation function to reduce range sidelobes, the method has high computational complexity and sacrifices communication rate due to constant modulus constraints.

[0006] Therefore, the present invention proposes a novel communication-perception integrated beamforming method, which aims to utilize the existing communication system to perform perception tasks and achieve low ambiguity function sidelobes in the distance-angle dimension, thereby improving the perception performance of the entire system. Summary of the invention

[0007] In view of the defects in the prior art, an object of the present invention is to provide a communication-aware integrated beamforming method and system for suppressing range-angle sidelobes.

[0008] A communication-aware integrated beamforming method for suppressing range-angle side lobes provided by the present invention comprises:

[0009] Step S1: The base station communicates with multiple users under the digital beamforming ISAC architecture and detects aerial targets in a self-transmitting and self-receiving mode;

[0010] Step S2: determining a perception channel model and a communication channel model, obtaining a communication reception signal at each user location and a perception reception signal scattered from the target back to the base station;

[0011] Step S3: Based on the communication reception signal at each user's position, the SINR of each user is calculated through the communication user's reception signal model; based on the perceived reception signal scattered back to the base station from the target, the distance-angle-Doppler three-dimensional perception ambiguity function based on the digital beamforming ISAC architecture is obtained through the perception reception signal model;

[0012] Step S4: Based on the distance-angle-Doppler three-dimensional perception ambiguity function, the main lobe and side lobe structure of the ambiguity function are analyzed, and the expression of the integral side lobe in the distance-angle dimension is extracted;

[0013] Step S5: According to the communication index and the perception index, the beamforming matrix of the ISAC system is jointly optimized and designed based on minimizing the integrated sidelobe value of the range-angle-Doppler three-dimensional perception ambiguity function in the preset range-angle sidelobe area;

[0014] Step S6: Auxiliary variables are introduced to transform the problem of optimizing the beamforming matrix into the problem of optimizing the covariance matrix of the beamforming matrix, so as to obtain a feasible solution to the joint design problem.

[0015] Preferably, the communication reception signal at each user location includes:

[0016]

[0017] Among them, z k (t) represents the additive Gaussian white noise of the received signal of the kth communication user at time t, with a corresponding mean of 0 and a variance of represents the communication channel model; w k represents the precoding vector of the kth user's transmitted signal, s k represents the signal transmitted by the kth user;

[0018] The sensed received signal scattered from the target back to the base station includes:

[0019]

[0020] Where M represents the number of transmitting antennas, a m,n represents the complex scattering coefficient between the mth transmitting antenna and the nth receiving antenna, and represents the attenuation and phase change of the signal during transmission. Indicates the Doppler frequency of a uniformly moving target, f c represents the carrier frequency of the transmitted signal, λ is the corresponding wavelength, represents the signal transmitted by the mth antenna, t represents the time t, τ m,n represents the path delay between the transmitting array element and the receiving array element pair (m,n), r0 represents the distance of the target, θ0 represents the angle of the target, and v0 represents the speed of the target; represents an additive white Gaussian noise signal.

[0021] Preferably, the step of calculating the SINR of each user based on the communication reception signal at each user position by using the reception signal model of the communication user includes:

[0022]

[0023] in, represents the channel model of the kth communication user, w k represents the precoding vector of the kth user's transmitted signal, w n represents the precoding vector of the nth user's transmitted signal, represents the variance of the noise in the signal received by the kth user.

[0024] Preferably, the sensing received signal scattered from the target back to the base station is used to obtain a distance-angle-Doppler three-dimensional sensing ambiguity function based on the digital beamforming ISAC architecture through a sensing received signal model, including:

[0025]

[0026] in, represents the phase caused by the target parameter, represents the received steering vector, the matrix X(Δr, Δf d) represents the two-dimensional ambiguity function matrix in the range-Doppler domain, whose dimension is K rows and K columns, where the diagonal elements correspond to the self-ambiguity function of each user's transmitted signal, and the non-diagonal elements represent the cross-ambiguity function between different users; the element in the kth row and ith column of the matrix X is:

[0027]

[0028] Among them, b T (f,θ) represents the transmission steering vector with respect to frequency f and angle θ, W = [w1 w2 ... w K ] represents the precoding matrix of the transmitted signal.

[0029] Preferably, the expression of the integrated sidelobe in the distance-angle dimension includes:

[0030]

[0031] Where W and D represent the set of angle sidelobe area and distance sidelobe area to be suppressed respectively; represents the ambiguity function of the transmitted signals of K users in the range-angle domain, θ0 represents the angle of target 1, θ1 represents the angle of target 2, and Δr represents the distance difference between target 1 and target 2.

[0032] Preferably, the step S5 comprises:

[0033]

[0034] P s G s .

[0035]

[0036] in, represents the submatrix consisting of the L1th row to L2th row and the L3th row to L4th column of the matrix ·; P t Indicates the total transmission power, G c is the minimum SINR constraint for each communicating user, G s is the beamforming gain constraint toward the sensing target; Indicates the frequency and the angle is The launch steering vector at .

[0037] Preferably, step S6 comprises:

[0038] Introducing the auxiliary matrix Among them, R k ±0 and rank(R k )=1;

[0039] Therefore, the objective function can be rewritten as follows:

[0040]

[0041] in, k represents the region {(k-1)L+1:kL,(k-1)L+1:kL} of the submatrix taken from the matrix [Y⊙M];

[0042] The constraints are transformed into R k Related, as follows:

[0043]

[0044] Among them, Tr(R k ) represents the matrix R k The trace of R k represents the covariance matrix of the precoding vector of the kth user, R W represents the sum of the covariance matrices of the precoding vectors of K users;

[0045] Without loss of generality, we choose w k , so that is non-negative for any user k; therefore, the gain constraint in the direction of the perceived target is written as follows

[0046]

[0047] Therefore, the optimization problem is rewritten into a new optimization problem as follows and solved efficiently using the SDR technique;

[0048]

[0049] R k ±0,k=1,...K,

[0050]

[0051] A communication sensing integrated beamforming system for suppressing range-angle side lobes provided by the present invention comprises:

[0052] Module M1: The base station communicates with multiple users under the digital beamforming ISAC architecture and detects aerial targets in a self-transmitting and self-receiving mode;

[0053] Module M2: Determine the perception channel model and the communication channel model, obtain the communication reception signal at each user location and the perception reception signal scattered from the target back to the base station;

[0054] Module M3: Based on the communication reception signal at each user's location, the SINR of each user is calculated through the communication user's reception signal model; based on the perceived reception signal scattered from the target back to the base station, the distance-angle-Doppler three-dimensional perception ambiguity function based on the digital beamforming ISAC architecture is obtained through the perception reception signal model;

[0055] Module M4: Based on the distance-angle-Doppler three-dimensional perception ambiguity function, analyze the main lobe and side lobe structure of the ambiguity function, and extract the expression of the integral side lobe in the distance-angle dimension;

[0056] Module M5: Based on the communication index and the perception index, the beamforming matrix of the ISAC system is jointly optimized and designed based on minimizing the integrated sidelobe value of the range-angle-Doppler three-dimensional perception ambiguity function in the preset range-angle sidelobe area;

[0057] Module M6: Auxiliary variables are introduced to transform the problem of optimizing the beamforming matrix into the problem of optimizing the covariance matrix of the beamforming matrix, and a feasible solution to the joint design problem is obtained.

[0058] Preferably, the communication reception signal at each user location includes:

[0059]

[0060] Among them, z k (t) represents the additive Gaussian white noise of the received signal of the kth communication user at time t, with a corresponding mean of 0 and a variance of represents the communication channel model; w k represents the precoding vector of the kth user's transmitted signal, s k represents the signal transmitted by the kth user;

[0061] The sensed received signal scattered from the target back to the base station includes:

[0062]

[0063] Where M represents the number of transmitting antennas, a m,n represents the complex scattering coefficient between the mth transmitting antenna and the nth receiving antenna, and represents the attenuation and phase change of the signal during transmission. Indicates the Doppler frequency of a uniformly moving target, f c represents the carrier frequency of the transmitted signal, λ is the corresponding wavelength, represents the signal transmitted by the mth antenna, t represents the time t, τ m,n represents the path delay between the transmitting array element and the receiving array element pair (m,n), r0 represents the distance of the target, θ0 represents the angle of the target, and v0 represents the speed of the target; represents additive white Gaussian noise signal;

[0064] The method of calculating the SINR of each user based on the communication reception signal at each user position by using the reception signal model of the communication user includes:

[0065]

[0066] in, represents the channel model of the kth communication user, w k represents the precoding vector of the kth user's transmitted signal, w n represents the precoding vector of the nth user's transmitted signal, represents the variance of the noise in the signal received by the kth user.

[0067] Preferably, the sensing received signal scattered from the target back to the base station is used to obtain a distance-angle-Doppler three-dimensional sensing ambiguity function based on the digital beamforming ISAC architecture through a sensing received signal model, including:

[0068]

[0069] in, represents the phase caused by the target parameter, represents the received steering vector, the matrix X(Δr, Δf d ) represents the two-dimensional ambiguity function matrix in the range-Doppler domain, whose dimension is K rows and K columns, where the diagonal elements correspond to the self-ambiguity function of each user's transmitted signal, and the non-diagonal elements represent the cross-ambiguity function between different users; the element in the kth row and ith column of the matrix X is:

[0070]

[0071] Among them, b T (f,θ) represents the transmission steering vector with respect to frequency f and angle θ, W = [w1 w2 ... w K ] represents the precoding matrix of the transmitted signal;

[0072] The expression of the integrated sidelobe in the distance-angle dimension includes:

[0073]

[0074] Where W and D represent the set of angle sidelobe area and distance sidelobe area to be suppressed respectively; represents the ambiguity function of the transmitted signals of K users in the range-angle domain, θ0 represents the angle of target 1, θ1 represents the angle of target 2, and Δr represents the distance difference between target 1 and target 2;

[0075] The module M5 comprises:

[0076]

[0077] P s =G s .

[0078]

[0079] in, represents the submatrix consisting of the L1th row to L2th row and the L3th row to L4th column of the matrix ·; P t Indicates the total transmission power, G c is the minimum SINR constraint for each communicating user, G s is the beamforming gain constraint toward the sensing target; Indicates the frequency and the angle is The launch steering vector at ;

[0080] The module M6 comprises:

[0081] Introducing the auxiliary matrix Among them, R k ±0 and rank(R k )=1;

[0082] Therefore, the objective function can be rewritten as follows:

[0083]

[0084] in, k represents the region {(k-1)L+1:kL,(k-1)L+1:kL} of the submatrix taken from the matrix [Y⊙M];

[0085] The constraints are transformed into R k Related, as follows:

[0086]

[0087] Among them, Tr(R k ) represents the matrix R k The trace of R k represents the covariance matrix of the precoding vector of the kth user, R W represents the sum of the covariance matrices of the precoding vectors of K users;

[0088] Without loss of generality, we choose w k , so that is non-negative for any user k; therefore, the gain constraint in the direction of the perceived target is written as follows

[0089]

[0090] Therefore, the optimization problem is rewritten into a new optimization problem as follows and solved efficiently using the SDR technique;

[0091]

[0092] R k ±0,k=1,...K,

[0093]

[0094] Compared with the prior art, the present invention has the following beneficial effects:

[0095] 1. The present invention utilizes the existing communication system to perform the perception task. Under the premise of meeting the total transmission power limit, the user signal-to-interference-plus-noise ratio (SINR) constraint and the target direction beam gain requirement, the beamforming matrix is ​​optimized to minimize the side lobes of the system perception ambiguity function in the distance-angle dimension, thereby improving the system's perception performance while maintaining excellent communication performance;

[0096] 2. The present invention constructs a joint optimization problem of communication and perception, aiming to minimize the ISL of the perception ambiguity function under the premise of satisfying the constraints of transmission power, communication SINR, and perception gain pointing to the target direction, so as to maintain the communication quality while improving the system perception performance;

[0097] 3. In order to solve the non-convexity problem in the optimization process, the present invention adopts the SDR method for effective solution;

[0098] 4. The present invention can significantly reduce the sidelobe level of the perception ambiguity function in the distance-angle domain, achieve a lower ISLR, effectively improve the perception performance and spectrum efficiency of the system, and provide guidance for beamforming design in various application scenarios such as sidelobe weak target detection in multi-target scenarios and reduction of false alarm probability in single target detection;

[0099] 5. Under the premise of meeting communication constraints, the present invention significantly reduces the integrated sidelobe level of the ambiguity function by minimizing the ISL of the perception ambiguity function; compared with other beamforming schemes, the present invention significantly reduces the sidelobe of the ambiguity function in the distance-angle dimension at the non-target position when the system detects the target, providing guidance for the detection of sidelobe weak targets in multi-target scenarios and the reduction of the false alarm probability during single target detection;

[0100] 6. When performing the joint optimization design of communication and perception, the present invention designs a beamforming design scheme to control the response of the control system at non-target positions as low as possible, thereby maintaining stable communication performance while minimizing the integrated sidelobe level of the perception ambiguity function in the distance-angle domain. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0102] Figure 1 Schematic diagram of communication-aware integrated beamforming in a multi-user scenario.

[0103] Figure 2 The present invention is a flow chart of a beamforming method for a communication sensing integrated system for suppressing range-angle sidelobes.

[0104] Figure 3 Schematic diagram of the comparison of the distance-angle dimension perception ambiguity function under the beamforming matrix designed for a communication perception integrated system for suppressing distance-angle sidelobes and other schemes.

[0105] Figure 4 A schematic diagram comparing the profiles of the distance and angle dimensions of the distance-angle dimension perception ambiguity function obtained by a beamforming method for a communication perception integrated system for suppressing distance-angle sidelobes and the beamforming matrix designed for other schemes.

[0106] Figure 5 Schematic diagram comparing the integrated sidelobe level of the perception ambiguity function in the distance-angle dimension obtained by a communication-perception integrated system beamforming method for suppressing distance-angle sidelobes and other different design schemes as the communication user's signal-to-interference-noise ratio changes.

[0107] Figure 6 A beamforming method for a communication-aware integrated system for suppressing range-angle sidelobes and beam pattern diagrams obtained from other different design schemes. DETAILED DESCRIPTION

[0108] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0109] Example 1

[0110] According to the present invention, a communication sensing integrated beamforming method for suppressing range-angle side lobes is provided, such as Figures 1 to 2 As shown, including:

[0111] A communication-aware integrated beamforming method for suppressing range-angle side lobes provided by the present invention comprises:

[0112] Step S1: The base station communicates with multiple users under the digital beamforming ISAC architecture and detects aerial targets in a self-transmitting and self-receiving mode;

[0113] Step S2: determining a perception channel model and a communication channel model, obtaining a communication reception signal at each user location and a perception reception signal scattered from the target back to the base station;

[0114] Step S3: Based on the communication reception signal at each user's position, the SINR of each user is calculated through the communication user's reception signal model; based on the perceived reception signal scattered back to the base station from the target, the distance-angle-Doppler three-dimensional perception ambiguity function based on the digital beamforming ISAC architecture is obtained through the perception reception signal model;

[0115] Step S4: Based on the distance-angle-Doppler three-dimensional perception ambiguity function, the main lobe and side lobe structure of the ambiguity function are analyzed, and the expression of the integral side lobe in the distance-angle dimension is extracted;

[0116] Step S5: According to the communication index and the perception index, the beamforming matrix of the ISAC system is jointly optimized and designed based on minimizing the integrated sidelobe value of the range-angle-Doppler three-dimensional perception ambiguity function in the preset range-angle sidelobe area;

[0117] Step S6: Auxiliary variables are introduced to transform the problem of optimizing the beamforming matrix into the problem of optimizing the covariance matrix of the beamforming matrix, so as to obtain a feasible solution to the joint design problem.

[0118] Specifically, the communication reception signal at each user location includes:

[0119]

[0120] Among them, z k (t) represents the additive Gaussian white noise of the received signal of the kth communication user at time t, with a corresponding mean of 0 and a variance of represents the communication channel model; w k represents the precoding vector of the kth user's transmitted signal, s k represents the signal transmitted by the kth user;

[0121] The sensed received signal scattered from the target back to the base station includes:

[0122]

[0123] Where M represents the number of transmitting antennas, a m,n represents the complex scattering coefficient between the mth transmitting antenna and the nth receiving antenna, and represents the attenuation and phase change of the signal during transmission. Indicates the Doppler frequency of a uniformly moving target, f c represents the carrier frequency of the transmitted signal, λ is the corresponding wavelength, represents the signal transmitted by the mth antenna, t represents the time t, τ m,n represents the path delay between the transmitting array element and the receiving array element pair (m,n), r0 represents the distance of the target, θ0 represents the angle of the target, and v0 represents the speed of the target; represents an additive white Gaussian noise signal.

[0124] Specifically, the SINR of each user is calculated based on the communication reception signal at each user position through the reception signal model of the communication user, including:

[0125]

[0126] in, represents the channel model of the kth communication user, w k represents the precoding vector of the kth user's transmitted signal, w n represents the precoding vector of the nth user's transmitted signal, represents the variance of the noise in the signal received by the kth user.

[0127] Specifically, the sensing received signal scattered from the target back to the base station is used to obtain a distance-angle-Doppler three-dimensional sensing ambiguity function based on the digital beamforming ISAC architecture through a sensing received signal model, including:

[0128]

[0129] in, represents the phase caused by the target parameter, represents the received steering vector, the matrix X(Δr, Δf d ) represents the two-dimensional ambiguity function matrix in the range-Doppler domain, whose dimension is K rows and K columns, where the diagonal elements correspond to the self-ambiguity function of each user's transmitted signal, and the non-diagonal elements represent the cross-ambiguity function between different users; the element in the kth row and ith column of the matrix X is:

[0130]

[0131] Among them, b T (f,θ) represents the transmission steering vector with respect to frequency f and angle θ, W = [w1 w2 ... w K ] represents the precoding matrix of the transmitted signal.

[0132] Specifically, the expression of the integrated sidelobe in the distance-angle dimension includes:

[0133]

[0134] Where W and D represent the set of angle sidelobe area and distance sidelobe area to be suppressed respectively; represents the ambiguity function of the transmitted signals of K users in the range-angle domain, θ0 represents the angle of target 1, θ1 represents the angle of target 2, and Δr represents the distance difference between target 1 and target 2.

[0135] Specifically, step S5 includes:

[0136]

[0137] P s =G s .

[0138]

[0139] in, represents the submatrix consisting of the L1th row to L2th row and the L3th row to L4th column of the matrix ·; P t Indicates the total transmission power, G c is the minimum SINR constraint for each communicating user, G s is the beamforming gain constraint toward the sensing target; Indicates the frequency and the angle is The launch steering vector at .

[0140] Specifically, step S6 includes:

[0141] Introducing the auxiliary matrix Among them, R k ≥0 and rank(R k )=1;

[0142] Therefore, the objective function can be rewritten as follows:

[0143]

[0144] in, k represents the region {(k-1)L+1:kL,(k-1)L+1:kL} of the submatrix taken from the matrix [Y⊙M];

[0145] The constraints are transformed into R k Related, as follows:

[0146]

[0147] Among them, Tr(R k ) represents the matrix R k The trace of R k represents the covariance matrix of the precoding vector of the kth user, R W represents the sum of the covariance matrices of the precoding vectors of K users;

[0148] Without loss of generality, we choose w k , so that is non-negative for any user k; therefore, the gain constraint in the direction of the perceived target is written as follows

[0149]

[0150] Therefore, the optimization problem is rewritten into a new optimization problem as follows and solved efficiently using the SDR technique;

[0151]

[0152] R k ±0,k=1...K,

[0153]

[0154] The present invention also provides a communication-aware integrated beamforming system for suppressing distance-angle side lobes. The communication-aware integrated beamforming system for suppressing distance-angle side lobes can be realized by executing the process steps of the communication-aware integrated beamforming method for suppressing distance-angle side lobes, that is, those skilled in the art can understand the communication-aware integrated beamforming method for suppressing distance-angle side lobes as a preferred implementation of the communication-aware integrated beamforming system for suppressing distance-angle side lobes.

[0155] Example 2

[0156] Embodiment 2 is a preferred embodiment of Embodiment 1

[0157] A communication-aware integrated beamforming method for suppressing range-angle side lobes provided by the present invention comprises:

[0158] Consider a single-base station communication sensing integrated system equipped with M antennas, which provides services to K communication users and detects targets at the same time. The target is assumed to be at a distance r0 from the first element of the transmitting array and moves toward the base station at a constant radial velocity ν0 in the direction of angle θ0. Assume s(t)∈C K×1 represents the information symbol to be sent to K communication users at time t. The symbol is transmitted through the beamforming matrix W = [w1, w2, ... w K ]∈C M×K Mapped to M transmitting antennas to form a signal The signal is then used for communication and target detection. At time t, the signal transmitted by the mth antenna is expressed as:

[0159]

[0160] Without loss of generality, consider the signal s k (t), k = 1, ..., K are orthogonal to each other, that is, within the signal duration T,

[0161]

[0162] Next, the SINR of the received signals at different users after the transmitted signal passes through the communication channel is calculated. First, the channel model h of the kth communication user in this embodiment is introduced. k , using the SV channel model, expressed as follows

[0163]

[0164] Among them, g k represents the path loss, L represents the number of multipaths between each user and the BS, represents the complex gain of the lth path, is the launch steering vector, where is the departure angle (DOD) of the lth path from the base station. Assuming that the first path is line-of-sight (Los) and the remaining paths are non-line-of-sight (non-LoS), the corresponding DoDs of the paths are

[0165] Based on the communication channel model in equation (3), the received signal of the kth communication user can be obtained as follows:

[0166]

[0167] Among them, z k (t) represents the additive Gaussian white noise of the received signal of the kth communication user at time t, with a corresponding mean of 0 and a variance of

[0168] Therefore, the signal-to-interference-to-noise ratio received by the kth communication user at time t is as follows:

[0169]

[0170] The signal to noise ratio of each user in equation (4) is As a key indicator for evaluating the communication performance of the system, it is used for the joint optimization design of the subsequent communication perception system.

[0171] To further obtain the perceived received signal after the transmitted signal passes through the perception channel, firstly, the impulse response of the perception channel with a uniform speed target is expanded by Taylor series as shown below:

[0172]

[0173] Among them, δ(t, θ0) represents the delay from the ISAC base station transmitting the signal to the signal being scattered back by the target, and the symbol θ0 = (r0, ν0, θ0) represents the parameter set of the target's distance, speed, and angle. Indicates the Doppler frequency of a uniformly moving target, f c is the carrier frequency of the transmitted signal, λ is the corresponding wavelength, τ m,n represents the path delay between the transmitting array element and the receiving array element pair (m,n).

[0174] Therefore, the expression of the signal scattered back from the target received by the nth receiving antenna of the base station before demodulation can be obtained as:

[0175]

[0176] in, is an additive white Gaussian noise signal, α is the signal between the mth transmitting antenna and the nth receiving antenna

[0177] The complex scattering coefficient of m,n represents the attenuation and phase change of the signal during transmission. The propagation delay τ of the signal m,n (r0,θ0) can be approximated as the sum of the following three parts under the far field assumption:

[0178] τ m,n (r0,θ0)≈τ(r0)+τ T,m (θ0)+τ R,n (θ0),(7)

[0179] Among them, τ(r0) = 2r0 / c, which is the delay of the signal's round-trip propagation, τ T,m (θ0) = mdsinθ0 / c, is the delay caused by the element spacing between the transmitting antennas, τ R,n(θ0) = ndsinθ0 / c is the time delay caused by the spacing between the receiving antennas. The symbol c represents the speed of light.

[0180] After obtaining the perceived received signal scattered by the target back to the base station, the perceived three-dimensional ambiguity function in the range-angle-Doppler domain can be derived based on this for subsequent joint optimization design. Considering that the target is moving slowly or stationary, the following narrowband assumption can be satisfied:

[0181]

[0182] Among them, B k is the bandwidth of the signal, T is the transmission signal s k (t) duration. In this case, you can ignore For the compression effect of the complex envelope of the waveform, we only need to pay attention to the carrier part. Under far-field conditions, τ T,m (θ0)<<τ(r0) and τ R,n (θ0)<<τ(r0), which means that although these two delays will significantly affect the phase of the received signal, their impact on the signal envelope can be ignored. In addition, under the far-field and narrow-band assumptions, the impact of different transmitting array elements and receiving array elements on the target scattering coefficient between channels can be ignored, that is, assuming that all α m,n are all equal to 1.

[0183] Therefore, after the received signal is demodulated to baseband, equation (6) can be restated as follows:

[0184]

[0185] Where f = f c +f ν is the frequency of the signal, which is equal to the carrier frequency plus the frequency change caused by the target motion, b T represents the launch steering vector, which is expressed as follows

[0186]

[0187] In a single-station ISAC system, when the transmitted signal is known, the optimal detector is a matched filter for a specific target parameter. and the kth user signal waveform s k (t) Perform matched filtering and consider the target parameters θ1 = (r1, ν1, θ1) and the known gain The signal component corresponding to the kth transmitted waveform received by the nth antenna can be expressed as

[0188]

[0189] in, The blur function is defined as the coherent sum of all noise-free matched filter output pairs (n, k), where n=1,...,M and k=1,...,K.

[0190] Therefore, the three-dimensional ambiguity function in the range-angle-Doppler domain of the ISAC system based on the digital beamforming architecture can be mathematically expressed as follows:

[0191]

[0192] in, represents the phase caused by the target parameter, represents the receiving steering vector, the matrix represents the two-dimensional ambiguity function in the range-Doppler domain, where the diagonal elements correspond to the self-ambiguity function of each user's transmitted signal, and the off-diagonal elements represent the cross-ambiguity function between different users. The element in the kth row and ith column of the matrix X is:

[0193]

[0194] It can be found that when the transmitted signal is an information symbol, the fuzzy function becomes random, but its expected value It can still be expressed in a similar mathematical form. For convenience, E[c(Q0,Q1)] and c(Θ0,Θ1) are uniformly expressed as the following formula

[0195]

[0196] in, Represents X and

[0197] In order to quantify the sidelobe level of the ambiguity function for subsequent use in communication-aware joint design, the present invention uses the integrated sidelobe level (ISL) of the ambiguity function as a metric. In slow-moving or stationary scenes, special attention is paid to the ISL in the distance-angle domain, which is defined as follows:

[0198]

[0199] Where W and D represent the set of angle sidelobe regions and distance sidelobe regions that are expected to be suppressed, respectively. In addition, in order to achieve accurate perception, the perception receiver must receive a signal with sufficient signal-to-noise ratio. However, due to the complexity of the environment, it is challenging to accurately control the signal-to-noise ratio of the perception reception signal. However, the signal-to-noise ratio of the perception reception signal can be indirectly enhanced by controlling the beam gain pointing in the target direction, thereby ensuring better perception performance. The expression of the beam gain pointing in the target direction is given as follows

[0200]

[0201] Based on the above analysis, the present invention then designs a communication-sensing integrated digital beamforming scheme by combining communication and perception performance indicators, aiming to minimize the ISL of the distance-angle dimension of the ambiguity function of the received signal after the transmitted signal passes through the perception channel in the ISAC system, thereby improving the system sensing performance. The design scheme can also meet the SINR requirements of communication users, the gain constraints on the perception target, and the total transmission power constraints. The established joint optimization problem is as follows:

[0202]

[0203] P s =G s .(17c)

[0204] Among them, G c is the minimum SINR constraint for each communicating user, G s is the beamforming gain constraint toward the sensing target. For the convenience of numerical calculation, Df d and Dr are discretized into L points respectively. Transform to matrix Each column represents L distance samples when the Pulser frequency value is fixed. At the same time, the angle sidelobe q1 to be optimized is discretized into an angle sidelobe region set Therefore, the objective function (17) can be reformulated as

[0205]

[0206] in, is a masking matrix, which is used to retain the sidelobe area that needs to be optimized and set the sidelobe area that does not need to be optimized to zero. Considering the orthogonality between the signals transmitted to different users, the mutual ambiguity function of different users in the range-Doppler domain is almost 0, so equation (16) can be further simplified to the following equation:

[0207]

[0208] in, represents the submatrix consisting of the L1th row to L2th row and the L3th row to L4th column of the matrix .

[0209] Substituting equation (17) into the optimization problem (P1), it can be found that the problem is still non-convex and difficult to solve. Next, the present invention uses the SDR method to transform the aforementioned non-convex problem into a convex optimization problem, and then effectively solves it. First, the auxiliary matrix is ​​introduced Where R k ±0 and ran k(R k )=1, so the objective function can be rewritten as follows:

[0210]

[0211] in, k represents the region of submatrices {k(-1)L+1:kL, (k-1)L+1:kL} taken from the matrix [Y⊙M]. The constraints can also be transformed into the same as R k Related to the following

[0212]

[0213] Considering w in (17c) k is a feasible solution to problem (P1), then w k Arbitrary phase rotation of is also possible. Without loss of generality, we choose w k , so that For any user k, it is non-negative. Therefore, the gain constraint in the direction of the perceived target can be written as follows:

[0214]

[0215] Therefore, the optimization problem (P1) can be rewritten as a new optimization problem as follows and solved efficiently using SDR technology:

[0216]

[0217] st(21),(22),(23),(24a)

[0218] R k ±0, k=1,...K,(24b)

[0219]

[0220] It is worth noting that in the above optimization problem (P2), in order to make all constraints convex, R is ignored. k is a rank-one constraint. Thus, (P2) becomes a standard convex optimization problem that can be solved efficiently using tools such as CVX. If the optimal solution R of (P2) is k If the rank of is greater than one, then rank reduction techniques such as eigenvalue decomposition or Gaussian randomization can be used to obtain a feasible solution w k .

[0221] Finally, through simulation verification, it can be found that when the beamforming scheme proposed in the present invention is used for system design, the suppression effect on the distance-angle sidelobe of the perception ambiguity function is significant, which is significantly better than other beamforming schemes.

[0222] like Figure 3As shown in the figure, the suppression effect of the proposed beamforming design scheme on the distance-angle sidelobe of the perception ambiguity function in the multi-user multi-antenna ISAC scenario is demonstrated, and the perception performance of other beamforming methods is compared, where (a) corresponds to the proposed beamforming scheme, (b) and (c) represent the beamforming design schemes that only consider the perception performance and only consider the communication performance, respectively, and (d) to (f) correspond to beamforming joint design 1 to joint design 3. Figure 4 Shown Figure 3 The results show that the proposed design can effectively reduce the sidelobe level of the perceived ambiguity function in the entire range-angle domain. Compared with designs (c)-(f), the proposed scheme achieves a maximum sidelobe reduction effect of about 33dB in the range dimension slice, and is close to the performance of design (b), where design (b) uses all resources for perception. In the angle dimension slice, compared with designs (e) and (f), the proposed design significantly reduces the sidelobe level of the ambiguity function in the optimized angle region, which is about 0.5dB lower than design (d).

[0223] Figure 5 The comparison of ISLR with communication SINR in the specified optimization area under different perception beam gain constraints for different design criteria is shown. The results show that under the same communication SINR conditions, the proposed method can reduce ISLR by about 3 to 15dB compared with other methods. In addition, as the beam gain in the target direction increases and the communication SINR improves, the ISLR of the proposed method remains basically unchanged, indicating that it can maintain stable perception performance without significantly affecting the communication quality.

[0224] However, it is worth noting that Figure 6 As shown, compared with other methods, the beamforming scheme in the present invention shows a slightly higher power level in the non-user direction, which may introduce potential interference to the system, and measures can be taken to mitigate such interference.

[0225] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0226] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A communication-aware integrated beamforming method for suppressing range-angle sidelobes, characterized in that: include: Step S1: The base station communicates with multiple users under the digital beamforming ISAC architecture and detects aerial targets in a self-transmitting and self-receiving mode; Step S2: determining a perception channel model and a communication channel model, obtaining a communication reception signal at each user location and a perception reception signal scattered from the target back to the base station; Step S3: Based on the communication reception signal at each user's position, the SINR of each user is calculated through the communication user's reception signal model; based on the perceived reception signal scattered back to the base station from the target, the distance-angle-Doppler three-dimensional perception ambiguity function based on the digital beamforming ISAC architecture is obtained through the perception reception signal model; Step S4: Based on the distance-angle-Doppler three-dimensional perception ambiguity function, the main lobe and side lobe structure of the ambiguity function are analyzed, and the expression of the integral side lobe in the distance-angle dimension is extracted; Step S5: According to the communication index and the perception index, the beamforming matrix of the ISAC system is jointly optimized and designed based on minimizing the integrated sidelobe value of the range-angle-Doppler three-dimensional perception ambiguity function in the preset range-angle sidelobe area; Step S6: Auxiliary variables are introduced to transform the problem of optimizing the beamforming matrix into the problem of optimizing the covariance matrix of the beamforming matrix, so as to obtain a feasible solution to the joint design problem.

2. The communication-aware integrated beamforming method for suppressing range-angle sidelobes according to claim 1, characterized in that: The communication reception signal at each user location includes: Among them, z k (t) represents the additive Gaussian white noise of the received signal of the kth communication user at time t, with a corresponding mean of 0 and a variance of represents the communication channel model; w k represents the precoding vector of the kth user's transmitted signal, s k represents the signal transmitted by the kth user; The sensed received signal scattered from the target back to the base station includes: Where M represents the number of transmitting antennas, a m,n represents the complex scattering coefficient between the mth transmitting antenna and the nth receiving antenna, and represents the attenuation and phase change of the signal during transmission. Indicates the Doppler frequency of a uniformly moving target, f c represents the carrier frequency of the transmitted signal, λ is the corresponding wavelength, represents the signal transmitted by the mth antenna, t represents the time t, τ m,n represents the path delay between the transmitting array element and the receiving array element pair (m,n), r0 represents the distance of the target, θ0 represents the angle of the target, and v0 represents the speed of the target; represents an additive white Gaussian noise signal.

3. The communication-aware integrated beamforming method for suppressing range-angle sidelobes according to claim 1, characterized in that: The method of calculating the SINR of each user based on the communication reception signal at each user position by using the reception signal model of the communication user includes: in, represents the channel model of the kth communication user, w k represents the precoding vector of the kth user's transmitted signal, w n represents the precoding vector of the nth user's transmitted signal, represents the variance of the noise in the signal received by the kth user.

4. The communication-aware integrated beamforming method for suppressing range-angle sidelobes according to claim 1, characterized in that: The method of obtaining a distance-angle-Doppler three-dimensional perception ambiguity function based on a digital beamforming ISAC architecture through a perception reception signal model based on the perception reception signal scattered from the target back to the base station includes: in, represents the phase caused by the target parameter, represents the received steering vector, the matrix X(Δr,Δf d ) represents the two-dimensional ambiguity function matrix in the range-Doppler domain, whose dimension is K rows and K columns, where the diagonal elements correspond to the self-ambiguity function of each user's transmitted signal, and the non-diagonal elements represent the cross-ambiguity function between different users; the element in the kth row and ith column of the matrix X is: Among them, b T (f,θ) represents the transmission steering vector with respect to frequency f and angle θ, W = [w1 w2 ... w K ] represents the precoding matrix of the transmitted signal.

5. The communication-aware integrated beamforming method for suppressing range-angle sidelobes according to claim 1, characterized in that: The expression of the integrated sidelobe in the distance-angle dimension includes: Where W and D represent the set of angle sidelobe area and distance sidelobe area to be suppressed respectively; represents the ambiguity function of the transmitted signals of K users in the range-angle domain, θ0 represents the angle of target 1, θ1 represents the angle of target 2, and Δr represents the distance difference between target 1 and target 2.

6. The communication-aware integrated beamforming method for suppressing range-angle side lobes according to claim 5, characterized in that: The step S5 comprises: P s =G s . in, represents the submatrix consisting of the L1th row to L2th row and the L3th row to L4th column of the matrix ·; P t Indicates the total transmission power, G c is the minimum SINR constraint for each communicating user, G s is the beamforming gain constraint toward the sensing target; Indicates the frequency and the angle is The launch steering vector at .

7. The communication-aware integrated beamforming method for suppressing range-angle sidelobes according to claim 1, characterized in that: The step S6 comprises: Introducing the auxiliary matrix Among them, R k ±0 and rank(R k )=1; Therefore, the objective function can be rewritten as follows: in, κ represents the region of submatrices {(k-1)L+1:kL,(k-1)L+1:kL} taken from the matrix [Y⊙M]; The constraints are transformed into R k Related, as follows: Among them, Tr(R k ) represents the matrix R k The trace of R k represents the covariance matrix of the precoding vector of the kth user, R W represents the sum of the covariance matrices of the precoding vectors of K users; Without loss of generality, we choose w k , so that is non-negative for any user k; therefore, the gain constraint in the direction of the perceived target is written as follows Therefore, the optimization problem is rewritten into a new optimization problem as follows and solved efficiently using the SDR technique; R k ±0,k=1,..K, 8. A communication-aware integrated beamforming system for suppressing range-angle sidelobes, characterized in that: include: Module M1: The base station communicates with multiple users under the digital beamforming ISAC architecture and detects aerial targets in a self-transmitting and self-receiving mode; Module M2: Determine the perception channel model and the communication channel model, obtain the communication reception signal at each user location and the perception reception signal scattered from the target back to the base station; Module M3: Based on the communication reception signal at each user's location, the SINR of each user is calculated through the communication user's reception signal model; based on the perceived reception signal scattered from the target back to the base station, the distance-angle-Doppler three-dimensional perception ambiguity function based on the digital beamforming ISAC architecture is obtained through the perception reception signal model; Module M4: Based on the distance-angle-Doppler three-dimensional perception ambiguity function, analyze the main lobe and side lobe structure of the ambiguity function, and extract the expression of the integral side lobe in the distance-angle dimension; Module M5: Based on the communication index and the perception index, the beamforming matrix of the ISAC system is jointly optimized and designed based on minimizing the integrated sidelobe value of the range-angle-Doppler three-dimensional perception ambiguity function in the preset range-angle sidelobe area; Module M6: Auxiliary variables are introduced to transform the problem of optimizing the beamforming matrix into the problem of optimizing the covariance matrix of the beamforming matrix, and a feasible solution to the joint design problem is obtained.

9. The communication-sensing integrated beamforming system for suppressing range-angle side lobes according to claim 8, characterized in that: The communication reception signal at each user location includes: Among them, z k (t) represents the additive Gaussian white noise of the received signal of the kth communication user at time t, with a corresponding mean of 0 and a variance of represents the communication channel model; w k represents the precoding vector of the kth user's transmitted signal, s k represents the signal transmitted by the kth user; The sensed received signal scattered from the target back to the base station includes: Where M represents the number of transmitting antennas, a m,n represents the complex scattering coefficient between the mth transmitting antenna and the nth receiving antenna, and represents the attenuation and phase change of the signal during transmission. Indicates the Doppler frequency of a uniformly moving target, f c represents the carrier frequency of the transmitted signal, λ is the corresponding wavelength, represents the signal transmitted by the mth antenna, t represents the time t, τ m,n represents the path delay between the transmitting array element and the receiving array element pair (m,n), and r0 represents the target distance, θ0 represents the angle of the target, and v0 represents the speed of the target; represents additive white Gaussian noise signal; The method of calculating the SINR of each user based on the communication reception signal at each user position by using the reception signal model of the communication user includes: in, represents the channel model of the kth communication user, w k represents the precoding vector of the kth user's transmitted signal, w n represents the precoding vector of the nth user's transmitted signal, represents the variance of the noise in the signal received by the kth user.

10. The communication sensing integrated beamforming system for suppressing range-angle side lobes according to claim 8, characterized in that: The method of obtaining a distance-angle-Doppler three-dimensional perception ambiguity function based on a digital beamforming ISAC architecture through a perception reception signal model based on the perception reception signal scattered from the target back to the base station includes: in, represents the phase caused by the target parameter, represents the received steering vector, the matrix X(Δr, Δf d ) represents the two-dimensional ambiguity function matrix in the range-Doppler domain, whose dimension is K rows and K columns, where the diagonal elements correspond to the self-ambiguity function of each user's transmitted signal, and the non-diagonal elements represent the cross-ambiguity function between different users; the element in the kth row and ith column of the matrix X is: Among them, b T (f,θ) represents the transmission steering vector with respect to frequency f and angle θ, W = [w1 w2 ... w K ] represents the precoding matrix of the transmitted signal; The expression of the integrated sidelobe in the distance-angle dimension includes: Where W and D represent the set of angle sidelobe area and distance sidelobe area to be suppressed respectively; represents the ambiguity function of the transmitted signals of K users in the range-angle domain, θ0 represents the angle of target 1, θ1 represents the angle of target 2, and Δr represents the distance difference between target 1 and target 2; The module M5 comprises: P s =G s . in, represents the submatrix consisting of the L1th row to L2th row and the L3th row to L4th column of the matrix ·; P t Indicates the total transmission power, G c is the minimum SINR constraint for each communicating user, G s is the beamforming gain constraint toward the sensing target; Indicates the frequency and the angle is The launch steering vector at ; The module M6 comprises: Introducing the auxiliary matrix Among them, R k ±0 and rank(R k )=1; Therefore, the objective function can be rewritten as follows: in, κ represents the region of submatrices {(k-1)L+1:kL,(k-1)L+1:kL} taken from the matrix [Y⊙M]; The constraints are transformed into R k Related, as follows: Among them, Tr(R k ) represents the matrix R k The trace of R k represents the covariance matrix of the precoding vector of the kth user, R W represents the sum of the covariance matrices of the precoding vectors of K users; Without loss of generality, we choose w k , so that is non-negative for any user k; therefore, the gain constraint in the direction of the perceived target is written as follows Therefore, the optimization problem is rewritten into a new optimization problem as follows and solved efficiently using the SDR technique; R k ±0,k=1,..K,

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