Communication and sensing integrated beamforming method and system for suppressing range-angle sidelobes

Abstract

The application provides a communication and sensing integrated beamforming method and system for suppressing range-angle sidelobes, comprising: a base station communicates with multiple users under a digital beamforming ISAC architecture, while simultaneously detecting an aerial target in a self-generation and self-reception mode; 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 from the target back to the base station; calculating the SINR of each user; obtaining a range-angle-Doppler three-dimensional sensing ambiguity function under the digital beamforming ISAC architecture; extracting the expression of the integral sidelobe in the range-angle dimension; according to the communication index and the sensing index, jointly optimizing and designing the beamforming matrix of the ISAC system according to the integral sidelobe value in the preset range-angle sidelobe region; introducing an auxiliary variable to convert the problem of optimizing the beamforming matrix into the problem of optimizing the covariance matrix of the beamforming matrix, and obtaining a feasible solution of the joint design problem.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular, to a communication and sensing integrated beamforming method and system for suppressing range-angle sidelobes. BACKGROUND

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

[0003] In ISAC systems, waveform design is one of the key technologies. Early research mainly focused on the design of time-frequency domain waveforms, adopting two main methods: one is to improve existing waveforms to simultaneously achieve communication and sensing functions; the other method utilizes the inherent sensing potential of communication signals to perform sensing tasks, which 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 ISAC waveform design means. By adjusting the weighting factors of the antenna array to adjust the directivity of the transmitted signal, existing research has proposed various beamforming design methods to achieve a balance between sensing and communication performance. However, most of the existing ISAC waveform design research, whether based on time-frequency domain or spatial domain beamforming methods, mainly focuses on analyzing the second-order statistical properties of the observation data containing target position information, such as the covariance matrix and Fisher information matrix, to improve the accuracy of parameter estimation or signal processing gain. For example, by minimizing the Cramer-Rao bound (CRB) to optimize beamforming design, thereby improving the estimation accuracy of target parameters, or by maximizing the signal-to-interference-and-noise ratio (SINR) of the sensing receiver through beamforming technology to improve signal detectability in an interference and noise environment.

[0005] Despite the many advances, existing beamforming design indicators mainly evaluate the performance of the system at the target position, with limited consideration of the system response at non-target positions. In contrast, the ambiguity function, as a means of measuring signal response in the delay-Doppler domain, can comprehensively describe signal characteristics, including the main peak at the target position and the sidelobes at non-target positions. Reducing the sidelobe level can improve the detection ability of weak targets, suppress sidelobe interference, and enhance the environmental reconstruction effect. However, few studies have utilized the spatial degrees of freedom (DoFs) brought by antenna arrays to effectively suppress sidelobes. Even recent research has proposed a symbol-level beamforming method based on autocorrelation functions to reduce range sidelobes, but this method has high computational complexity and sacrifices communication rate due to the constant modulus constraint.

[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, the object of the present invention is to provide a communication-aware integrated beamforming method and system for suppressing range-angle sidelobes.

[0008] According to the present invention, a communication-aware integrated beamforming method for suppressing range-angle sidelobes is provided, comprising:

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

[0010] Step S2: 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;

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

[0012] Step S4: Based on the range-angle-Doppler three-dimensional perception ambiguity function, analyze the main lobe and side lobe structures of the ambiguity function, and extract the expression of the integrated side lobe in the range-angle dimension;

[0013] Step S5: Based on the communication index and the perception index, the beamforming matrix of the ISAC system is jointly optimized and designed by minimizing the integrated sidelobe value of the range-angle-Doppler three-dimensional perception ambiguity function within a preset range-angle sidelobe region;

[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, thereby obtaining 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 signal received by 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 to the target, θ0 represents the angle of the target, and v0 represents the speed of the target; represents the additive white Gaussian noise signal.

[0021] Preferably, the calculating of the SINR of each user based on the communication reception signal at each user location 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 method of obtaining a range-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:

[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, with dimensions of K rows and K columns, 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 functions 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, step S5 includes:

[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 transmit 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 includes:

[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 traces, 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, choose w k , making For any user k, it is non-negative; 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 SDR technology;

[0048]

[0049] R k ±0,k=1,...K,

[0050]

[0051] According to the present invention, a communication-aware integrated beamforming system for suppressing range-angle sidelobes is provided, comprising:

[0052] Module M1: The base station communicates with multiple users under the digital beamforming ISAC architecture and simultaneously 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 received signal at each user location, the SINR of each user is calculated using the received signal model of the communication user. Based on the perceived received signal scattered from the target back to the base station, the three-dimensional perception ambiguity function of range-angle-Doppler based on the digital beamforming ISAC architecture is obtained using the perceived received signal model.

[0055] Module M4: Based on the range-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 integrated side lobe in the range-angle dimension;

[0056] Module M5: Based on communication and perception indicators, the ISAC system's beamforming matrix is ​​jointly optimized to minimize the integrated sidelobe value of the range-angle-Doppler three-dimensional perception ambiguity function within a preset range-angle sidelobe region.

[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 signal received by 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 to the target, θ0 represents the angle of the target, and v0 represents the speed of the target; represents the additive white Gaussian noise signal;

[0064] The calculating of 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 method of obtaining a range-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:

[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, with dimensions of K rows and K columns, 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 functions 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 side lobe 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 includes:

[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 transmit 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 includes:

[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 traces, 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, choose w k , making For any user k, it is non-negative; 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 SDR technology;

[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. This invention utilizes existing communication systems to perform perception tasks. Under the premise of meeting the total transmit power limit, user signal-to-interference-and-noise ratio (SINR) constraints, and target direction beam gain requirements, it optimizes the design of the beamforming matrix to minimize the sidelobes of the system perception ambiguity function in the range-angle dimension, thereby improving the system's perception performance while maintaining excellent communication performance.

[0096] 2. This 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 transmit power, communication SINR, and perception gain in the target direction, thereby improving the system perception performance while maintaining communication quality.

[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. This invention can significantly reduce the sidelobe level of the perception ambiguity function in the range-angle domain, achieving lower ISLR, effectively improving the system's perception performance and spectral efficiency. It provides guidance for beamforming design in various application scenarios, such as sidelobe weak target detection in multi-target scenarios and reducing the false alarm probability in single-target detection.

[0099] 5. While satisfying 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 sidelobes of the ambiguity function in the range-angle dimension at non-target locations when the system detects targets, providing guidance for sidelobe weak target detection in multi-target scenarios and reducing 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, while maintaining stable communication performance and 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 upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0102] Figure 1 Schematic diagram of integrated communication and perception beamforming in multi-user scenarios.

[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 comparing the distance-angle dimension perception ambiguity function under the beamforming matrix designed for a communication perception integrated system beamforming method for suppressing distance-angle sidelobes and other schemes.

[0105] Figure 4 A schematic diagram comparing the cross-sectional diagrams of the range-angle perception ambiguity function in the distance and angle dimensions obtained by using a beamforming matrix designed for a communication-perception integrated system for suppressing range-angle sidelobes and other schemes.

[0106] Figure 5 A 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 used to suppress range-angle sidelobes and the beam pattern diagrams obtained from different design schemes. DETAILED DESCRIPTION

[0108] The present invention will be described in detail below with reference to specific embodiments. The following examples 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 skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0109] Example 1

[0110] According to the present invention, a communication-aware integrated beamforming method for suppressing range-angle side lobes is provided. Figures 1 to 2 Shown, including:

[0111] According to the present invention, a communication-aware integrated beamforming method for suppressing range-angle sidelobes is provided, comprising:

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

[0113] Step S2: 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;

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

[0115] Step S4: Based on the range-angle-Doppler three-dimensional perception ambiguity function, analyze the main lobe and side lobe structures of the ambiguity function, and extract the expression of the integrated side lobe in the range-angle dimension;

[0116] Step S5: Based on the communication index and the perception index, the beamforming matrix of the ISAC system is jointly optimized and designed by minimizing the integrated sidelobe value of the range-angle-Doppler three-dimensional perception ambiguity function within a preset range-angle sidelobe region;

[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, thereby obtaining 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 signal received by 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 to the target, θ0 represents the angle of the target, and v0 represents the speed of the target; represents the additive white Gaussian noise signal.

[0124] Specifically, the calculation of 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:

[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 three-dimensional perception ambiguity function of range, angle, and Doppler based on the digital beamforming ISAC architecture is obtained through a perception reception signal model based on the perception reception signal scattered from the target back to the base station, 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, with dimensions of K rows and K columns, 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 functions 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 transmit 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 traces, 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, choose w k , making For any user k, it is non-negative; 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 SDR technology;

[0151]

[0152] R k ±0,k=1...K,

[0153]

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

[0155] Example 2

[0156] Example 2 is a preferred example of Example 1

[0157] According to the present invention, a communication-aware integrated beamforming method for suppressing range-angle sidelobes is provided, comprising:

[0158] Consider a single-base station communication and perception integrated system equipped with M antennas, which simultaneously provides services to K communication users and detects targets. The target is assumed to be at a distance r0 from the first element of the transmitting array and moves towards 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, we calculate the SINR of the received signals at different users after the transmitted signal passes through the communication channel. First, we introduce the channel model h of the kth communication user in this embodiment. 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 angle of departure (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 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 signal received by the kth communication user at time t, with a corresponding mean of 0 and a variance of

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

[0169]

[0170] The signal to interference 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, first perform Taylor series expansion on the impulse response of the perception channel with a uniform speed target as shown below:

[0172]

[0173] Where δ(t, Θ0) represents the time 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 noise 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 propagation round trip, τ T,m (θ0) = mdsinθ0 / c, is the time delay caused by the spacing between the transmitting antennas, τ R,n(θ0) = ndsinθ0 / c is the time delay due to 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 three-dimensional perception ambiguity function in the range-angle-Doppler domain can be derived based on it for subsequent joint optimization design. Considering that the target is slowly moving 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, it can be ignored For the compression effect of the complex envelope of the waveform, we only need to focus on 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 time 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 narrowband assumptions, the impact of the target scattering coefficient between different transmitting array element-receiving array element pairs 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, formula (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 due to 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 noiseless 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 received steering vector, 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 ambiguity 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 perception joint design, the present invention uses the integrated sidelobe level (ISL) of the ambiguity function as a metric. In slow-moving or stationary scenarios, special attention is paid to the ISL in the range-angle domain, which is defined as follows:

[0198]

[0199] Where W and D represent the set of angular sidelobe regions and distance sidelobe regions that are desired to be suppressed, respectively. In addition, in order to achieve accurate perception, the perception receiver must receive a signal with a 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 for the beam gain pointing in the target direction is given as follows:

[0200]

[0201] Based on the above analysis, the present invention then designs an integrated communication and perception digital beamforming scheme, combining communication and perception performance metrics. This scheme aims to minimize the distance-angle distance (ISL) of the ambiguity function of the received signal after the transmitted signal passes through the perception channel in the ISAC system, thereby improving system sensing performance. This design also meets the communication user's SINR requirements, the gain constraints on the perception target, and the total transmission power constraints. The joint optimization problem established 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 towards the sensing target. In order to facilitate numerical calculation, Df d and Dr are discretized into L points respectively, Transformed into a matrix Each column represents L distance samples when the Pulser frequency value is fixed. At the same time, the angle side lobe q1 to be optimized is discretized into the angle side lobe area 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 column and the L3th row to L4th column of the matrix .

[0209] Substituting equation (17) into the optimization problem (P1), we can find 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 solve it. First, introduce the auxiliary matrix 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 {k(-1)L+1:kL, (k-1)L+1:kL} of the submatrix taken from the matrix [Y⊙M]. The constraints can also be transformed into the same as R k Related, as shown below

[0212]

[0213] Considering w in (17c) k is a feasible solution to problem (P1), then w k Arbitrary phase rotation of is also feasible. Without loss of generality, we choose w k , making 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 order to make all constraints convex in the above optimization problem (P2), 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 is greater than one, 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 sidelobes of the perception ambiguity function is significant, which is significantly better than other beamforming schemes.

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

[0223] Figure 5 A comparative plot of the ISLR variation of different design criteria within a specified optimization region, under varying sensing beam gain constraints, is presented. The results demonstrate that, under the same sensing beam gain conditions, the proposed method can reduce ISLR by approximately 3 to 15 dB compared to other methods. Furthermore, the proposed method maintains essentially unchanged ISLR with increasing beam gain in the target direction and improving sensing SINR, demonstrating its ability to maintain stable sensing performance without significantly impacting 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. Measures can be taken to mitigate such interference.

[0225] Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, and units provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same functions of the system and its various devices, modules, and units provided by the present invention 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 a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; the devices, modules, and units for implementing various functions can also be considered as both software modules implementing the method and structures within the hardware component.

[0226] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

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 simultaneously detects aerial targets in a self-transmitting and self-receiving mode; Step S2: 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; Step S3: Based on the communication received signal at each user location, the SINR of each user is calculated using the communication user's received signal model. Based on the perceived received signal scattered from the target back to the base station, the three-dimensional range-angle-Doppler perception ambiguity function based on the digital beamforming ISAC architecture is obtained using the perceived received signal model. Step S4: Based on the range-angle-Doppler three-dimensional perception ambiguity function, analyze the main lobe and side lobe structures of the ambiguity function, and extract the expression of the integrated side lobe in the range-angle dimension; Step S5: Based on the communication index and the perception index, the beamforming matrix of the ISAC system is jointly optimized and designed by minimizing the integrated sidelobe value of the range-angle-Doppler three-dimensional perception ambiguity function within a preset range-angle sidelobe region; 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, thereby obtaining 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 signal received by 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 to the target, θ0 represents the angle of the target, and v0 represents the speed of the target; represents the 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 calculating of 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, wherein: The method of obtaining a range-angle-Doppler three-dimensional perception ambiguity function based on a digital beamforming ISAC architecture by using 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, with dimensions of K rows and K columns, 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 functions 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 side lobe 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 sidelobes 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 transmit 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: Described 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 {(k-1)L+1:kL,(k-1)L+1:kL} of the submatrix 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 traces, 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, choose w k , making For any user k, it is non-negative; 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 SDR technology; 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 simultaneously 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 received signal at each user location, the SINR of each user is calculated using the received signal model of the communication user. Based on the perceived received signal scattered from the target back to the base station, the three-dimensional perception ambiguity function of range-angle-Doppler based on the digital beamforming ISAC architecture is obtained using the perceived received signal model. Module M4: Based on the range-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 integrated side lobe in the range-angle dimension; Module M5: Based on communication and perception indicators, the ISAC system's beamforming matrix is ​​jointly optimized to minimize the integrated sidelobe value of the range-angle-Doppler three-dimensional perception ambiguity function within a preset range-angle sidelobe region. 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-aware integrated beamforming system for suppressing range-angle sidelobes 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 signal received by 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 target distance, θ0 represents the angle of the target, and v0 represents the speed of the target; represents the additive white Gaussian noise signal; The calculating of 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 sidelobes according to claim 8, characterized in that: The method of obtaining a range-angle-Doppler three-dimensional perception ambiguity function based on a digital beamforming ISAC architecture by using 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, with dimensions of K rows and K columns, 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 functions 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 side lobe 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 includes: 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 transmit 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 includes: 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 {(k-1)L+1:kL,(k-1)L+1:kL} of the submatrix 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 traces, 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, choose w k , making For any user k, it is non-negative; 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 SDR technology; R k ±0,k=1,..K,

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