A communication and sensing integrated system sensing aided beamforming method

Abstract

The application provides a communication and sensing integrated system sensing assisted beam forming method, and relates to the technical field of communication; the method comprises the following steps: S10, an ISAC base station sends a reference signal, the reference signal comprises a reference signal sent omnidirectionally in an initialization stage and a reference signal sent directionally according to a preset encoding vector f k in a sensing assistance stage; S20, an ISAC receiving end utilizes a return signal to acquire parameters of surrounding targets or users based on orthogonal matching pursuit (OMP) and multiple signal classification (MUSIC) respectively; S30, the distance and angle of the targets or users are obtained by using a sensing time slot, and the encoding vector f k is calculated; S40, steps S20 and S30 are repeated to realize sensing assisted cellular communication beam forming; the application has the beneficial effects that high-precision position information of the ISAC receiving end can be sensed, and more accurate sending beams can be formed.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and more specifically, to a sensing-assisted beamforming method for an integrated communication and sensing system. Background Technology

[0002] Cellular communication systems are a crucial architecture for wireless communication, currently in their fifth generation and impacting all aspects of people's lives. Therefore, research into next-generation cellular communication systems is receiving increasing attention. Integrated Sensing and Communication (ISAC) is considered one of the key areas for next-generation wireless communication systems, as communication and sensing share many similarities in signal processing and system architecture. Adding communication functionality to a sensing system expands its application scenarios. Conversely, incorporating sensing functionality into a communication system significantly enhances its performance and makes it more intelligent. Communication systems can further improve their communication performance by utilizing high-precision positioning information of surrounding targets and the environment obtained through sensing.

[0003] The environmental information obtained by the integrated communication and sensing system can be applied to many aspects, one of the more important being beamforming. Codebook-based or mathematical optimization are commonly used beamforming methods. However, codebook-based beamforming often produces suboptimal beams, failing to maximize communication performance. Mathematically optimized beamforming requires frequent channel estimation to obtain channel state information, resulting in significant communication overhead and performance degradation. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a sensing-assisted beamforming method for an integrated communication and sensing system.

[0005] The technical solution adopted by this invention to solve its technical problem is: a sensing-assisted beamforming method for an integrated communication and sensing system, the improvement of which is that the method includes the following steps:

[0006] S10, the ISAC base station transmits a reference signal, which includes a reference signal transmitted omnidirectionally during the initialization phase and a reference signal transmitted according to a preset coding vector f during the sensing assistance phase. k A directional reference signal;

[0007] The S20 and ISAC receivers use echo signals to obtain parameters of surrounding targets or users based on Orthogonal Matching Pursuit (OMP) and Multi-Signal Classification (MUSIC) algorithms, respectively, through a joint sensing method. These parameters include distance, speed, and angle.

[0008] S30. Obtain the distance and angle of the target or user using the sensing time slots, and calculate the encoded vector f.k Alternatively, the encoding vector f can be calculated by combining historical information obtained through sensing methods with kinematic equations. k ;

[0009] S40. Repeat steps S20 and S30 to achieve sensing-assisted cellular communication beamforming.

[0010] Furthermore, the ISAC base station includes M transmitting antennas and N receiving antennas, and the number of surrounding targets or users is K;

[0011] The ISAC base station and the ISAC receiver operate based on a 5G positioning reference signal, which is an OFDM signal with Q OFDM symbols and P subcarriers, where Δf is the subcarrier spacing.

[0012] In a certain time slot, the distance, velocity, and angle of the k-th target or user are respectively d k v k θ k The signal vectors of K sensing / communication users on the p-th subcarrier and q-th OFDM symbol are:

[0013] S p,q =[s p,q,1 ,…,s p,q,K ] T ;

[0014] The transmitted signal is:

[0015] X p,q =FS p,q ;

[0016] Wherein, the k-th column of F is the encoding vector f of the k-th sensing / communication user. k ;

[0017]

[0018] Where, p k p represents the transmit power of the k-th beam in the current OFDM symbol period. k and Inversely proportional, The distance between the k-th sensing / communication user and the base station predicted by the sensing algorithm, and the total power. e represents the natural constant, j is the imaginary unit, π is the mathematical constant pi, and f c Let d be the carrier frequency, d0 be the antenna spacing, and sin(.) denote the sine function. Let c be the location of the k-th sensing / communication user predicted by the sensing algorithm, M be the speed of light, M be the number of transmitting antennas, and T be the transpose symbol. This indicates the square root operation.

[0019] Furthermore, during the initialization phase:

[0020]

[0021] Furthermore, in step S20, the joint sensing method based on orthogonal matching pursuit (OMP) includes the following steps:

[0022] S201. Divide the angle, distance, and velocity domains into a uniform grid:

[0023] θ = [θ1, θ2, ..., θ U ];

[0024] d = [d1, d2, ..., d V ];

[0025] v = [v1, v2, ..., v W ];

[0026] S202. The received signal from the ISAC base station is converted into the sparse domain as follows:

[0027]

[0028] Where p and q represent the p-th carrier and the q-th OFDM symbol, respectively, ∑ represents the summation symbol, U, V, and W are the number of grids in the angle, range, and velocity domains, respectively, and v w d v and θ u These are the grid points w, v, and u in the velocity, distance, and angle domains, respectively, and T. s Let be the symbol period, Δf be the subcarrier spacing, c be the speed of light, and a be the speed of light. M (θ u ), a N (θ u ) are the transmit and receive steering vectors, respectively, T is the transpose symbol, and X is the transmit and receive steering vector. p,q To send a signal, α u,v,w Let n be the target reflectance coefficient. p,q This is the noise vector;

[0029] For the Kronecker product, θ, d, and v are the meshing vectors for the angle, distance, and velocity domains, respectively, and α is the sparse vector. U It is an identity matrix of size U;

[0030] S203, Collect y on P subcarriers and Q OFDM symbols p,q The result obtained by piecing together:

[0031] y = Zα + n;

[0032] in,

[0033]

[0034]

[0035] Furthermore, in step S20, the orthogonal matching pursuit (OMP) joint sensing method also includes the following steps:

[0036] The Orthogonal Matching Pursuit (OMP) method is used to solve sparse models, including the following steps:

[0037] S204, Input received signal y, dictionary matrix Z, number of surrounding targets or users is K;

[0038] S205. Cycle from 1 to K, and let the residual r0 = y;

[0039] S206. Find the residual r k-1 The subscript λ of the column with the largest inner product in the dictionary matrix;

[0040] S207, Update index set Λ k =Λ k-1 ∪λ and the set of atoms Φ of the dictionary matrix k =[Φ k-1 ,Z[:,λ]];

[0041] S208, Using the least squares method to find

[0042]

[0043] Where W represents the sparse representation coefficients of the signal. This is an estimate of W;

[0044] S209, Update Residuals

[0045] S210. Determine if k > K. If yes, stop iterating; otherwise, continue the loop. Output the index set Λ. K That is what I wanted;

[0046] S211. Convert the distance, velocity, and angle of K targets using the following formula:

[0047] speed

[0048] distance Where mod(A,B) represents the remainder of A / B;

[0049] angle

[0050] Among them, Λ K This is the index set output by the iterative process, where U and V are the number of grids in the angle and distance domains, respectively. This indicates a round-down operation.

[0051] Furthermore, in step S20, the acquisition of parameters of surrounding targets or users based on the multi-signal classification algorithm MUSIC and the joint sensing method includes:

[0052] S2001, MUSIC Angle Measurement: Input received signal y p,q K estimated angles are obtained.

[0053] S2002, Spatial Filtering: Constructing the corresponding beamforming vector using K estimated angles:

[0054]

[0055] K filtered signals y' are obtained p,q,k ;

[0056] S2003, Point Division: Constructing the sending guide vector:

[0057]

[0058] Filtered signal y' p,q,k Dot removal Get y p,q,k ;

[0059] S2004, 2D-MUSIC algorithm for distance and velocity measurement, for K y p,q,k The 2D-MUSIC algorithm is used sequentially for distance and velocity measurement to achieve joint distance, velocity and angle measurement.

[0060] Furthermore, in step S30, the kinematic equation is:

[0061]

[0062] Among them, t s To sense the time slot interval, The distance to the k-th target or user obtained in the t-th sensing time slot. For the k-th target or user speed obtained in the t-th sensing time slot, The radial acceleration of the k-th target or user in the current time slot. The location of the k-th target or user obtained in the t-th sensing time slot. This represents the tangential velocity of the k-th target or user in the current time slot.

[0063] Furthermore, the received signal of the ISAC receiver is:

[0064]

[0065] Among them, T s For the symbol period, d k v k These represent the target's distance and speed, respectively, n. p,q The noise vector and the steering vector are:

[0066] Target reflectance λ c For wavelength, σ RCS This represents the radar cross section.

[0067] Furthermore, the signal received at the k-th communication user is:

[0068]

[0069] The user receiver steering vector is:

[0070]

[0071] R represents the number of user receiving antennas;

[0072] The signal-to-interference-plus-noise ratio (SIR) at the k-th communication user on the p-th carrier and q-th symbol is calculated as follows:

[0073]

[0074] in, H is the conjugate transpose symbol, -1 indicates the inverse operation, and d k v k θ k f represents the distance, speed, and angle of the k-th target or user, respectively. i This is the encoding vector for the i-th beam;

[0075] The achieved rates are:

[0076]

[0077] Among them, I R It is an identity matrix of size R.

[0078] The beneficial effects of this invention are as follows: This invention proposes a sensing-assisted beamforming method for an integrated communication sensing system, which can sense high-precision position information of the ISAC receiver and form a more accurate transmission beam. Predicting the receiver position using historical sensing velocity and angle information between two sensing time slots avoids frequent channel estimation, greatly reduces channel state information tracking overhead, and enables faster beam failure recovery. Attached Figure Description

[0079] Figure 1 This is a flowchart illustrating a sensing-assisted beamforming method for an integrated communication and sensing system according to the present invention.

[0080] Figure 2 This is a schematic diagram of the MIMO-OFDM ISAC system model in this invention.

[0081] Figure 3 This is a simulation result diagram of sparse vector based on the orthogonal matching pursuit (OMP) joint sensing method in this invention.

[0082] Figure 4 This is a simulation diagram of distance and velocity estimation based on the orthogonal matching pursuit (OMP) joint sensing method in this invention.

[0083] Figure 5 This is a simulation diagram of the distance angle estimation based on the orthogonal matching pursuit (OMP) joint sensing method in this invention.

[0084] Figure 6 This is a comparison chart of the root mean square error (RMSE) curves of the embodiments and comparative examples in this invention.

[0085] Figure 7 This is a comparison chart of the achievable rate curves of the embodiments and comparative examples in this invention. Detailed Implementation

[0086] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0087] The following will clearly and completely describe the concept, specific structure, and technical effects of the present invention in conjunction with embodiments and accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention. Furthermore, all connections / linkages involved in the patent do not simply refer to direct contact between components, but rather to the ability to form a better connection structure by adding or reducing connecting accessories according to specific implementation conditions. The various technical features in this invention can be combined interactively without contradicting each other.

[0088] Example 1

[0089] Reference Figure 1 As shown, this invention discloses a sensing-assisted beamforming method for an integrated communication and sensing system, which is based on... Figure 2 The illustrated MIMO-OFDM ISAC system model includes an ISAC base station and an ISAC receiver. The ISAC base station has M transmit antennas and N receive antennas, and the number of surrounding targets or users is K. Both the ISAC base station and the ISAC receiver operate based on a 5G positioning reference signal, which is an OFDM signal with Q OFDM symbols and P subcarriers, where Δf is the subcarrier spacing.

[0090] In this embodiment, a sensing-assisted beamforming method for an integrated communication and sensing system according to the present invention includes steps S10-S40, wherein:

[0091] S10, the ISAC base station transmits a reference signal, which includes a reference signal transmitted omnidirectionally during the initialization phase and a reference signal transmitted according to a preset coding vector f during the sensing assistance phase. k A directional reference signal;

[0092] In a certain time slot, the distance, velocity, and angle of the k-th target or user are respectively d k v k θ k The signal vectors of K sensing / communication users on the p-th subcarrier and q-th OFDM symbol are:

[0093] S p,q =[s p,q,1 ,…,s p,q,K ] T ;

[0094] The transmitted signal is:

[0095] X p,q =FS p,q ;

[0096] Where the k-th column of F is the k-th sensing / communication user, f k ;

[0097]

[0098] Where, p k p represents the transmit power of the k-th beam in the current OFDM symbol period. k and Inversely proportional, The distance between the k-th sensing / communication user and the base station predicted by the sensing algorithm, and the total power. e represents the natural constant, j is the imaginary unit, π is the mathematical constant pi, and f c Let d be the carrier frequency, d0 be the antenna spacing, and sin(.) denote the sine function. Let c be the location of the k-th sensing / communication user predicted by the sensing algorithm, M be the speed of light, M be the number of transmitting antennas, and T be the transpose symbol. This indicates the square root operation.

[0099] Therefore, during the initialization phase:

[0100]

[0101] The S20 and ISAC receivers use echo signals to obtain parameters of surrounding targets or users based on Orthogonal Matching Pursuit (OMP) and Multi-Signal Classification (MUSIC) algorithms, respectively, through a joint sensing method. These parameters include distance, speed, and angle.

[0102] In this embodiment, step S20, based on the Orthogonal Matching Pursuit (OMP) joint sensing method, includes the following steps:

[0103] S201. Divide the angle, distance, and velocity domains into a uniform grid:

[0104] θ = [θ1, θ2, ..., θ U ];

[0105] d = [d1, d2, ..., d V ];

[0106] v = [v1, v2, ..., v W ];

[0107] S202. The received signal from the ISAC base station is converted into the sparse domain as follows:

[0108]

[0109] Where p and q represent the p-th carrier and q-th OFDM symbol, respectively, ∑· represents the summation symbol, U, V, and W are the number of grids in the angle, range, and velocity domains, respectively, and v w d v and θ u These are the grid points w, v, and u in the velocity, distance, and angle domains, respectively, and T. s Let be the symbol period, Δf be the subcarrier spacing, c be the speed of light, and a be the speed of light. M (θ u ), a N (θ u ) are the transmit and receive steering vectors, respectively, T is the transpose symbol, and X is the transmit and receive steering vector. p,q To send a signal, α u,v,w Let n be the target reflectance coefficient. p,q This is the noise vector;

[0110] α u,v,w Let α be the target reflection coefficient, and α be a sparse vector.

[0111] I U It is an identity matrix of size U;

[0112] S203, Collect y on P subcarriers and Q OFDM symbols p,q The result obtained by piecing together:

[0113] y = Zα + n;

[0114] in,

[0115]

[0116] In step S20, the joint sensing method based on orthogonal matching pursuit (OMP) further includes the following steps:

[0117] The Orthogonal Matching Pursuit (OMP) method is used to solve sparse models, including the following steps:

[0118] S204, Input received signal y, dictionary matrix Z, number of surrounding targets or users is K;

[0119] S205. Cycle from 1 to K, and let the residual r0 = y;

[0120] S206. Find the residual r k-1 The subscript λ of the column with the largest inner product in the dictionary matrix;

[0121] S207, Update index set Λ k =Λ k-1 ∪λ and the set of atoms Φ of the dictionary matrix k=[Φ k-1 ,Z[:,λ]];

[0122] S208, Using the least squares method to find

[0123]

[0124] Where W represents the sparse representation coefficients of the signal. This is an estimate of W;

[0125] S209, Update Residuals

[0126] S210. Determine if k > K. If yes, stop iterating; otherwise, continue the loop. Output the index set Λ. K That is what I wanted;

[0127] S211. Convert the distance, velocity, and angle of K targets using the following formula:

[0128] speed

[0129] distance Where mod(A,B) represents the remainder of A / B;

[0130] angle

[0131] Among them, Λ K This is the index set output by the iterative process, where U and V are the number of grids in the angle and distance domains, respectively. This indicates a round-down operation.

[0132] In addition, in this embodiment, step S20, which involves obtaining parameters of surrounding targets or users based on the multi-signal classification algorithm MUSIC and the joint sensing method, includes:

[0133] S2001, MUSIC Angle Measurement: Input received signal y p,q K estimated angles are obtained.

[0134] S2002, Spatial Filtering: Constructing the corresponding beamforming vector using K estimated angles:

[0135]

[0136] K filtered signals y' are obtained p,q,k ;

[0137] S2003, Point Division: Constructing the sending guide vector:

[0138]

[0139] Filtered signal y' p,q,k Dot removal Get y p,q,k ;

[0140] S2004, 2D-MUSIC algorithm for distance and velocity measurement, for K y p,q,k The 2D-MUSIC algorithm is used sequentially for ranging and velocity measurement to achieve joint ranging, velocity measurement, and angle measurement. In this embodiment, 2D is used to represent the carrier and OFDM symbol dimensions.

[0141] S30. Calculate the encoding vector f using historical information obtained through sensing methods and kinematic equations. k ;

[0142] In this embodiment, in step S30, the kinematic equation is:

[0143]

[0144]

[0145] Among them, t s To sense the time slot interval, The distance to the k-th target or user obtained in the t-th sensing time slot. For the k-th target or user speed obtained in the t-th sensing time slot, The radial acceleration of the k-th target or user in the current time slot. The location of the k-th target or user obtained in the t-th sensing time slot. This represents the tangential velocity of the k-th target or user in the current time slot.

[0146] Furthermore, the received signal of the ISAC receiver is:

[0147]

[0148] Among them, T s For the symbol period, d k v k These represent the target's distance and speed, respectively, n. p,q The noise vector and the steering vector are:

[0149] Target reflectance λ c For wavelength, σ RCS Radar cross section;

[0150] The signal received at the k-th communication user is:

[0151]

[0152] The user receiver steering vector is:

[0153]

[0154] R represents the number of user receiving antennas;

[0155] The signal-to-interference-plus-noise ratio (SIR) at the k-th communication user is calculated as follows:

[0156]

[0157] in, H is the conjugate transpose symbol, -1 indicates the inverse operation, and d k v k θ k f represents the distance, speed, and angle of the k-th target or user, respectively. i This is the encoding vector for the i-th beam;

[0158] The achieved rates are:

[0159]

[0160] Among them, I R It is an identity matrix of size R.

[0161] S40. Repeat steps S20 and S30 to achieve sensing-assisted cellular communication beamforming.

[0162] Based on this, this invention proposes a sensing-assisted beamforming method for an integrated communication and sensing system, which can sense high-precision position information of the ISAC receiver and form a more accurate transmission beam. Between two sensing time slots, using historical sensing velocity and angle information to predict the receiver's position avoids frequent channel estimation, greatly reduces channel state information tracking overhead, and enables faster beam failure recovery.

[0163] Example 2

[0164] This invention provides a sensing-assisted beamforming method for an integrated communication and sensing system. In this embodiment, the difference from Embodiment 1 is that in step S30, the distance and angle of the target or user are obtained using the sensing time slot, and the coded vector f is calculated. k .

[0165] Example 3

[0166] In this embodiment, the present invention provides a sensing-assisted beamforming method for an integrated communication and sensing system. This embodiment is based on Embodiment 1, and the effect is verified by simulation. The parameters are set as follows: system carrier frequency of 5 GHz, bandwidth of 1.92 MHz, number of carriers of 64 (P = 64), subcarrier spacing of 30 kHz, and number of OFDM symbols of 18 (Q = 18). The ISAC base station has 16 transmitting antennas (M = 16), 32 receiving antennas (N = 32), 4 user terminal antennas (R = 4), and the number of sensing targets / users is 3 (K = 3).

[0167] First, with a signal-to-noise ratio of 5dB and the distances, velocities, and angles of the three targets being (46m, 62m, 76m), (-26m / s, 6m / s, 16m / s), and (-18°, 4°, 18°), respectively, the simulation results based on orthogonal matching pursuit (OMP) joint sensing are as follows: Figure 3 , 4 As shown in Figure 5. Figure 3 The simulation results of sparse vectors obtained based on orthogonal matching pursuit (OMP) sensing are given, where there are numerical values ​​at the index positions with targets. Figure 4 The simulation results of distance and velocity sensing by orthogonal matching tracking OMP are shown in the figure. The marked points in the figure represent the distance and velocity of the actual target, and the white blocks represent the sensed distance and velocity. Figure 5 The figure shows the simulation results of distance and angle sensing by OMP. As can be seen from the figure, OMP based on orthogonal matching tracking can jointly sense the target's distance, velocity, and angle.

[0168] Secondly, the RMSE of the joint sensing method based on orthogonal matching pursuit (OMP) of this invention is compared with that of the multi-signal classification algorithm (MUSIC). Figure 6 As shown in the figure, compared with the multi-signal classification algorithm MUSIC, the joint sensing method based on orthogonal matching pursuit (OMP) in this invention can avoid the loss of accuracy in distance and velocity estimation caused by spatial filtering, and the accuracy of distance and velocity estimation is greatly improved. At the same time, its angle estimation accuracy is also improved to a certain extent.

[0169] Finally, the achievable rates of the sensing-assisted beamforming method of this invention are compared with those of beamforming methods based on codebooks and random beams, such as... Figure 7 As shown, BF1 represents the beamforming vector f. k The beamforming vector f is calculated by combining historical information about the target obtained from perception with kinematic equations. kThe target / user distance and angle are calculated using the joint sensing algorithm obtained from the previous sensing time slot. As shown in the figure, compared to codebook-based and random beamforming, the four sensing-assisted beamforming methods significantly improve the communication system's rate and approach the optimal rate when the transmission power is high and the channel information is known. Specifically, the random beamforming method achieves a maximum achievable rate of approximately 5.56 bps / channel, the codebook method approximately 10.27 bps / channel, and the sensing-assisted beamforming method approximately 15.67 bps / channel.

[0170] In summary, this invention, targeting the MIMO-OFDMISAC scenario, employs a joint sensing method for distance, velocity, and angle based on orthogonal matched pursuit (OMP). Compared to commonly used MUSIC algorithms, it avoids spatial filtering, significantly improving the estimation accuracy of distance and velocity, and also enhancing the accuracy of angle estimation. Simultaneously, it utilizes the cellular system's reference signal as the active sensing signal, achieving real-time joint sensing without compromising cellular communication performance. Furthermore, it employs a sensing-assisted beamforming method to form a more accurate transmission beam and avoid frequent channel estimation, further improving the system's communication rate.

[0171] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A sensing-assisted beamforming method for an integrated sensing and communication (ISAC) system, characterized in that, The method includes the following steps: S10, the ISAC base station transmits a reference signal, which includes a reference signal transmitted omnidirectionally during the initialization phase and a reference signal transmitted according to a preset coding vector f during the sensing assistance phase. k A directional reference signal; The ISAC base station includes M transmitting antennas and N receiving antennas, and the number of integrated communication and sensing users is K. The ISAC base station and the ISAC receiver operate based on a 5G positioning reference signal, which is an OFDM signal with Q OFDM symbols and P subcarriers, where Δf is the subcarrier spacing. In a certain time slot, the distance, speed, and angle of the kth integrated communication and sensing user are respectively d k v k θ k The signal vectors of K integrated communication and sensing users on the p-th subcarrier and q-th OFDM symbol are: S p,q =[s p,q,1 ,…,s p,q,K ] T ; The transmitted signal is: X p,q =FS p,q ; Wherein, the k-th column of F is the encoding vector f of the k-th integrated communication and sensing user. k ; Where, p k p represents the transmit power of the k-th beam in the current OFDM symbol period. k and Inversely proportional, The distance between the k-th integrated communication and sensing user and the base station predicted by the sensing algorithm, and the total power. e represents the natural constant, j is the imaginary unit, π is the mathematical constant pi, and f c Let d be the carrier frequency, d0 be the antenna spacing, and sin(.) denote the sine function. Let c be the location of the k-th integrated communication and sensing user predicted by the sensing algorithm, M be the speed of light, M be the number of transmitting antennas, and T be the transpose symbol. This indicates the square root operation; The S20 and ISAC receivers utilize echo signals and, based on Orthogonal Matching Pursuit (OMP) and Multi-Signal Classification (MUSIC) algorithms respectively, jointly employ sensing methods to acquire parameters of the integrated communication and sensing user. These parameters include distance, speed, and angle. In step S20, the joint sensing method based on orthogonal matching pursuit (OMP) includes the following steps: S201. Divide the angle, distance, and velocity domains into a uniform grid: θ=[θ1,θ2,...,θ U ]; d=[d1,d2,...,d V ]; v=[v1,v2,...,v W ]; S202. The received signal from the ISAC base station is converted into the sparse domain as follows: Where p and q represent the p-th carrier and the q-th OFDM symbol, respectively, ∑ represents the summation symbol, U, V, and W are the number of grids in the angle, range, and velocity domains, respectively, and v w d v and θ u These are the grid points w, v, and u in the velocity, distance, and angle domains, respectively, and T. s Let be the symbol period, Δf be the subcarrier spacing, c be the speed of light, and a be the speed of light. M (θ u ), a N (θ u ) are the transmit and receive steering vectors, respectively, T is the transpose symbol, and X is the transmit and receive steering vector. p,q To send a signal, α u,v,w Let n be the target reflectance coefficient. p,q This is the noise vector; For the Kronecker product, θ, d, and v are the meshing vectors for the angle, distance, and velocity domains, respectively, and α is the sparse vector. U It is an identity matrix of size U; S203, Collect y on P subcarriers and Q OFDM symbols p,q The result obtained by piecing together: y = Zα + n; in, In step S20, the joint sensing method based on orthogonal matching pursuit (OMP) further includes the following steps: The Orthogonal Matching Pursuit (OMP) method is used to solve sparse models, including the following steps: S204, Input received signal y, dictionary matrix Z, number of integrated communication and sensing users is K; S205. Cycle from 1 to K, and let the residual r0 = y; S206. Find the residual r k-1 The subscript λ of the column with the largest inner product in the dictionary matrix; S207, Update index set Λ k =Λ k-1 ∪λ and the set of atoms Φ of the dictionary matrix k =[Φ k-1 ,Z[:,λ]]; S208, Using the least squares method to find Where W represents the sparse representation coefficients of the signal. This is an estimate of W; S209, Update Residuals k = k + 1; S210. Determine if k > K. If yes, stop iterating; otherwise, continue the loop. Output the index set Λ. K That is what I wanted; S211. Convert the distance, speed, and angle of K integrated communication and sensing users using the following formula: speed distance Where mod(A,B) represents the remainder of A / B; angle Among them, Λ K This is the index set output by the iterative process, where U and V are the number of grids in the angle and distance domains, respectively. This indicates a round-down operation; In step S20, the acquisition of parameters of the integrated communication and sensing user based on the multi-signal classification algorithm MUSIC and the joint sensing method includes: S2001, MUSIC Angle Measurement: Input received signal y p,q K estimated angles are obtained. S2002, Spatial Filtering: Constructing the corresponding beamforming vector using K estimated angles: K filtered signals y' are obtained p,q,k ; S2003, Point Division: Constructing the sending guide vector: Filtered signal y' p,q,k Dot removal Get y p,q,k ; S2004, 2D-MUSIC algorithm for distance and velocity measurement, for K y p,q,k The 2D-MUSIC algorithm is used sequentially for ranging and velocity measurement to achieve joint ranging, velocity measurement and angle measurement. S30. Obtain the distance and angle of the integrated communication and sensing user using the sensing time slot, and calculate the coded vector f. k Alternatively, the encoding vector f can be calculated by combining historical information obtained through sensing methods with kinematic equations. k ; In step S30, the kinematic equations are: Among them, t s To sense the time slot interval, The distance to the k-th integrated communication and sensing user obtained in the t-th sensing time slot is... The speed of the k-th integrated communication and sensing user obtained in the t-th sensing time slot. Let K be the radial acceleration of the k-th integrated communication and sensing user in the current time slot. The location of the k-th integrated communication and sensing user is obtained in the t-th sensing time slot. The tangential velocity of the k-th integrated communication and sensing user in the current time slot; The received signal of the ISAC receiver is: Among them, T s For the symbol period, d k v k These represent the distance and speed of the user in the integrated communication and sensing system, n p,q The noise vector and the steering vector are: Target reflectance λ c For wavelength, σ RCS Radar cross section; The signal received at the kth integrated communication and sensing user site is: The user receiver steering vector is: R represents the number of user receiving antennas; The signal-to-interference-plus-noise ratio (SIR) at the k-th user site on the p-th carrier and q-th symbol is calculated as follows: in, H is the conjugate transpose symbol, -1 indicates the inverse operation, and d k v k θ k f represents the distance, speed, and angle of the k-th integrated communication and sensing user, respectively. i This is the encoding vector for the i-th beam; The achieved rates are: Among them, I R It is an identity matrix of size R; S40. Repeat steps S20 and S30 to achieve sensing-assisted cellular communication beamforming.

2. The sensing-assisted beamforming method for an integrated communication and sensing system according to claim 1, characterized in that, During the initialization phase:

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