A Bistatic Communication and Sensing Integrated Beamforming Design Method Based on Interference Exploitation

Abstract

The present invention provides a bistatic communication and sensing integrated beamforming design method based on interference exploitation, belonging to the field of base station communication technology. The present invention constructs a bistatic communication and sensing integrated system, re-derives the Cramer-Rao bound for point target angle estimation under the bistatic model, and for the first time introduces the constructive interference of 16QAM in the beamforming design of communication and sensing integration, models the beamforming design of communication and sensing integration as an optimization problem with the optimization variable being the communication signal beamforming matrix, the objective function of this problem is the Cramer-Rao bound of the angle estimation of the point target representing the sensing performance, and the constraints include: the power of the base station transmitted signal must be within the power budget and the constraint of the received signal of the communication user satisfying the 16QAM constructive interference. On the premise that the communication user end satisfies the constructive interference constraint, the present invention minimizes the Cramer-Rao bound of the sensing performance as much as possible.

Description

Technical Field

[0001] The present invention belongs to the technical field of base communication, and particularly relates to a bistatic communication and sensing integrated beamforming design method based on interference utilization. Background Technique

[0002] For the vision of future communication systems, 6G wireless communication technology needs to meet more stringent performance metrics, including extremely high throughput, ultra-low latency, and high energy efficiency. In addition, wireless networks also need to support diverse intelligent applications, which not only pose higher requirements for communication performance but also continuously increase the demand for sensing capabilities. Technological trends indicate that future networks will not only meet traditional communication needs but also provide ubiquitous environmental sensing services.

[0003] With the evolution of technology, wireless sensing and wireless communication technologies have significant similarities in hardware architecture and signal processing algorithms, especially under high-frequency broadband conditions. At the same time, the scale of antennas in future systems will also continue to increase, which provides technical feasibility for the deep integration of communication and sensing. Therefore, integrating communication and sensing into a unified platform to share spectrum, energy, and hardware resources becomes a natural and efficient solution. Integrated sensing and communications (ISAC) technology can serve both communication and sensing requirements by sharing software and hardware platforms and time, frequency, and space resources, thus significantly improving spectrum utilization efficiency and reducing software and hardware complexity.

[0004] Numerous studies have investigated ISAC systems from different perspectives, and transmit beamforming design is an important part of them. Transmit beamforming can focus the transmitted wireless signals in the desired direction, thereby achieving high-speed data transmission and high-precision target sensing. There have been many studies on the beamforming problem in the 5G era, but this problem is more difficult for ISAC systems because beamforming in ISAC systems often needs to meet the requirements of both communication and sensing simultaneously. How to design the transmit beamforming matrix to achieve good communication and sensing performance is the key to ISAC beamforming design.

[0005] Most of the existing work focuses on the scenario setting of a single base station, expanding the communication base station into a full-duplex dual-functional base station (BS) with both communication and sensing functions. This model may have problems such as serious self-interference and limited sensing coverage. At present, the work on the integrated beamforming design of communication and sensing for multi-base stations is not yet perfect. Based on this, the present invention constructs a bistatic communication and sensing integrated model, re-derives the Cramér-Rao bound for point target angle estimation based on the bistatic model, and for the first time introduces 16QAM constructive interference in the integrated beamforming design of communication and sensing, relaxing the constraints of communication users, achieving better communication performance while ensuring a certain communication performance, and considering the integrated beamforming design work of communication and sensing under the above background. Summary of the Invention

[0006] The purpose of the present invention is to provide a bistatic communication and sensing integrated beamforming design method based on interference utilization, aiming to solve the problems that the existing model may have serious self-interference and limited sensing coverage, and the current work on the integrated beamforming design of communication and sensing for multi-base stations is not yet perfect.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] A bistatic communication and sensing integrated beamforming design method based on interference utilization, comprising the following steps:

[0009] Step 1: The signal transmitted by the transmitting base station generates an echo signal after detecting the sensing target, which is received by the sensing base station. The sensing base station estimates the angle based on the echo signal and derives the Cramér-Rao Bound (CRB) of the angle estimation.

[0010] Step 2: Introduce 16QAM constructive interference (CI), and select the corresponding communication constraints under the constructive interference according to the transmitted 16QAM signal.

[0011] Step 3: According to the derived communication constraints based on constructive interference and the Cramér-Rao bound of the angle estimation, construct a problem of minimizing the Cramér-Rao bound of the angle estimation on the premise of ensuring communication performance, transform the original non-convex problem into a convex problem by the semidefinite relaxation (SDR) method, and use the CVX toolbox to solve to obtain the optimal beamforming matrix.

[0012] The power constraint of the transmitted signal is:

[0013] ||x|| 2 ≤P t ;

[0014] where P t represents the given power budget;

[0015] The optimization problem solved after introducing CI is:

[0016]

[0017] ||x|| 2 ≤ P t ;

[0018] Convert it into an equivalent form:

[0019] where U ∈ ￡ 2×2 is an auxiliary matrix, tr(·) represents the trace of a matrix, and after semi - definite relaxation, it becomes:

[0020]

[0021] tr(R x ) ≤ P t ,

[0022] W k = w k w k H ;

[0023] Here, W k = w k w k H is an auxiliary variable, and relax it to W k f w k w k H , which is equivalent to the following linear matrix inequality:

[0024]

[0025] The final optimization problem is transformed into:

[0026]

[0027] tr(R x ) ≤ P t ,

[0028]

[0029] The transformed optimization problem is a convex optimization problem, and the CVX tool is used to solve it.

[0030] As a preferred solution of the present invention, the scenario includes a transmitter station, a sensing base station, at least one downlink communication user, and a sensing target. The 16QAM signal sent by the transmitting base station to the i-th communication user is The beamforming matrix of the i-th communication user is The transmitted signal is:

[0031]

[0032] And the baseband signals sent to different communication users are independent of each other, and the baseband signals are known at the transmitting base station and the sensing base station.

[0033] As a preferred solution of the present invention, it is set that θ and respectively correspond to the angles of the target relative to the transmitting base station and the sensing base station. The sensing channel matrix of the transmitting base station via the sensing target to the sensing base station is expressed as:

[0034]

[0035] where and are the receiving array response vector and the transmitting array response vector respectively. β is the reflection coefficient considering the path loss and radar cross-section (RCS) of the sensing target. [·] H represents the conjugate transpose of the vector, and [·] T represents the transpose of the vector.

[0036] As a preferred solution of the present invention, the echo signal received by the sensing base station is:

[0037]

[0038] In the formula, is an N t ×1 noise vector, and each term of which is additive white Gaussian noise in the sensing channel with a mean of 0 and a variance of σ s 2 ;

[0039] The unknown parameters in the sensing channel are composed of , where β r = Re{β}, β i = Im{β}. The Fisher information matrix for estimating the unknown target parameters is expressed as:

[0040]

[0041] In the formula: u = vec(Y R) represents the vectorization of the received signal, and Re{·} represents taking the real part;

[0042] In the matrix:

[0043]

[0044]

[0045] Each term in the matrix can be calculated by the following formula:

[0046]

[0047] Therefore, each term in the matrix is:

[0048]

[0049] Where, represents taking the derivative with respect to , represents taking the derivative with respect to θ, and R x = xx H ;

[0050] The perceived CRB matrix is:

[0051]

[0052] The estimation and the Cramer-Rao bound of θ are represented by the diagonal elements of . Taking as the sum of the Cramer-Rao bounds for the estimation of and θ as a characterization of the sensing performance.

[0053] As a preferred embodiment of the present invention, the received signal y at the i-th user i ∈￡ is expressed as:

[0054]

[0055] Where, represents the channel from the transmitting base station to the i-th communication user, and n i : CN(0,σ i 2 ) is additive white Gaussian noise with a mean of 0 and a variance of σ i 2 , which is the noise at the i-th user.

[0056] As a preferred embodiment of the present invention, based on the 16QAM constellation diagram, it is divided into four parts, and its constructive interference (CI) is as follows:

[0057] Ⅰ: For the four constellation points enclosed within the 16QAM constellation diagram, if the transmitted signal s i corresponds to one of these four points, then the constraint conditions are:

[0058] C1:

[0059] C2:

[0060] Ⅱ: For the four constellation points near the imaginary axis on the upper and lower sides of the outer part of the 16QAM constellation diagram, the constructive region extends away from the real axis along the decision boundaries on both the left and right sides of the constellation points. If the transmitted signal s i corresponds to one of these four points, then the constraint conditions are:

[0061] C1:

[0062] C2:

[0063] Ⅲ: For the four constellation points near the real axis on the left and right sides of the outer part of the 16QAM constellation diagram, the constructive region extends away from the imaginary axis along the decision boundaries on both the upper and lower sides of the constellation points. If the transmitted signal s i corresponds to one of these four points, then the constraint conditions are:

[0064] C1:

[0065] C2:

[0066] Ⅳ: For the constellation points at the four vertices of the outer part of the 16QAM constellation diagram, the constructive region extends infinitely far away from the imaginary axis and the real axis. If the transmitted signal s i corresponds to one of these four points, then the constraint conditions are:

[0067] C1:

[0068] C2:

[0069] Among them, represents the noise-free received signal of the i-th communication user, and Γ i represents the threshold of the signal-to-interference-plus-noise ratio (SINR) of the i-th communication user. The SINR of the i-th communication user is before introducing CI and becomes after introducing CI and satisfies the corresponding conversion relationship;

[0070] In the formula: The symbol represents different symbols corresponding to different quadrants. In the first quadrant, represents ≥; in the second quadrant, the real-axis part represents ≤, the imaginary-axis part represents ≥; in the third quadrant represents ≤; in the fourth quadrant, the real-axis part represents ≥, the imaginary-axis part represents ≤.

[0071] As a preferred embodiment of the present invention, based on the 16QAM constellation diagram, it is divided into four parts, and its constructive interference (CI) is as follows:

[0072] Ⅰ: For the four constellation points enclosed within the 16QAM constellation diagram, if the transmitted signal s i corresponds to one of these four points, then the constraint conditions are:

[0073] C1:

[0074] C2:

[0075] Ⅱ: For the four constellation points near the imaginary axis on the upper and lower sides outside the 16QAM constellation diagram, the constructive region extends away from the real axis along the decision boundaries on the left and right sides of the constellation points. If the transmitted signal s i corresponds to one of these four points, then the constraint conditions are:

[0076] C1:

[0077] C2:

[0078] Ⅲ: For the four constellation points near the real axis on the left and right sides outside the 16QAM constellation diagram, the constructive region extends away from the imaginary axis along the decision boundaries on the upper and lower sides of the constellation points. If the transmitted signal s i corresponds to one of these four points, then the constraint conditions are:

[0079] C1:

[0080] C2:

[0081] Ⅳ: For the constellation points at the four vertices outside the 16QAM constellation diagram, the constructive region extends infinitely far away from the imaginary axis and the real axis. If the transmitted signal s i corresponds to one of these four points, then the constraint conditions are:

[0082] C1:

[0083] C2:

[0084] Among them, represents the noise-free received signal of the \(i\)-th communication user, and \(\Gamma\) i represents the threshold of the signal-to-interference-plus-noise ratio (SINR) of the \(i\)-th communication user. The SINR of the \(i\)-th communication user is becomes after introducing CI and satisfies the corresponding conversion relationship;

[0085] In the formula: The symbol represents different symbols corresponding to different quadrants. In the first quadrant, represents \(\geq\); in the second quadrant, the real-axis part represents \(\leq\), and the imaginary-axis part represents \(\geq\); in the third quadrant, represents \(\leq\); in the fourth quadrant, the real-axis part represents \(\geq\), and the imaginary-axis part represents \(\leq\).

[0086] As a preferred solution of the present invention, it includes a transmitting base station, a sensing base station, at least one downlink communication user, and a sensing target. The transmitting base station transmits signals to communicate with the communication user and sense the target, and the sensing base station is used to receive the target echo signal and perform angle estimation.

[0087] As a preferred solution of the present invention, the transmitting base station is equipped with \(N\) t antennas, the sensing base station is equipped with \(N\) r antennas, and \(N\) t =\(N\) r , the communication user is configured with a single antenna, and the sensing target is a stationary point object.

[0088] Compared with the prior art, the beneficial effects of the present invention are:

[0089] 1. In the present invention, by constructing a bistatic communication and sensing integrated model, the Cramér-Rao bound for point target angle estimation is re-derived based on this. In this way, a more accurate basis is provided for system performance evaluation. Compared with the single base station model, it can better adapt to communication and sensing tasks in multi-base station scenarios, effectively reduce self-interference and expand the sensing coverage range, and improve the overall performance of the system.

[0090] 2. In the present invention, 16QAM constructive interference is first introduced into the communication-sensing integrated beamforming design. By analyzing different regions of the 16QAM constellation diagram, corresponding communication constraints are formulated. On the premise of ensuring communication reliability, the communication constraints are relaxed, which enables the system to more effectively utilize interference, convert multi-user interference into beneficial interference, improve the communication achievable rate of legitimate users, and optimize communication performance.

[0091] 3. In the present invention, by comprehensively considering the communication constraints based on constructive interference and the Cramér-Rao bound of angle estimation, an optimization problem is constructed and solved. The original non-convex problem is transformed into a convex problem to obtain the optimal beamforming matrix. While ensuring communication performance, the Cramér-Rao bound of angle estimation is reduced as much as possible, realizing the joint optimization of communication and sensing performance. On the basis of ensuring a certain communication performance, the sensing performance of the system is significantly improved, achieving a better balance of comprehensive performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0093] Figure 1 is the system model diagram in the present invention;

[0094] Figure 2 is the schematic diagram of 16QAM constructive interference in the present invention;

[0095] Figure 3 is the curve diagram of the Cramér-Rao bound varying with the power budget in the present invention;

[0096] Figure 4 is the curve diagram of the achievable rate varying with the power budget in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0097] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0098] Embodiment 1

[0099] Please refer to Figure 1 - Figure 4 , the present invention provides the following technical solutions:

[0100] A bistatic communication-sensing integrated beamforming design method based on interference utilization, comprising the following steps:

[0101] Step 1: The signal transmitted by the transmitting base station generates an echo signal after detecting the sensing target, which is received by the sensing base station. The sensing base station estimates the angle based on the echo signal and derives the Cramér-Rao Bound (CRB) of the angle estimation.

[0102] Step 2: Introduce 16QAM constructive interference (CI), and select the corresponding communication constraints under the constructive interference according to the transmitted 16QAM signal.

[0103] Step 3: According to the derived communication constraints based on constructive interference and the Cramér-Rao Bound of angle estimation, construct a problem of minimizing the Cramér-Rao Bound of angle estimation while ensuring communication performance. Transform the original non-convex problem into a convex problem by the semidefinite relaxation (SDR) method, and use the CVX toolbox to solve for the optimal beamforming matrix.

[0104] In a specific embodiment of the present invention, the object of the present invention is to solve the communication-sensing integrated beamforming design problem. It is intended to establish a communication-sensing integrated system model, which consists of a bistatic, multiple communication users, and a single sensing target. Introduce constructive interference to relax the communication constraints while ensuring communication performance. By deriving the Cramér-Rao Bound of angle estimation in the bistatic model, under the communication constraints of constructive interference and the transmit signal power budget constraint, optimize the beamforming vector at the transmitter to minimize the Cramér-Rao Bound of angle estimation, so as to reduce the Cramér-Rao Bound of angle estimation as much as possible and improve the sensing performance while ensuring communication performance. Figure 1 The scenario of the present invention is shown. The transmitting base station transmits a signal to communicate with the communication user, and at the same time uses the transmitted signal to sense the target in the scenario. The generated echo signal is received by the sensing base station and parameter estimation is performed. Figure 2 The schematic diagram of constructive interference of the 16QAM constellation diagram is shown. Introduce the constructive interference of 16QAM, and select the corresponding constructive interference constraints according to the region where the constellation points of the generated 16QAM signal are located. Represent the echo signal by establishing a sensing channel model of the two-base station. Derive the Cramér-Rao Bound of angle estimation from the echo signal. Under the communication constraints of constructive interference and the transmit signal power budget constraint, minimize the Cramér-Rao Bound to obtain the beamforming vector at the transmitter that makes the transmit signal meet the communication constraints and has better sensing performance.

[0105] Specifically, please refer to Figure 1 - Figure 2 , and take the process that the transmitting base station sends a signal to communicate with the communication user and uses this signal to sense the target to generate an echo signal, and the echo signal is received by the sensing base station as an example to illustrate in more detail:

[0106] The present invention considers the beamforming design of a dual-base station communication and sensing integrated system. The transmitting base station has N t antennas, and the sensing base station has N r antennas, and N t = N r . The communication user has a single-antenna configuration, and a single point target is a stationary point object. The transmitting base station transmits a communication signal, which is used to sense the single point target in the scene while communicating with the downlink communication user. The transmitted signal of the transmitting base station is expressed as:

[0107]

[0108] where s i ∈ £ is the 16QAM signal sent by the transmitting base station to the i-th communication user. The 16QAM signals of all users are randomly generated. is the beamforming matrix for the i-th communication user at the transmitter. The baseband signals s i sent to different communication users are independent of each other, that is, E{s i s j} = 0, i ≠ j, and E{s i s j} represents the cross-correlation function of s i and s j . The baseband signals are known at both the transmitting base station and the sensing base station.

[0109] According to the transmitted signal, the power of the transmitted signal is ||x|| 2 , and ||·|| 2 represents the square of the vector norm. Therefore, the power constraint is:

[0110] ||x|| 2 ≤ P t ;

[0111] where P t is the pre-determined power budget;

[0112] Let θ and respectively correspond to the angle of the target relative to the transmitting base station and the angle of the target relative to the sensing base station. Since the sensing target is stationary and the transmitting base station and the sensing base station are fixed, the two angles are fixed. Then, the sensing channel in the sensing system is expressed as:

[0113]

[0114] where β is a complex coefficient considering the path loss and radar cross-section (RCS) of sensing. Here, take where λ is the wavelength, c is the speed of light, f is the carrier frequency, and j is the imaginary unit, and r is the distance from the target to the base station. is the receive array response vector, is the transmit array response vector, [·] H denotes the conjugate transpose of a vector, [·] T denotes the transpose of a vector; the echo signal received by the sensing base station is:

[0115]

[0116] where, is an N t ×1 noise vector, each term of which is additive white Gaussian noise in the sensing channel with a mean of 0 and a variance of σ s 2 .

[0117] Specifically, the unknown parameters to be sensed are composed of , where β r = Re{β}, β i = Im{β}; the Fisher information matrix for estimating the unknown target parameters is expressed as:

[0118]

[0119] Here, u = vec(Y R ) denotes vectorizing the received signal, and Re{·} denotes the real part operation, and each term in the matrix can be calculated by the following formula:

[0120]

[0121] denotes the derivative of u with respect to p, and each term in the matrix is specifically expressed as:

[0122]

[0123] where, denotes derived with respect to , denotes derived with respect to θ, and R x = xx H denotes the covariance matrix of the transmit signal.

[0124] The CRB matrix for sensing is obtained through the inverse of the Fisher information matrix, and the CRB matrix for sensing is:

[0125]

[0126] Estimation The Cramer-Rao lower bound of and θ is represented by the diagonal elements of denotes the estimation of the sum of the Cramer-Rao lower bounds of

[0127] Assume that the channel state information (CSI) of the downlink communication channel is available for the transmitting base station, and the received signal y at the i-th communication user i ∈￡ is expressed as:

[0128]

[0129] where represents the communication channel from the transmitting base station to the i-th communication user, and the communication channel is set as a Rayleigh fading channel; n i :CN(0,σ i 2 ) is additive white Gaussian noise with a mean of 0 and a variance of σ i 2 which is the noise at the i-th user.

[0130] Constructive interference (CI) is defined as the interference that moves the constellation points of the received symbols away from the constellation diagram decision threshold. The idea is to relax the constraints on the transmitted symbols so that they are in the constructive interference region. Being in the constructive interference region can be considered as correctly identifying the received signal, and the constructive interference region is larger than the general traditional region, which also enables the introduction of CI technology to provide a more relaxed optimization for the communication system;

[0131] Based on the 16QAM constellation diagram, the constellation diagram is divided into four parts to discuss the CI constraints respectively, as shown in Figure 2 in the attached figure:

[0132] (1) For the constellation points in the box marked "1" in Figure 2 , since these constellation points are completely surrounded by the decision boundary, the constraint conditions should ensure that the received signal reaches the exact constellation point to avoid exceeding the decision boundary. Assume that the transmitted signal s i corresponds to a point in this region, and the corresponding constraint conditions are expressed as:

[0133] C1:

[0134] C2:

[0135] (2) For the constellation points in the box marked "2" in Figure 2 , the constraint should ensure that the received signal falls within the detection region far from the decision boundary (i.e., the dotted line parallel to the real axis); assume that the transmitted signal si For a certain point in this region, the corresponding constraint condition is expressed as:

[0136] C1:

[0137] C2:

[0138] (3) For the constellation points in the box marked "3" in Figure 2 , the constraint should ensure that the received signal falls within the detection region far from the decision boundary (i.e., the dotted line parallel to the imaginary axis). Assuming the transmitted signal is s i For a certain point in this region, the corresponding constraint condition is expressed as:

[0139] C1:

[0140] C2:

[0141] (4) For the constellation points in the box marked "4" in Figure 2 , the constraint should ensure that the received signal falls within the detection region far from the decision boundary. Since the constellation points in the box marked "4" are not completely surrounded by the decision boundary, they have a relatively large infinitely extended detection region. Assuming the transmitted signal is s i For a certain point in this region, the corresponding constraint condition is expressed as:

[0142] C1:

[0143] C2:

[0144] Among them, represents the noise-free received signal of the i-th communication user, and Γ i represents the threshold of the signal-to-interference-plus-noise ratio (SINR) of the i-th communication user, which is predefined before signal transmission. The SINR of the i-th communication user is before introducing CI and becomes after introducing CI. The symbol represents different symbols corresponding to different quadrants. For example, in the first quadrant, represents ≥; in the second quadrant, the real-axis part represents ≤, and the imaginary-axis part represents ≥; in the third quadrant, represents ≤; in the fourth quadrant, the real-axis part represents ≥, and the imaginary-axis part represents ≤.

[0145] After introducing constructive interference, minimize the Cramer-Rao bound of angle estimation while ensuring the communication performance of communication users, that is, solve the following optimization problem:

[0146]

[0147] ||x|| 2 ≤P t ;

[0148] Analyzing this problem is difficult to solve, so transform it into the following equivalent but easier-to-handle form:

[0149]

[0150] ||x|| 2 ≤P t ;

[0151] where U ∈ ￡ 2×2 is an auxiliary matrix, and then use semidefinite relaxation to solve the transformed optimization problem. After semidefinite relaxation, the problem becomes:

[0152]

[0153] tr(R x ) ≤ P t ,

[0154] W k =w k w k H ;

[0155] Here, tr(R x ) and ||x|| 2 both represent the transmit signal power, and W k =w k w k H is an auxiliary variable, which is relaxed to W k fw k w k H which is equivalent to the following linear matrix inequality:

[0156]

[0157] Finally, the problem is transformed into:

[0158]

[0159] tr(R x ) ≤ P t ,

[0160]

[0161] The transformed problem is a convex optimization problem and can be directly solved using the CVX tool.

[0162] Specifically, please refer to Figure 3 - Figure 4 , conduct numerical simulation on the analysis method proposed in this technical solution, and compare this technical solution with two other solutions. The results are as Figure 3 and Figure 4 shown. In the figure, this technical solution is referred to as "CI". One of the comparison solutions is the solution when CI is not adopted in this technical solution, which is referred to as "COM" in the figure. The other is the solution when Γ i is all 0 in this technical solution without adopting CI, which is referred to as "SEN" in the figure. The "SEN" solution represents the pursuit of extreme sensing performance. Figure 3 describes the relationship between the power budget and the Cramer-Rao bound of the three solutions. It can be seen from Figure 3 that under the same power budget, the CRB value of the "COM" solution is the largest, followed by the "CI" solution, and the "SEN" solution is the smallest. This also meets the expectations. Since the "CI" solution introduces the CI technology and relaxes the communication function constraints, its sensing performance is better than that of the "COM" solution under the same power constraint. And the "SEN" solution focuses on improving the sensing performance and does not consider the communication performance. Therefore, it can obtain the smallest CRB value under the same power constraint, that is, it can obtain the best sensing performance.

[0163] Figure 4 describes the relationship between the power budget and the achievable rate of the three solutions. The achievable rate of the i-th communication user in the solution of the present invention is expressed as:

[0164]

[0165] The achievable rate of the i-th communication user of the other two solutions is expressed as:

[0166]

[0167] The difference is that the "CI" solution adopts the CI technology and converts the multi-user interference into beneficial interference. Therefore, the achievable communication rate of the legitimate users does not include multi-user interference, while the "COM" solution and the "SEN" solution do not adopt the CI technology, so there is multi-user interference. It can be seen from Figure 4 that the achievable rate of the "CI" solution is the largest, followed by the "COM" solution, and the achievable rate of the "SEN" solution is the lowest.

[0168] Through Figure 3 , Figure 4The comparison results show that among the three solutions, the proposed technical solution can achieve a good level of communication performance while ensuring a certain level of sensing performance.

[0169] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A bistatic communication and sensing integrated beamforming design method based on interference exploitation, characterized in that: It includes the following steps: Step 1: The signal transmitted by the transmitting base station generates an echo signal after detecting the sensing target, which is received by the sensing base station. The sensing base station estimates the angle based on the echo signal and derives the Cramer-Rao bound of the angle estimation. Step 2: Introduce 16QAM constructive interference and select the corresponding communication constraints under the constructive interference according to the transmitted 16QAM signal. Step 3: According to the derived communication constraints based on constructive interference and the Cramer-Rao bound of the angle estimation, construct a problem of minimizing the Cramer-Rao bound of the angle estimation while ensuring the communication performance. Transform the original non-convex problem into a convex problem by the semidefinite relaxation method, and use the CVX toolbox to solve to obtain the optimal beamforming matrix. The power constraint of the transmitted signal is: ||x|| 2 ≤P t ; where P t represents a given power budget; The optimization problem solved after introducing CI is: Transform it into an equivalent form: where is the auxiliary matrix, and tr(·) represents the trace of the matrix. After semi-definite relaxation processing, it becomes: Here, W k = w k w k H is an auxiliary variable, which is relaxed to W k ≥ w k w k H , which is equivalent to the following linear matrix inequality: The final optimization problem is transformed into: The transformed optimization problem is a convex optimization problem and is solved using the CVX tool.

2. A method for designing a bistatic communication and sensing integrated beamforming based on interference utilization according to claim 1, characterized in that: The scenario includes a transmitter station, a sensing base station, at least one downlink communication user, and a sensing target. The 16QAM signal sent by the transmitting base station to the i-th communication user is The beamforming matrix of the i-th communication user is The transmitted signal is: Moreover, the baseband signals sent to different communication users are independent of each other, and the baseband signals are known at the transmitting base station and the sensing base station.

3. A bistatic communication and sensing integrated beamforming design method based on interference utilization according to claim 2, characterized in that: Set θ and correspond to the angles of the target relative to the transmitting base station and the sensing base station respectively. The sensing channel matrix of the transmitting base station through the sensing target to the sensing base station is expressed as: wherein and are the received array response vector and the transmitted array response vector respectively, β is the reflection coefficient considering the path loss and radar cross section of the sensing target, [·] H denotes the conjugate transpose of a vector, [·] T denotes the transpose of a vector.

4. A method for designing a bistatic communication and sensing integrated beamforming based on interference utilization according to claim 3, characterized in that: The echo signal received by the sensing base station is: wherein, is an N t ×1 noise vector, each term of which is additive white Gaussian noise in a sensing channel with a mean of 0 and a variance of σ s 2 ; The unknown parameters in the sensing channel are composed of , where β r = Re{β}, β i = Im{β}, and the Fisher information matrix for estimating the unknown target parameters is expressed as: where: u = vec(Y R ) represents vectorizing the received signal, and Re{·} represents taking the real part; In the matrix: Each term in the matrix can be calculated by the following formula: Therefore, each term in the matrix is: Among them, denotes taking the derivative of with respect to denotes taking the derivative of R with respect to θ, x = xx H ; The CRB matrix of sensing is: Estimation The Cramér-Rao lower bound of and θ is represented by the diagonal element of Take as the sum of the Cramér-Rao lower bounds of and θ as a characterization of the sensing performance.

5. A method for designing a bistatic communication and sensing integrated beamforming based on interference utilization according to claim 4, characterized in that: Received signal at the i-th user Denoted as: Among them, represents the channel from the transmitting base station to the i-th communication user, n i ~CN(0,σ i 2 ) is additive white Gaussian noise with a mean of 0 and a variance of σ i 2 , which is the noise at the i-th user.

6. A bistatic communication and sensing integrated beamforming design method based on interference utilization according to claim 5, characterized in that: Based on the 16QAM constellation diagram, it is divided into four parts, and their constructive interferences are as follows: Ⅰ: For the four constellation points enclosed within the 16QAM constellation diagram, if the transmitted signal s i corresponds to one of these four points, then the constraint condition is: Ⅱ: For the four constellation points near the imaginary axis on the upper and lower outer sides of the 16QAM constellation diagram, the constructive region extends along the decision boundaries on the left and right sides of the constellation points in the direction away from the real axis. If the transmitted signal s i corresponds to one of these four points, then the constraint condition is: Ⅲ: For the four constellation points near the real axis on the left and right outer sides of the 16QAM constellation diagram, the constructive region extends along the decision boundaries on the upper and lower sides of the constellation points in the direction away from the imaginary axis. If the transmitted signal s i corresponds to one of these four points, then the constraint condition is: Ⅳ: For the constellation points at the outer four vertices of the 16QAM constellation diagram, the constructive region extends infinitely far away from the imaginary axis and the real axis. If the transmitted signal s i corresponds to one of these four points, then the constraint is: wherein, represents the noise-free received signal of the i-th communication user, and Γ i represents the threshold of the signal-to-interference-plus-noise ratio (SINR) of the i-th communication user. The SINR of the i-th communication user is and becomes after introducing CI, and satisfies the corresponding conversion relationship; Where: the symbol indicates different symbols corresponding to different quadrants. In the first quadrant, represents ≥; in the second quadrant, the real-axis part represents ≤, and the imaginary-axis part represents ≥; in the third quadrant, represents ≤; in the fourth quadrant, the real-axis part represents ≥, and the imaginary-axis part represents ≤.

7. A bistatic communication and sensing integrated beamforming design method based on interference exploitation according to claim 6, characterized in that: It includes a transmitting base station, a sensing base station, at least one downlink communication user and a sensing target. The transmitting base station uses the beamforming design method described in any one of claims 1-6 to transmit signals to communicate with the communication user and sense the target. The sensing base station is used to receive the target echo signal and estimate the angle.

8. A bistatic communication and sensing integrated beamforming design method based on interference utilization according to claim 7, characterized in that: The transmitting base station is equipped with N t antennas, the sensing base station is equipped with N r antennas, and N t = N r . The communication user is configured with a single antenna, and the sensing target is a stationary point object.

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