Design method of interference minimization-based flux-inductance integrated waveform

Abstract

The invention relates to the technical field of wireless communication, and discloses a design method of an interference-minimized communication-sensing integrated waveform, which comprises the following steps of: determining parameter data of an MIMO communication-sensing integrated system so as to obtain a communication channel matrix at a transmitting end and construct a radar sensing model; on the basis of constraint conditions of a radar perception model, a problem of minimizing interference power among multiple users is constructed; adding similarity constraint and constant modulus constraint to construct an optimization problem of minimizing interference power among multiple users; then, adding a weighting factor to obtain a compromise optimization problem; and determining a concave-convex penalty algorithm to enable a constant modulus constraint in the non-convex optimization problem to be equivalent to a convex constraint and a non-convex constraint, and converting the non-convex constraint into the convex constraint by using a successive convex approximation method to obtain a globally optimal solution. Compared with a strict radar constraint scheme, the method provided by the invention can provide a higher average reachable rate and a lower bit error rate, and greatly improves the communication performance while ensuring the sensing performance.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular, to a design method for a communication-sensing integrated waveform with minimized interference. Background Art

[0002] With the advent of the 5G era, the number of wireless communication devices has increased explosively. Against this background, the global communication industry's demand for wireless spectrum has become increasingly urgent. To alleviate this problem, future communication systems need to explore the possibility of coexisting with other electronic devices in the same frequency band. The connotation of communication-sensing integration not only includes enabling the co-frequency coexistence of radar and communication devices without mutual interference, so as to efficiently utilize the spectrum, but also includes designing a new integrated system that is compatible with both, enabling the system to simultaneously perform two functions of information transmission and target detection. Among them, a core issue of the new integrated communication-sensing system is the integrated waveform design, that is, designing a new multiplexing waveform that can carry communication information and be used for radar target detection.

[0003] The existing communication-sensing integrated waveforms mainly focus on the processing of time-frequency domain signals, and are mainly divided into the following two categories. The first category is centered on sensing, embedding communication information in the radar waveform; the second category is centered on communication, coupling sensing information in the communication waveform. The early communication-sensing integrated waveforms were mainly of the first category, but this method also has the disadvantages of unstable communication information and slow information rate transmission. With the development from 5G to 6G, the integrated waveform design that supports target detection while carrying communication data is becoming a viable solution. On the premise of fully ensuring communication / sensing performance, the sensing / communication performance can be improved, or a compromise can be made between the two according to actual needs. Since the jointly designed composite waveform fully considers the different performance requirements of radar and communication, the jointly designed composite waveform design is more comprehensive than the composite waveform design of communication and radar.

[0004] Therefore, in recent years, more and more scholars have begun to study this waveform. The integrated waveforms designed in some literatures can effectively achieve radar and communication capabilities. However, they often optimize the radar and lose communication performance, and the distortion of the transmitted waveform is not taken into account. Summary of the Invention

[0005] The present invention provides a design method for a communication-sensing integrated waveform with minimized interference, which greatly improves communication performance while ensuring sensing performance, so as to solve the technical problems of imperfect optimization and insufficient consideration in the existing waveform design methods.

[0006] The present invention provides a design method for a communication-sensing integrated waveform with minimized interference, based on a MIMO communication-sensing integrated system, where the MIMO communication-sensing integrated system includes a transmitting base station, multiple single-antenna users, and a sensing target; the method includes:

[0007] S1. Determine the parameter data of the MIMO communication-sensing integrated system to obtain a communication channel matrix at the transmitting end and construct a radar sensing model;

[0008] S2. Based on the constraint conditions of the radar sensing model, construct a problem of minimizing the interference power among multiple users;

[0009] S3. According to the problem of minimizing the interference power among multiple users, add similarity constraints and constant modulus constraints to construct an optimization problem of minimizing the interference power among multiple users;

[0010] S4. According to the optimization problem of minimizing the interference power among multiple users, add a weighting factor to obtain a compromise optimization problem; where the compromise optimization problem is a non-convex optimization problem;

[0011] S5. Determine a concave-convex penalty algorithm according to the non-convex optimization problem to equivalently transform the constant modulus constraint in the non-convex optimization problem into a convex constraint and a non-convex constraint, and use the successive convex approximation method to transform the non-convex into a convex constraint to obtain a global optimal solution.

[0012] Further, in step S1, the method for obtaining the channel matrix is: the transmitting end sends a pilot, and the receiving end calculates the channel matrix based on the received signal and the pilot and feeds it back to the transmitting end through a control link.

[0013] Further, in step S1, the parameter data includes: the transmitting end antenna array is a uniform linear array, the spacing between adjacent antennas is 0.5 wavelengths, the radar carrier frequency is 5.7 GHz, the number of subcarriers N used in OFDM is 16, the detection target is a point target, and the directions are -60° and -30°; the power P of the base station transmitting signal T is limited to 1, the signal-to-noise ratio of the communication receiving end is 20 dB, and the communication channel satisfies the standard complex Gaussian distribution.

[0014] Further, constructing the radar sensing model includes:

[0015] The communication received signal matrix of K downlink users is expressed as:

[0016] Y = HX + W

[0017] where, is the channel matrix, is the transmitted signal matrix, L is the length of the communication frame; is the noise matrix, and both have represents a Gaussian distribution;

[0018] The radar beam pattern can be represented by the covariance matrix of the directly designed radar transmit signal as:

[0019]

[0020] Assume L≥N to ensure that R X is positive definite; the transmit beam pattern of the communication-sensing integrated system is expressed as:

[0021] P d (θ) = α(θ) H R X α(θ)

[0022] where is the steering vector of the uniform linear array, θ is the detection angle, and Δ is the spacing between adjacent antennas after wavelength normalization;

[0023] The radar detection probability is defined as:

[0024]

[0025] where erf(x) is the complementary error function, and P FA is the false alarm probability;

[0026] The radar sensing model is constructed by using the difference power between the transmit waveform matrix X and the ideal radar waveform matrix X0, that is, the similarity constraint, and is expressed as:

[0027] Furthermore, the step S2 includes:

[0028] Given the covariance matrix R d corresponding to the ideal radar waveform, the waveform design model is expressed as:

[0029]

[0030] where R d is the desired Hermitian positive semi-definite covariance matrix.

[0031] Furthermore, the step S3 includes:

[0032] According to the waveform design model, the optimization problem of minimizing the multi-user interference power is formulated as:

[0033]

[0034] where is the ideal radar reference waveform, and x i,jis the (i, j)-th term of X, constraint C1 is a similarity constraint that controls the mismatch between the designed waveform and the perfect sensing waveform, and η is the allowable mismatch value; constraint C2 is an equality constraint for the perfect sensing waveform; constraint C3 is a constant modulus constraint.

[0035] Furthermore, step S4 includes:

[0036] Introduce a weighting factor to minimize the weighted sum of multi-user interference at the communication end and the similarity constraint at the radar end, obtaining the corresponding trade-off optimization problem as shown in the following equation:

[0037]

[0038] where 0 ≤ ρ ≤ 1 is a weighting factor introduced to balance the communication performance and sensing performance in the integrated system.

[0039] Furthermore, step S5 includes:

[0040] According to the radar reference waveform X0, express the expression function of the trade-off optimization problem as:

[0041]

[0042] Vectorize the matrix X to obtain x = vec(X) = [x 11 , …, x N1 , …, x 1L , …, x NL T , and rewrite the above objective function and constraints as:

[0043]

[0044] where is a positive semi-definite matrix; thus, the objective function is convex and the constraints are non-convex. The constant modulus constraint can be equivalently expressed as:

[0045]

[0046] The left and right sides of this new constraint are non-convex and convex, respectively; thus, to solve the non-convex constraint on the left side as a convex constraint, first use the successive convex approximation (SCA) method to replace |x(n)| 2 , and the global lower bound is as follows:

[0047] |x(n) - x0(n)| 2 ≥ 0 → |x(n)| 2 ≥ 2x * (n)x0(n) - |x0(n)| 2

[0048] Replace 1 ≤ |x(n)| with the lower bound​2 |x(n)| in 2 to achieve where x0 is the reference point; the quadratic non-convex constraint is split into a non-convex and a convex constraint, and then converted into two convex constraints, and the resulting optimization problem is finally expressed as:

[0049]

[0050] where τ > 0 is the penalty coefficient, and a(n), b(n) are auxiliary variables used to control the feasible region of the optimization problem.

[0051] Furthermore, after the step S5, it further includes:

[0052] S6. Use MATLAB simulation to verify the effectiveness of the algorithm and minimize multi-user interference.

[0053] The beneficial effects of the present invention are:

[0054] The present invention takes minimizing downlink multi-user interference as the waveform design goal, considers the radar waveform similarity constraint to improve communication performance at the same time, and the constant modulus constraint reduces the transmission distortion of the waveform, and constructs a non-convex optimization problem with quadratic equality constraints. The concave-convex penalty algorithm is proposed to effectively obtain the global optimal solution. Finally, the simulation results verify the convergence and effectiveness of the algorithm, indicating that this scheme can provide higher average achievable sum rate and lower bit error rate than the strict radar constraint scheme, and has better sensing performance than the existing integrated waveforms, achieving a good compromise between communication and sensing performance. Description of the Drawings

[0055] Figure 1 It is a schematic structural diagram of the communication and sensing integrated system model in the present invention.

[0056] Figure 2 It is a schematic comparison diagram of the relationship between the achievable sum rate and the signal-to-noise ratio of the present invention and other strategies.

[0057] Figure 3 It is a schematic comparison diagram of the relationship between the bit error rate and the signal-to-noise ratio under the present invention and other strategies.

[0058] Figure 4 It is a schematic diagram of the change of the total objective decreasing with the number of iterations in the present invention.

[0059] Figure 5 It is a schematic diagram of the change of the achievable sum rate at different trade-off factors in the present invention.

[0060] Figure 6 It is a schematic comparison diagram of the radar beam pattern between the present invention and the case of only radar.

[0061] Figure 7It is a schematic diagram of the relationship between radar detection probability and communication reachability and rate in the present invention.

[0062] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0063] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0064] Based on the radar communication integrated waveform design method with performance compromise, a communication perception integrated system for single-antenna users is established to address the waveform transmission distortion problem caused by radar amplifier oversaturation. The present invention studies a waveform design goal that minimizes downlink multi-user interference, while considering radar waveform similarity constraints to improve communication performance and constant modulus constraints to reduce waveform transmission distortion, and constructs a non-convex optimization problem with quadratic equality constraints. A convex-concave penalty algorithm is proposed, which equates the constant modulus constraint in the non-convex optimization problem to a convex constraint and a non-convex constraint, and uses the successive convex approximation method to transform the non-convex constraint into a convex constraint, and ultimately effectively obtains the global optimal solution. Finally, MATLAB is used to verify and analyze the performance of the communication perception integrated system. The specific steps include:

[0065] A design method for a synaesthesia integrated waveform with minimal interference, based on a MIMO communication perception integrated system, such as Figure 1 As shown, the MIMO communication and perception integrated system includes a transmitting base station, multiple single-antenna users and a perception target; the method includes:

[0066] S1. Determine parameter data of the MIMO communication and perception integrated system to obtain a communication channel matrix at the transmitting end and construct a radar perception model;

[0067] The method for obtaining the channel matrix is as follows: the transmitting end sends a pilot signal, the receiving end calculates the channel matrix through the received signal and the pilot signal, and feeds it back to the transmitting end through a control link.

[0068] The communication and perception integrated system includes a transmitting base station, multiple single-antenna users and a perception target; in the present invention, the transmitting end antenna array is a uniform linear array, the spacing between adjacent antennas is 0.5 wavelengths, the radar carrier frequency is 5.7GHz, the number of subcarriers used by OFDM is N=16, the detection target is a point target, and the direction is -60° and -30°; the power P of the base station transmission signal T The limit is 1, the signal-to-noise ratio at the communication receiving end is 20dB, and the communication channel satisfies the standard complex Gaussian distribution.

[0069] Building a radar perception model includes:

[0070] The communication received signal matrix of K downlink users is expressed as:

[0071] Y = HX + W (1)

[0072] where, is the channel matrix, is the transmit signal matrix, and L is the length of the communication frame; is the noise matrix, and both have represents the Gaussian distribution.

[0073] Considering the traditional working scenario, the following assumptions are made here:

[0074] 1. The communication-sensing integrated waveform designed in the present invention, i.e., the transmit signal matrix X, is based on the communication signal to detect the target.

[0075] 2. The waveform transmission channel H is a flat fading model, that is, it can ensure that the signal will not cause attenuation changes due to frequency changes.

[0076] 3. Ignoring the case of imperfectly estimated channel state information, the channel H can be perfectly estimated by pilot symbols for easy solution of the simulation.

[0077] Assume that the useful symbol matrix sent by the base station to the downlink users is Then the received signal matrix can be rewritten as:

[0078] Y = S + (HX - S) + W (2)

[0079] Assume that the elements of S in each user come from the same constellation. The second term in Equation (2) represents multi-user interference. Then the total interference power among multiple users in the system can be expressed as:

[0080]

[0081] The result shows that the average achievable sum rate of the downlink users is directly affected by the multi-user interference power. The SINR per frame of the i-th user is given by:

[0082]

[0083] where, s i,j represents the element located in the i-th row and j-th column of the matrix S, and E(·) represents the mathematical expectation. It can be concluded that the achievable sum rate of the users is expressed as:

[0084]

[0085] When the power of the communication constellation is given, the useful signal E(|s i,j |2 ) The power is constant. Then, by minimizing the total power of multi-user interference, the average achievable sum rate can be maximized.

[0086] The radar beam pattern can be expressed in terms of the covariance matrix of the directly designed radar transmit signal as:

[0087]

[0088] Assume L≥N to ensure that R X is positive definite; secondly, the transmit beam pattern of the communication-sensing integrated system is expressed as:

[0089] P d (θ) = α(θ) H R X α(θ) (7)

[0090] where is the steering vector of the uniform linear array, θ is the detection angle, and Δ is the spacing between adjacent antennas after wavelength normalization.

[0091] The radar detection probability is defined as:

[0092]

[0093] where erf(x) is the complementary error function, and P FA is the false alarm probability;

[0094] Similarly, the difference power between the transmit waveform matrix X and the ideal radar waveform matrix X0, i.e., the similarity constraint, is used to construct the radar sensing model, which is expressed as:

[0095]

[0096] S2. Based on the constraint conditions of the radar sensing model, construct the problem of minimizing the multi-user interference power;

[0097] The step S2 includes:

[0098] First, consider the problem of minimizing the multi-user interference power under the strict constraint conditions of the MIMO radar. Given the covariance matrix R d corresponding to the ideal radar waveform, the waveform design model is expressed as:

[0099]

[0100] where R d is the desired Hermitian positive semi-definite covariance matrix. After performing Cholesky decomposition on it, we get:

[0101] R d = FFH (11)

[0102] Among them, is a lower triangular matrix. To ensure that F is invertible, in the present invention, it is assumed that the covariance matrix R d is positive definite. Therefore, Equation (11) can be equivalently written as:

[0103]

[0104] Let The problem can be reformulated as:

[0105]

[0106] This is an orthogonal Procrustes problem, which has a simple closed-form global solution based on singular value decomposition, expressed as:

[0107] X0 * = UI N×L V H (14)

[0108] Among them, UI N×L V H = F H X is the singular value decomposition of F H X. Thus, the solution to the problem is:

[0109]

[0110] S3. According to the multi-user interference power minimization problem, add similarity constraints and constant modulus constraints to construct an optimization problem for multi-user interference power minimization;

[0111] The step S3 includes:

[0112] According to the waveform design model, i.e., Equation (10), the optimization problem for multi-user interference power minimization is formulated as:

[0113]

[0114] Among them, is the ideal radar reference waveform, x i,j is the (i, j)-th term of X, the constraint C1 is the similarity constraint, which controls the mismatch between the designed waveform and the perfect sensing waveform, and η is the admissible mismatch value; the constraint C2 is the equality constraint of the perfect sensing waveform; the constraint C3 is the constant modulus constraint.

[0115] S4. According to the optimization problem of multi-user interference power minimization, add a weighting factor to obtain a compromise optimization problem; among them, the compromise optimization problem is a non-convex optimization problem;

[0116] Step S4 includes:

[0117] Although the above optimization problem improves communication performance, in order to achieve a trade-off with sensing performance and simplify the problem, a weighting factor ρ is introduced to minimize the weighted sum of multi-user interference at the communication end and similarity constraint at the radar end, obtaining a corresponding trade-off optimization problem. The optimization problem can be equivalently rewritten as:

[0118]

[0119] Among them, 0 ≤ ρ ≤ 1 is a weighting factor introduced to balance communication performance and sensing performance in the integrated system. Among them, the trade-off design is actually Pareto optimization, and the solution obtained by solving equation (17) reaches Pareto optimality.

[0120] Mathematically, the objective function can be combined as follows:

[0121]

[0122] Let The objective function of problem (17) can be concisely described as:

[0123]

[0124] The above problem is an improved and simplified optimization model.

[0125] S5. Determine the concave-convex penalty algorithm according to the non-convex optimization problem to equivalently transform the constant modulus constraint in the non-convex optimization problem into a convex constraint and a non-convex constraint, and use the successive convex approximation method to transform the non-convex into a convex constraint to obtain the global optimal solution.

[0126] Step S5 includes:

[0127] According to the derived radar reference waveform X0, expand the objective function of equation (19) as:

[0128]

[0129] Let Q = A H A, G = A H B, problem (19) can be rewritten as:

[0130]

[0131] Vectorize the matrix X to obtain x = vec(X) = [x 11 , …, x N1 , …, x 1L , …, x NL T , and the above objective function and constraints are rewritten as: ​

[0132]

[0133] Among them, is a positive semi - definite matrix. Therefore, the objective function is convex and the constraint is non - convex. The constant modulus constraint can be equivalently expressed as:

[0134]

[0135] The left - hand side and the right - hand side of this new constraint are non - convex and convex respectively. Therefore, it is necessary to convert the non - convex constraint on the left - hand side into a convex constraint for solution. First, the successive convex approximation (SCA) method is used to replace |x(n)| 2 , and the global lower bound is as follows:

[0136] |x(n)-x0(n)| 2 ≥0 → |x(n)| 2 ≥2x * (n)x0(n)-|x0(n)| 2 (24)

[0137] Replace |x(n)| in 1≤|x(n)| 2 with the lower bound to achieve 2 , where x0 is the reference point; the quadratic non - convex constraint is split into a non - convex and a convex constraint, and then converted into two convex constraints. The resulting optimization problem is finally expressed as: Among them, τ>0 is the penalty coefficient, and a(n), b(n) are auxiliary variables used to control the feasible region of the optimization problem.

[0138]

[0139] Among them, τ>0 is the penalty coefficient, a(n), b(n) are auxiliary variables, used to control the feasible region of the optimization problem.

[0140] Both the objective function and the constraint conditions of the problem are converted into convex functions, so the CVX toolbox can be directly used for solution. The iterative steps of the concave - convex penalty algorithm are summarized in Algorithm 1.

[0141]

[0142] S6. Use MATLAB simulation to verify the effectiveness of the algorithm and the minimization of multi - user interference.

[0143] As Figure 2 shown, the achievable sum rate of users shows an upward trend as the signal - to - noise ratio increases. When the signal - to - noise ratio is fixed, the communication performance of the proposed omnidirectional waveform design is always better than that of the directional waveform design. In addition, it can also be found that in both cases of only radar, serious communication performance losses are suffered, while the design of the proposed scheme can significantly improve the average achievable rate. More importantly, the proposed waveform design achieves communication performance very close to zero MUI.

[0144] As Figure 3 shown, the bit error rate decreases with the increase of the signal-to-noise ratio. Without significant loss of sensing performance, the design of the present invention can significantly reduce the bit error rate.

[0145] As Figure 4 shown, the convergence behaviors of the proposed concave-convex penalty algorithm are compared with those of the Riemannian manifold algorithm and the alternating multiplier minimization algorithm. As the number of iterations increases, the multi-user interference gradually decreases and approaches a constant, which proves that the method proposed by the present invention can converge within a limited number of iterations and converges faster than other algorithms. Moreover, compared with other algorithms, the lower bound approximation constant of this algorithm is smaller, that is, the multi-user interference is smaller.

[0146] As Figure 5 shown, it can be seen that when the signal-to-noise ratio is constant, the closer the weighting factor is to 1, the greater the weight of the communication performance, and the better the performance, approaching the ideal situation without MUI. It can be concluded that weighted optimization has a more flexible practical application scenario than directly solving the optimization problem.

[0147] As Figure 6 shown, it can be seen that compared with the ideal beam pattern of the radar beam pattern designed by the proposed scheme of the present invention, the main lobe height is slightly lower than that of the ideal radar sensing waveform, while the sidelobe height is slightly higher than that of the ideal radar sensing waveform. This indicates that compared with the ideal sensing waveform, the loss of sensing performance of the communication-sensing integrated waveform of the present invention is slight, which can be ignored compared with the substantial improvement of the above communication performance.

[0148] As Figure 7 shown, it can be seen that as the detection probability increases or decreases, the average achievable sum rate also decreases or increases accordingly, and the two show an inverse relationship, which indicates that there is a trade-off relationship of mutual optimization between the communication performance and the radar sensing performance of the proposed integrated waveform.

[0149] Therefore, the present invention can clearly sense and distinguish the echoes of different moving targets while communicating with multiple single-line user targets, which not only greatly improves the user achievable and transmission rates, but also speeds up the convergence rate of the algorithm.

[0150] In summary, the present invention discloses a design method for a communication-sensing integrated waveform with minimized interference. In the proposed waveform design method, first, based on a communication-sensing integrated system for single-antenna users, with minimizing downlink multi-user interference as the waveform design objective, while considering the radar waveform similarity constraint to improve communication performance and the constant modulus constraint to reduce waveform transmission distortion, a non-convex optimization problem with quadratic equality constraints is constructed. Second, a concave-convex penalty algorithm is proposed to equivalently transform the constant modulus constraint in the non-convex optimization problem into a convex constraint and a non-convex constraint, and the successive convex approximation method is used to transform the non-convex into a convex constraint, and finally the global optimal solution can be effectively obtained. Finally, the simulation results verify the convergence and effectiveness of the algorithm, indicating that this scheme can provide a higher average achievable sum rate and a lower bit error rate than the strict radar constraint scheme, and has better sensing performance than the existing integrated waveforms, achieving a better compromise between communication and sensing performance.

[0151] It should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, device, article or method. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, device, article or method including that element.

[0152] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A design method for a joint communication and sensing integrated waveform with minimized interference, characterized in that, Based on the MIMO communication and sensing integrated system, the MIMO communication and sensing integrated system includes a transmitting base station, multiple single-antenna users, and a sensing target; the method includes: S1. Determine the parameter data of the MIMO communication and sensing integrated system to obtain a communication channel matrix at the transmitting end and construct a radar sensing model; S2. Based on the constraint conditions of the radar sensing model, construct a problem of minimizing the interference power among multiple users; S3. According to the problem of minimizing the interference power among multiple users, add similarity constraints and constant modulus constraints to construct an optimization problem of minimizing the interference power among multiple users; S4. According to the optimization problem of minimizing the interference power among multiple users, add a weighting factor to obtain a compromise optimization problem; wherein, the compromise optimization problem is a non-convex optimization problem; S5. Determine the concave-convex penalty algorithm according to the non-convex optimization problem to equivalently represent the constant modulus constraint in the non-convex optimization problem as a convex constraint and a non-convex constraint, and use the successive convex approximation method to transform the non-convex into a convex constraint to obtain the global optimal solution.

2. The design method of the integrated communication and sensing waveform with minimum interference according to claim 1, characterized in that, In the step S1, the method for obtaining the channel matrix is: the transmitting end sends pilots, and the receiving end calculates the channel matrix based on the received signals and pilots and feeds it back to the transmitting end through a control link.

3. The design method of the integrated communication and sensing waveform with minimized interference according to claim 2, characterized in that, In the step S1, the parameter data includes: the transmitting antenna array is a uniform linear array, the spacing between adjacent antennas is 0.5 wavelengths, the radar carrier frequency is 5.7 GHz, the number of subcarriers N used in OFDM is 16, the detection target is a point target, and the directions are -60° and -30°; the power P of the base station transmitting signal T is limited to 1, the signal-to-noise ratio of the communication receiving end is 20 dB, and the communication channel satisfies the standard complex Gaussian distribution.

4. The design method of the integrated communication and sensing waveform with minimized interference according to claim 3, characterized in that, Constructing the radar sensing model includes: The communication received signal matrix of K downlink users is expressed as: Y = HX + W Among them, is the channel matrix, is the transmitted signal matrix, and L is the length of the communication frame; is the noise matrix, and both have represents a Gaussian distribution; The radar beam pattern can be expressed by the covariance matrix of the directly designed radar transmission signal as: Assume that L ≥ N and ensure R X is positive definite; the transmit beam pattern of the communication-sensing integrated system is expressed as: P d (θ) = α(θ) H R X α(θ) Among them, is the steering vector of the uniform linear array, θ is the detection angle, and Δ is the spacing between adjacent antennas after wavelength normalization; The radar detection probability is defined as: where erf(x) is the complementary error function, and P FA is the false alarm probability; The radar perception model is constructed using the difference power between the transmitted waveform matrix X and the ideal radar waveform matrix X0, that is, the similarity constraint, which is expressed as:

5. The design method of the integrated communication and sensing waveform with minimized interference according to claim 4, characterized in that, The step S2 includes: Given the covariance matrix R corresponding to the ideal radar waveform d , the waveform design model is expressed as: where, R d is the desired Hermitian positive semi - definite covariance matrix.

6. The design method of the integrated communication and sensing waveform with minimum interference according to claim 5, characterized in that The step S3 includes: According to the waveform design model, the optimization problem of minimizing the interference power among multiple users is formulated as: s.t. C1: ||X - X0|| 2 ≤ η Among them, is the ideal radar reference waveform, and x i,j is the (i, j)-th term of X. The constraint C1 is a similarity constraint that controls the mismatch between the designed waveform and the perfect sensing waveform, and η is the admissible mismatch value; the constraint C2 is an equality constraint for the perfect sensing waveform; the constraint C3 is a constant modulus constraint.

7. The design method of the integrated communication and sensing waveform with minimum interference according to claim 6, characterized in that, The step S4 includes: Introduce a weighting factor to minimize the weighted sum of the multi-user interference at the communication end and the similarity constraint at the radar end to obtain the corresponding compromise optimization problem, as shown in the following formula: where 0 ≤ ρ ≤ 1 is a weighting factor introduced to balance the communication performance and sensing performance in the integrated system.

8. The design method of the integrated communication and sensing waveform with minimum interference according to claim 7, characterized in that The step S5 includes: According to the radar reference waveform X0, express the expression function of the compromise optimization problem as: Vectorize the matrix X to obtain x = vec(X) = [x 11 , …, x N1 , …, x 1L , …, x NL T , and the above objective function and constraints are rewritten as:​ where Q > 0 is a positive semi-definite matrix; therefore, the objective function is convex and the constraint is non-convex, and the constant modulus constraint can be equivalently expressed as: The left and right sides of this new constraint are non-convex and convex, respectively; therefore, to solve the non-convex constraint on the left side by converting it into a convex constraint, the successive convex approximation (SCA) method is first used to replace |x(n)| 2 , and the global lower bound is shown as follows: |x(n) - x0(n)| 2 ≥0 → |x(n)| 2 ≥2x * (n)x0(n) - |x0(n)| 2 Replace |x(n)| in 1 ≤ |x(n)| with a lower bound 2 to achieve 2 where x0 is the reference point; split the quadratic non-convex constraint into a non-convex and a convex constraint, and then convert it into two convex constraints. The resulting optimization problem is finally expressed as: where x0 is the reference point; split the quadratic non-convex constraint into a non-convex and a convex constraint, and then convert it into two convex constraints. The resulting optimization problem is finally expressed as: where τ > 0 is a penalty coefficient, and a(n), b(n) are auxiliary variables used to control the feasible region of the optimization problem.

9. The design method of the integrated communication and sensing waveform with minimum interference according to claim 1, characterized in that, After the step S5, it further includes: S6. Use MATLAB simulation to verify the effectiveness of the algorithm and the minimization of multi-user interference.

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