ISAC beamforming method taking perception as main part in target search stage

Abstract

The invention discloses a sensing-oriented ISAC beam forming method in a target search stage, and the method comprises the steps: building a system model, and obtaining a downlink communication signal of a communication user and a target echo signal at each angle sampling point in a sensing region; carrying out analysis and formula derivation on measurement indexes of the communication performance and the sensing performance of the ISAC system; a sensing-dominated ISAC beamforming method in a target search stage is provided, and the maximum CRB of target positioning in a sensing area is minimized on the premise that the SNR of target echo signals at communication, rate and angle sampling points and the total transmitting power constraint of a base station are met; an SDR algorithm is used for solving a sensing-oriented ISAC beam forming method in a target search stage, and the sensing-oriented ISAC beam forming method is compared with a pure communication scene to analyze a communication performance loss condition. According to the invention, the cooperative improvement of the communication performance and the sensing performance in the ISAC system is realized, the expected sensing precision can be achieved, and the loss of the communication performance is controlled.

Description

Technical Field

[0001] The present invention belongs to the field of communication technologies, and relates to an ISAC beamforming method mainly based on sensing in the target search stage. Background Art

[0002] With the rapid development of emerging application scenarios such as the Internet of Things, intelligent transportation, and smart cities, traditional communication and sensing systems are facing problems such as tight spectrum resources, high device complexity, and single function. In order to meet the dual demands of efficient and reliable communication and precise sensing in modern society, the Integrated Sensing and Communication (ISAC) technology has emerged. By integrating sensing and communication functions into a single radio frequency device, the ISAC technology not only improves the spectrum efficiency of the system but also enhances the flexibility and intelligent decision-making ability of the system.

[0003] In an ISAC system, beamforming technology is one of the key resource allocation methods to achieve efficient communication and precise sensing. By reasonably designing the beamforming scheme, the signal quality of the sensing target can be optimized while meeting the needs of communication users. However, the design of the ISAC beamforming scheme in the target search stage faces many challenges.

[0004] In the target search stage, since the target position is unknown and only a certain sensing area is known, the base station cannot accurately transmit signals in the direction of the target. Traditional ISAC beamforming in the target search stage only transmits narrow beams to communication users and wide beams to the sensing area to cover the entire sensing area, but it cannot guarantee the sensing performance within the sensing area. Therefore, a low-complexity ISAC beamforming scheme in the target search stage is needed to solve this problem.

[0005] In the existing research solutions for the target search stage, they can be roughly divided into two categories: (1) alternately performing communication and sensing functions through time resource or frequency domain division, such as allocating communication and sensing time slots based on a frame structure. Although such methods can avoid mutual interference between communication and sensing, the resource utilization rate is low, and the interval time of beam scanning may lead to target detection delay or missed detection, making it difficult to meet the requirements of search scenarios with high real-time requirements; (2) with communication performance as the leading factor, minimizing the beam pattern matching error within the sensing area on the premise of ensuring communication sum rate, but this may result in an overly wide main lobe of the sensing beam and serious side lobe leakage, reducing the sensing accuracy.

[0006] In the existing ISAC beamforming scheme in the target search stage, although the communication performance is guaranteed and the detection probability or beam pattern shape within the sensing area can be ensured, in the actual application scenario, the sensing accuracy within the sensing area is the ultimate concern. The target search stage often faces scenarios of high-speed moving targets or sudden interferences, while the existing schemes lack the ability of real-time dynamic adjustment. For example, the slow beam switching speed and the algorithm relying on static environment assumptions may lead to target loss or communication interruption, and it is difficult to adapt to the rapidly changing channel conditions or target positions. Moreover, the ISAC beamforming scheme in the target search stage often has difficulty in optimizing resource allocation. Overemphasis on sensing may lead to a decrease in communication throughput, and vice versa, which affects the search efficiency. In addition, the joint design complexity of the dual-functional beam is high, which easily causes performance trade-offs and reduces the overall system efficiency.

[0007] Therefore, there is a trade-off between communication performance and sensing performance in the ISAC system. It is necessary to analyze and study the ISAC beamforming scheme in the target search stage of the actual scenario, and propose a low-complexity ISAC beamforming scheme that can improve the sensing accuracy within the entire sensing area while ensuring the communication performance. Summary of the Invention

[0008] The present invention aims to solve the technical problem of how to simultaneously meet the sensing accuracy and communication performance. The present invention provides an ISAC beamforming method mainly for sensing in the target search stage, and the technical solution adopted is:

[0009] An ISAC beamforming method mainly for sensing in the target search stage includes the steps of:

[0010] S1. Construct a system model to obtain the downlink communication signal of the communication user and the target echo signal at each angular sampling point within the sensing area;

[0011] S2. According to the downlink communication signal and the target echo signal, analyze and derive the formula for the measurement indexes of the communication performance and sensing performance of the ISAC system;

[0012] S3. According to the above analysis and formula derivation, propose an ISAC beamforming method mainly for sensing in the target search stage, and minimize the maximum CRB of target positioning within the sensing area under the premise of meeting the communication sum rate, the SNR of the target echo signal at the angular sampling point, and the base station total transmit power constraint;

[0013] S4. Use the SDR algorithm to solve the ISAC beamforming method mainly for sensing in the target search stage, and compare it with the pure communication scenario to analyze the communication performance loss situation.

[0014] In an embodiment of the present invention, the step S1 includes:

[0015] The system model includes a base station, a sensing area, and K communication users;

[0016] The positions of the communication users are randomly distributed. The base station has N antennas, and the spacing between the antennas is d = λ / 2, where λ is the signal wavelength, and d k is the distance between the k-th communication user and the base station;

[0017] Assume that the sensing area is a sector area centered at the base station. The entire sensing area is sampled by angle, and the sensing area is equally divided into I parts by angle. The distance from each angle sampling point in the sensing area to the base station is d r .

[0018] In an embodiment of the present invention, the step S1 includes:

[0019] The array steering vector at the direction θ k of the k-th communication user is expressed as:

[0020]

[0021] In formula (1), k = 1, 2,..., K. The base station transmits an OFDM signal with M subcarriers and L symbols. The transmitted OFDM symbol satisfies unit symbol energy, that is, |[X k m,l | = 1;

[0022] The channel attenuation of the communication signal is expressed as where α is the attenuation coefficient;

[0023] τ k = d k / c represents the signal propagation delay between the k-th communication user and the base station. Δf is the subcarrier spacing. Then the downlink communication signal at the k-th communication user is expressed as:

[0024]

[0025] In formula (2), w c,k represents the beamforming weight of the k-th communication user, and w r,j represents the beamforming weight of the j-th sensing area. The first part is the downlink user desired signal, the second part is the inter-user interference, the third part is the interference of the sensing signal on the communication signal, and the fourth part is the additive white Gaussian noise, where m = 1, 2,..., M and l = 1, 2,..., L.

[0026] In an embodiment of the present invention, the step S1 includes:

[0027] The calculation of the echo signal attenuation is expressed as:​

[0028]

[0029] In formula (3), P r represents the total transmit power allocated by the base station to the sensing area, G represents the base station antenna gain, and ρ represents the scattering cross-sectional area of the target within the sensing area;

[0030] Let X represent the OFDM signal transmitted by the base station to the sensing area, and T z represents the sum of the basic symbol duration and the cyclic prefix duration of OFDM, and ν i represents the radial velocity of the target at the i-th angular sampling point within the sensing area relative to the base station. The target echo signal received by the base station at the i-th angular sampling point is expressed as:

[0031]

[0032] In formula (4), the first part is the desired echo signal, the second part is the communication user interference, and the third part is the additive white Gaussian noise.

[0033] In one embodiment of the present invention, the step S2 includes:

[0034] According to the downlink communication signal of the k-th communication user, the SINR of the k-th downlink user is expressed as:

[0035]

[0036] In formula (5), σ 2 represents the noise variance;

[0037] The calculation of the communication sum rate is expressed as:

[0038]

[0039] According to the target echo signal, the calculation of the SNR of the target echo signal received by the base station at the i-th angular sampling point is expressed as:

[0040]

[0041] The target echo signal at each angular sampling point within the sensing area is obtained.

[0042] In one embodiment of the present invention, the step S2 includes:

[0043] The channel parameters of the target are expressed as: The Fisher information matrix of the channel parameters is expressed as:

[0044]

[0045] In formula (8), E = Y - G is the noise-free part in the echo signal, and D i and D r,i are intermediate variables;

[0046]

[0047] The position parameter is expressed as: ξ po,i = [p i T , ν i , g r T , where p i represents the target coordinate at the i-th angular sampling point;

[0048] The channel parameter ξ ch,i and the position parameter ξ po,i The corresponding relationship between elements is:

[0049]

[0050] The channel parameter ξ ch,i and the position parameter ξ po,i There is a conversion from polar coordinates to rectangular coordinates, and the calculation of the Jacobian matrix is expressed as:

[0051]

[0052] In formula (12), a = 1, 2, 3, 4 and b = 1, 2, 3, 4;

[0053] The Fisher information matrix of the position parameter is expressed as:

[0054]

[0055] The CRB of the target position estimation is expressed as:

[0056]

[0057] In formula (14), tr(·) represents the trace operation.

[0058] In an embodiment of the present invention, the step S3 includes:

[0059] According to the analysis of the measurement indexes of the communication performance and sensing performance of the ISAC system, the ISAC beamforming method mainly based on sensing in the target search stage is expressed as:

[0060]

[0061] In formula (15), the first constraint is that the communication sum rate satisfies the sum rate threshold Γ​c Requirement, the second constraint is that the perceived signal-to-noise ratio satisfies the signal-to-noise ratio threshold Γ SNR Requirement, the third constraint is the total transmit power P of the base station t Requirement;

[0062] The goal is to minimize the maximum CRB within the sensing area to meet the sensing accuracy requirements, and ||·||2 represents the two-norm operation.

[0063] In one embodiment of the present invention, the step S3 includes:

[0064] Using the properties of the logarithmic function to convert the communication sum rate calculation formula (6) into:

[0065]

[0066] Then, using the conversion relationship between the logarithmic function and the exponential function, the communication sum rate constraint in formula (15) is converted to:

[0067]

[0068] Combining formula (5) and formula (17), the communication sum rate constraint is further transformed into:

[0069]

[0070] The transmit power constraint in formula (15) is converted to

[0071]

[0072] Set the CRB to a predetermined value reflecting the sensing accuracy requirements, assume that the CRB threshold t has a fixed value and optimize the sensing beamforming vector, while ensuring that the system meets the constraint conditions. Introduce an intermediate variable t, and formula (15) is converted to:

[0073]

[0074] Formula (20) represents the sensing-based ISAC beamforming method in the target search stage.

[0075] In one embodiment of the present invention, the step S4 includes:

[0076] Apply the SDR algorithm to solve the sensing-based ISAC beamforming method in the target search stage, and introduce new variables and F in formula (8) ch,i is converted to:

[0077]

[0078] The problem of formula (20) is transformed into:

[0079]

[0080] In formulas (21) and (22), the constraints of rank(W c,k ) = 1 and rank(W r ) = 1 are relaxed, and after solving the problem to obtain W c,k and W r , the communication beam weights are obtained as follows:

[0081]

[0082] In formula (23), h k = g k a(θ k );

[0083] Using the eig function in MATLAB to perform EVD decomposition on W r to obtain the eigenvalues U and eigenvectors Ω, and then sorting U in descending order to obtain the sorted eigenvalue vector ε, then the sensing beam weight calculation is expressed as:

[0084]

[0085] In formula (24), Ω :,1 is the eigenvector corresponding to the eigenvalue.

[0086] In an embodiment of the present invention, the step S4 includes:

[0087] Let w k represent the beam weight of the k-th communication user in a pure communication scenario, and set an intermediate variable Then the SINR of the k-th communication user in a pure communication scenario is expressed as:

[0088]

[0089] The communication sum rate calculation in a pure communication scenario is expressed as:

[0090]

[0091] The beamforming problem in a pure communication scenario is expressed as:

[0092]

[0093] Using the SDR algorithm to solve the problem of formula (27), the final form of the beamforming problem in a pure communication scenario is expressed as:

[0094]

[0095] The calculation of communication performance loss is expressed as:

[0096]

[0097] The loss of communication performance is calculated by formula (29) to keep the loss of communication performance within an acceptable range.

[0098] Advantages of the present invention:

[0099] 1. In the target search stage of the present invention, the ISAC beamforming method mainly based on sensing evaluates the specific sensing performance within the sensing area. On the premise of meeting the constraints of communication sum rate, signal-to-noise ratio of sensing signals, and total transmit power of the base station, the maximum Cramer-Rao bound of target localization within the entire sensing area is minimized, and the sensing performance within the entire sensing area is improved. The present invention effectively realizes the collaborative improvement of communication performance and sensing performance in the ISAC system through the proposed beamforming scheme, achieves the desired sensing accuracy, and the communication performance loss is less than 3%, within an acceptable range.

[0100] 2. The ISAC beamforming method mainly based on sensing in the target search stage of the present invention has low complexity, which is only related to the number of symbols M and the number of subcarriers L of the OFDM signal, and the complexity is Moreover, it can ensure the requirements of communication sum rate, SNR, and total transmit power. Description of the Drawings

[0101] Figure 1 is a flowchart of the ISAC beamforming method mainly based on sensing in the target search stage provided by an embodiment of the present invention;

[0102] Figure 2 is a system model diagram provided by an embodiment of the present invention;

[0103] Figure 3 is a comparison result diagram of the maximum CRB of target localization when the number of antennas is different provided by an embodiment of the present invention;

[0104] Figure 4 is a comparison result diagram of the communication performance loss when the number of antennas is different provided by an embodiment of the present invention;

[0105] Figure 5 is a comparison result diagram of the maximum CRB of target localization when the number of communication users is different provided by an embodiment of the present invention;

[0106] Figure 6 is a comparison result diagram of the communication performance loss when the number of communication users is different provided by an embodiment of the present invention. Detailed Embodiments

[0107] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0108] In order to deeply study the communication performance and sensing performance in an Integrated Sensing and Communication (ISAC) system, the present invention provides an ISAC beamforming method mainly for sensing in the target search phase. Compared with the traditional ISAC beamforming scheme in the target search phase, the present invention evaluates the specific sensing performance within the sensing area and improves the sensing performance within the entire sensing area on the premise of meeting the communication performance.

[0109] Referring to the attached Figure 1 drawings, the ISAC beamforming method mainly for sensing in the target search phase includes the following steps:

[0110] S1. Construct a system model to obtain the downlink communication signal of the communication user and the target echo signal at each angular sampling point within the sensing area;

[0111] S2. Analyze and derive the formula for the measurement indicators of the communication performance and sensing performance of the ISAC system based on the downlink communication signal and the target echo signal;

[0112] S3. Based on the analysis and formula derivation, propose an ISAC beamforming method mainly for sensing in the target search phase, and minimize the maximum Cramér-Rao bound (CRB) of target localization within the sensing area on the premise of meeting the communication sum rate, the signal-to-noise ratio (SNR) of the target echo signal at the angular sampling point, and the total transmit power constraint of the base station;

[0113] S4. Use the semidefinite relaxation (SDR) algorithm to solve the ISAC beamforming method mainly for sensing in the target search phase, and compare it with the pure communication scenario to analyze the communication performance loss.

[0114] The system model of the present invention includes a base station, a sensing area, and K communication users. Referring to the attached Figure 2 drawings, the positions of the communication users are randomly distributed. The base station has N antennas, and the spacing between the antennas is d = λ / 2, where λ is the signal wavelength. d k is the distance between the kth communication user and the base station. Assume that the sensing area is a sector area centered on the base station. The entire sensing area is sampled by angle, that is, each angular sampling point within the sensing area has the same distance from the base station but different angles. The sensing area is equally divided into I parts by angle, and the distance from each angular sampling point in the sensing area to the base station is d r .

[0115] Step S1 of the present invention includes:

[0116] The array steering vector at the direction θ of the k-th communication user k is expressed as:

[0117]

[0118] In formula (1), k = 1, 2,..., K, the base station transmits M subcarriers and an OFDM (Orthogonal Frequency Division Multiplexing) signal of L symbols, and the transmitted OFDM symbols satisfy unit symbol energy, that is, |[X k m,l | = 1. The channel attenuation of the communication signal is expressed as where α is the attenuation coefficient. τ k = d k / c represents the signal propagation delay between the k-th communication user and the base station, Δf is the subcarrier spacing, then the downlink communication signal at the k-th communication user is expressed as:

[0119]

[0120] In formula (2), w c,k represents the beamforming weight of the k-th communication user, w r,j represents the beamforming weight of the j-th sensing area. The first part is the downlink user desired signal, the second part is the inter-user interference, the third part is the interference of the sensing signal on the communication signal, and the fourth part is the additive white Gaussian noise, m = 1, 2,..., M and l = 1, 2,..., L.

[0121] The calculation of the echo signal attenuation is expressed as:

[0122]

[0123] In formula (3), P r represents the total transmission power allocated by the base station to the sensing area, G represents the base station antenna gain, and ρ represents the scattering cross-sectional area of the target in the sensing area.

[0124] Let X represent the OFDM signal transmitted by the base station to the sensing area, T z represents the sum of the basic symbol duration and the cyclic prefix duration of OFDM, ν i represents the radial velocity of the target at the i-th angular sampling point in the sensing area relative to the base station. The target echo signal received by the base station at the i-th angular sampling point is expressed as:

[0125] ​

[0126] In Equation (4), the first part is the desired echo signal, the second part is the interference from communication users, and the third part is the additive white Gaussian noise.

[0127] Step S2 of the present invention includes:

[0128] According to the downlink communication signal of the k-th communication user, the SINR (Signal to Interference plus Noise Ratio) of the k-th downlink user is expressed as:

[0129]

[0130] In Equation (5), σ 2 represents the noise variance.

[0131] The calculation of the communication sum rate is expressed as:

[0132]

[0133] According to the target echo signal, the calculation of the SNR of the target echo signal at the i-th angle sampling point received by the base station is expressed as:

[0134]

[0135] The target echo signal at each angle sampling point within the sensing area is obtained.

[0136] The channel parameters of the target are expressed as: The Fisher information matrix of the channel parameters is expressed as:

[0137]

[0138] In Equation (8), E = Y - G is the noise-free part in the echo signal, and D i and D r,i are intermediate variables;

[0139]

[0140] The position parameter is expressed as: ξ po,i = [p i T , ν i , g r T , where p i represents the target coordinates at the i-th angle sampling point;

[0141] The correspondence between the channel parameter ξ ch,i and the position parameter ξ po,i elements is: ​

[0142]

[0143] Channel parameter ξ ch,i With the position parameter ξ po,i There is a conversion from polar coordinates to rectangular coordinates, and the calculation of the Jacobian matrix is expressed as:

[0144]

[0145] In formula (12), a = 1, 2, 3, 4 and b = 1, 2, 3, 4;

[0146] The Fisher information matrix of the position parameter is expressed as:

[0147]

[0148] The CRB of the target position estimation is expressed as:

[0149]

[0150] In formula (14), tr(·) represents the trace operation.

[0151] According to the analysis of the measurement indexes of the communication performance and sensing performance of the ISAC system, the ISAC beamforming method mainly based on sensing in the target search stage is expressed as:

[0152]

[0153] In formula (15), the first constraint is that the sum rate of communication meets the sum rate threshold Γ c requirement, the second constraint is that the sensing signal-to-noise ratio meets the signal-to-noise ratio threshold Γ SNR requirement, and the third constraint is the total transmit power P of the base station t requirement. The goal is to minimize the maximum CRB in the sensing area to meet the sensing accuracy requirement, and ||·||2 represents the two-norm operation.

[0154] The ISAC beamforming method proposed by the present invention has low complexity and is only related to the number of symbols M and the number of subcarriers L of the OFDM signal. The complexity is

[0155] Step S3 of the present invention includes:

[0156] Since the calculation formula (6) of the communication sum rate contains a logarithmic operation, and the variable x passed into the log2(x) function in MATLAB (matrix & laboratory) must be a known value, an error will be reported during the calculation of the sum rate constraint and it cannot be directly calculated using MATLAB. Using the properties of the logarithmic function, the calculation formula (6) of the communication sum rate is converted to:

[0157]

[0158] By using the conversion relationship between the logarithmic function and the exponential function, the communication sum-rate constraint in formula (15) is converted to:

[0159]

[0160] Since the SINR of the k-th communication user is in fractional form and the division operation cannot be directly performed in CVX (Convex), by combining formula (5) and formula (17), the communication sum-rate constraint is further transformed into:

[0161]

[0162] The transmit power constraint in formula (15) is converted to

[0163]

[0164] Since the objective function in formula (15) is non-convex, by introducing an auxiliary variable t, the non-convex objective function can be transformed into a linear constraint to meet the conditions of convex optimization. Set the CRB to a predetermined value reflecting the sensing accuracy requirement. In this case, assume that the CRB threshold t has a fixed value and optimize the sensing beamforming vector while ensuring that the system meets the constraint conditions. Here, t ensures that the requirement is met in the worst-case scenario of sensing accuracy. By introducing the intermediate variable t and according to the above formula derivation, formula (15) is transformed into:

[0165]

[0166] Formula (20) represents the sensing-based ISAC beamforming method in the target search stage.

[0167] Step S4 of the present invention includes:

[0168] Apply the SDR algorithm to solve the sensing-based ISAC beamforming method in the target search stage, and introduce new variables and F in formula (8) ch,i is converted to:

[0169]

[0170] The problem of formula (20) is converted to:

[0171]

[0172] In formula (21) and formula (22), the rank(W c,k ) = 1 and rank(W r) = 1 constraint, solve the problem to obtain W c,k and W r After that, the communication beam weight is calculated as follows:

[0173]

[0174] In formula (23), h k = g k a(θ k );

[0175] Use the eig function in MATLAB to perform EVD (Eigenvalue decomposition) on W r to obtain the eigenvalue U and eigenvector Ω. Then, sort U in descending order to get the sorted eigenvalue vector ε. The sensing beam weight calculation is expressed as:

[0176]

[0177] In formula (24), Ω :,1 is the eigenvector corresponding to the eigenvalue.

[0178] Let w k represent the beam weight of the k-th communication user in the pure communication scenario. Set the intermediate variable Then, the SINR of the k-th communication user in the pure communication scenario is expressed as:

[0179]

[0180] The communication sum rate calculation in the pure communication scenario is expressed as:

[0181]

[0182] The beamforming problem in the pure communication scenario is expressed as:

[0183]

[0184] Use the SDR algorithm to solve the problem of formula (27). The final form of the beamforming problem in the pure communication scenario is expressed as:

[0185]

[0186] The calculation of communication performance loss is expressed as:

[0187]

[0188] Calculate the loss of communication performance through formula (29) to make the loss of communication performance less than 3%, which is within the acceptable range.

[0189] To further prove the effectiveness of the present invention, simulation verification is carried out.

[0190] The system model diagram refers to the appendix Figure 2 .

[0191] Simulation conditions: The signal transmitted by the base station is an OFDM signal, the number of its subcarriers N = 3000, and the number of transmitted symbols L = 32. The position p of the base station b = [0,0] T , the distance d between the k-th communication user and the base station k = 200m, and the distance d between the i-th angular sampling point in the sensing area and the base station r = 200m.

[0192] When the number of antennas is different, with 8 communication users, the variation trend of the maximum CRB of target localization with respect to the SNR threshold Γ SNR is as shown in the appendix Figure 3 . It can be seen from the appendix Figure 3 that when the SNR threshold is the same, as the number of antennas increases, the maximum CRB of target localization decreases. This is because as the number of antennas increases, the antenna array gain of the base station is improved, the echo signal is enhanced, the beamforming ability of the base station is also enhanced, the interference of communication users and noise to sensing is reduced, the parameter estimation accuracy is higher, and thus the maximum CRB of target localization is reduced. When the number of antennas is the same, as the SNR threshold increases, the maximum CRB of target localization decreases. This is because as the SNR threshold increases, the total power allocated by the base station to the sensing area becomes larger and larger, the SNR of the sensing signal is improved, and the maximum CRB of target localization is strongly correlated with the SNR of the sensing signal, resulting in a decrease in the maximum CRB of target localization.

[0193] When the number of antennas is different, the variation trend of the communication performance loss in formula (28) with respect to the SNR threshold is as shown in the appendix Figure 4 . It can be seen from the appendix Figure 4 that under the same SNR threshold, as the number of antennas increases, the communication performance loss decreases. This is because as the number of antennas increases, the sum rate of communication increases, and the ratio between the sum rate C(γ k ) in the ISAC scenario and the sum rate C com (γ c,k ) in the pure communication scenario increases. It can be obtained from formula (28) that the communication performance loss decreases. When the number of antennas is the same, as the SNR threshold increases, the communication performance loss increases. This is because as the SNR threshold increases, the total power allocated by the base station to the sensing area becomes more and more, and due to the total transmission power constraint of the base station, the total power allocated to communication users becomes less and less, resulting in a smaller communication sum rate and thus a gradual increase in the communication performance loss.

[0194] When the number of communication users is different, when there are 16 antennas, the variation trend of the maximum CRB of target positioning with respect to the SNR threshold is as shown in the appendix Figure 5 as follows. From the appendix Figure 5 it can be seen that when the SNR threshold is the same, as the number of communication users increases, the maximum CRB of target positioning increases. This is because as the number of communication users increases, the interference of communication users to the sensing area becomes larger, resulting in an increase in the maximum CRB of target positioning. When the number of communication users is the same, as the SNR threshold increases, the maximum CRB of target positioning decreases. This is because as the SNR threshold increases, the total power allocated by the base station to the sensing area becomes larger and larger, improving the SNR of the sensing signal, thus resulting in a decrease in the maximum CRB of target positioning.

[0195] When the number of communication users is different, the variation trend of communication performance loss with respect to the SNR threshold is as shown in the appendix Figure 6 as follows. From the appendix Figure 6 it can be seen that as the number of communication users increases, the communication performance loss increases. This is because as the number of communication users increases, both the sum rate C(γ k ) in the ISAC scenario and the sum rate C com (γ c,k ) in the pure communication scenario increase, but the interference between communication users also increases, and the ratio between C(γ k ) and C com (γ c,k ) decreases. From Equation (28), it can be concluded that the communication performance loss increases. When the number of communication users is the same, as the SNR threshold increases, the communication performance loss also increases. This is because as the SNR threshold increases, the total power allocated by the base station to the sensing area becomes more and more, and due to the total transmit power constraint of the base station, the total power allocated to communication users becomes less and less, resulting in a smaller sum rate of communication, and thus the communication performance loss gradually increases.

[0196] Compared with the traditional ISAC beamforming method in the target search stage, the proposed sensing-based ISAC beamforming scheme in the target search stage (the directions of communication users are known, the direction of the sensing target is unknown, and only a certain sensing area is known) effectively realizes the coordinated improvement of communication performance and sensing performance in the ISAC system, achieves the desired sensing accuracy, and the communication performance loss is less than 3%, which is within an acceptable range.

[0197] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention shall be covered by the protection scope of the present invention.

Claims

1. An ISAC beamforming method based on perception in the target search phase, characterized in that: Includes steps: S1. Build a system model to obtain the downlink communication signal of the communication user and the target echo signal at each angle sampling point in the sensing area; S2. Analyze and derive formulas for measuring indicators of communication performance and perception performance of the ISAC system according to the downlink communication signal and the target echo signal; S3. Based on the analysis and formula derivation, a perception-based ISAC beamforming method is proposed in the target search phase to minimize the maximum CRB of target positioning in the perception area under the premise of satisfying the communication rate, SNR of the target echo signal at the angle sampling point and the total transmission power constraints of the base station; S4. Use the SDR algorithm to solve the perception-based ISAC beamforming method in the target search phase, compare it with the pure communication scenario, and analyze the communication performance loss.

2. The ISAC beamforming method in the target search phase based on perception according to claim 1, characterized in that: The step S1 comprises: The system model includes a base station, a sensing area and K communication users; The communication users are randomly distributed. The base station has N antennas. The spacing between the antennas is d=λ / 2, where λ is the signal wavelength and d k is the distance between the kth communication user and the base station; Assuming that the sensing area is a sector area with the base station as the center, the entire sensing area is angle sampled, and the sensing area is divided into I equal parts according to the angle. The distance between each angle sampling point in the sensing area and the base station is d r .

3. The ISAC beamforming method in the target search phase based on perception according to claim 2, characterized in that: The step S1 comprises: The direction θ of the kth communication user k The array steering vector at is expressed as: In formula (1), k = 1, 2, ..., K, the base station transmits an OFDM signal with M subcarriers and L symbols, and the transmitted OFDM symbol satisfies the unit symbol energy, that is, [X k ] m,l =1; The channel attenuation of the communication signal is expressed as Among them, α is the attenuation coefficient; τ k =d k / c represents the signal propagation delay between the kth communication user and the base station, Δf is the subcarrier spacing, and the downlink communication signal at the kth communication user is expressed as: In formula (2), w c,k represents the beamforming weight of the kth communication user, w r,j Represents the beamforming weight of the jth sensing area. The first part is the downlink user desired signal, the second part is the inter-user interference, the third part is the interference of the sensing signal to the communication signal, and the fourth part is the additive white Gaussian noise. m=1,2,...,M and l=1,2,...,L.

4. The ISAC beamforming method in the target search phase based on perception according to claim 3, characterized in that: The step S1 comprises: The echo signal attenuation calculation is expressed as: In formula (3), P r represents the total transmission power allocated by the base station to the sensing area, G represents the base station antenna gain, and ρ represents the scattering cross-sectional area of ​​the target in the sensing area; Let X represent the OFDM signal transmitted by the base station to the sensing area, T z represents the sum of the OFDM basic symbol duration and the cyclic prefix time, ν i represents the radial velocity of the target at the i-th angle sampling point in the sensing area relative to the base station. The target echo signal at the i-th angle sampling point received by the base station is expressed as: In formula (4), the first part is the expected echo signal, the second part is the communication user interference, and the third part is the additive Gaussian white noise.

5. The ISAC beamforming method in the target search phase based on perception according to claim 4, characterized in that: The step S2 comprises: According to the downlink communication signal of the k-th communication user, the SINR of the k-th downlink user is expressed as: In formula (5), σ 2 represents the noise variance; The calculation of communication sum rate is expressed as: According to the target echo signal, the calculation of the SNR of the target echo signal at the i-th angle sampling point received by the base station is expressed as: The target echo signal at each angle sampling point in the sensing area is obtained.

6. The ISAC beamforming method in the target search phase based on perception according to claim 5, characterized in that: The step S2 comprises: The target channel parameters are expressed as: The Fisher information matrix of the channel parameters is expressed as: In formula (8), E = YG is the noise-free part of the echo signal, and D i and D r,i is an intermediate variable; The location parameter is expressed as: po,i =[p i T ,ν i ,g r ] T , where p i Represents the target coordinates at the i-th angle sampling point; The channel parameter ξ ch,i With the position parameter ξ po,i The corresponding relationship between the elements is: The channel parameter ξ ch,i With the position parameter ξ po,i There is a conversion from polar coordinates to rectangular coordinates, and the Jacobian matrix calculation is expressed as: In formula (12), a=1,2,3,4 and b=1,2,3,4; The Fisher information matrix of the location parameter is expressed as: F po,i =J i T F ch,i J i (13) The CRB of target position estimation is expressed as: In formula (14), tr(·) represents the trace operation.

7. The ISAC beamforming method based on perception in the target search phase according to claim 6, characterized in that: The step S3 comprises: Based on the analysis of the communication performance and perception performance indicators of the ISAC system, the perception-based ISAC beamforming method in the target search phase is expressed as: In formula (15), the first constraint is that the communication sum rate satisfies the sum rate threshold Γ c The second constraint is that the perceived signal-to-noise ratio satisfies the signal-to-noise ratio threshold Γ SNR The third constraint is the total base station transmission power P t Require; The goal is to minimize the maximum CRB in the perception area to meet the perception accuracy requirements, and ||·||2 represents the two-norm operation.

8. The ISAC beamforming method based on perception in the target search phase according to claim 7, characterized in that: The step S3 comprises: Using the properties of the logarithmic function, the communication sum rate calculation formula (6) is converted into: Using the conversion relationship between logarithmic function and exponential function, the communication and rate constraints in formula (15) are converted to: Combining formula (5) with formula (17), the communication and rate constraints are further transformed into: The transmit power constraint in formula (15) is transformed into CRB is set to a predetermined value that reflects the perception accuracy requirement. Assuming that the CRB threshold t has a fixed value and optimizing the perception beamforming vector, while ensuring that the system meets the constraints, the intermediate variable t is introduced, and formula (15) is transformed into: Formula (20) represents the perception-based ISAC beamforming method in the target search phase.

9. The ISAC beamforming method based on perception in the target search phase according to claim 8, characterized in that: The step S4 comprises: Apply the SDR algorithm to solve the perception-based ISAC beamforming method in the target search phase, introducing new variables and F in formula (8) ch,i Translates to: The problem of formula (20) is transformed into: In formula (21) and formula (22), the rank (W c,k )=1 and rank(W r )=1 constraint, solving the problem yields W c,k and W r After that, the communication beam weight is obtained: In formula (23), h k = g k a(θ k ) Use the eig function in MATLAB to calculate W r Perform EVD decomposition to obtain the eigenvalue U and eigenvector Ω, then sort U in descending order to obtain the sorted eigenvalue vector ε. The perceptual beam weight calculation is expressed as: In formula (24), Ω :,1 is the eigenvector corresponding to the eigenvalue.

10. The ISAC beamforming method based on perception in the target search phase according to claim 9, characterized in that: The step S4 comprises: Let w k Represents the beam weight of the kth communication user in the pure communication scenario, setting the intermediate variable Then the SINR of the kth communication user in the pure communication scenario is expressed as: The communication and rate calculation in the pure communication scenario is expressed as: The beamforming problem in a pure communication scenario is expressed as: Using the SDR algorithm to solve the problem in formula (27), the final form of the beamforming problem in a pure communication scenario is expressed as: The communication performance loss calculation is expressed as: The loss of communication performance is calculated by formula (29) so that the loss of communication performance is within an acceptable range.

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