Near-field ISAC system channel estimation and waveform optimization method based on DPSS

Abstract

The invention discloses a near-field ISAC system channel estimation and waveform optimization method based on DPSS. The method comprises the following steps: establishing a near-field ISAC channel and a signal model; designing a channel estimation scheme; designing a joint communication perception optimization problem; based on the designed joint communication perception optimization problem, a joint optimization algorithm under a full-digital architecture is designed; and then designing a joint optimization algorithm under the hybrid beam architecture based on the designed joint communication perception optimization problem. Compared with a DFT codebook structure and a spherical codebook structure, the method has higher energy concentration and lower inter-column correlation, energy leakage can be effectively reduced, channel sparsity is improved, and channel estimation precision is remarkably improved. Besides, the DPSS structure is more suitable for near-field propagation characteristics, and high-precision channel estimation is realized under limited pilot frequency resources in combination with a compressed sensing method, so that the communication and sensing performance is improved in a near-field ISAC system.

Description

Technical Field

[0001] The present invention relates to a method for channel estimation and waveform optimization of a DPSS near-field ISAC system, belonging to the technical field of wireless communication and sensing integration. Background Art

[0002] With the rapid development of the sixth-generation mobile communication technology (6G), traditional wireless communication systems are gradually evolving towards integrated sensing and communication (ISAC) systems. By simultaneously achieving high-quality communication and high-precision sensing tasks on shared spectrum resources and hardware platforms, ISAC systems can effectively improve spectrum utilization, system integration, and service diversity, and have become an important research direction for the next-generation wireless communication systems.

[0003] In an ISAC system, obtaining accurate channel state information (CSI) is a key prerequisite for achieving communication reliability and sensing accuracy. However, most of the existing channel estimation methods are based on far-field propagation models, assuming that the signal wavefront received by each antenna element of the receiving array is a plane wave and is only related to the angle parameter. This modeling method is more applicable in traditional far-field communication scenarios. However, in typical 6G applications such as large-scale antenna arrays, millimeter-wave / terahertz communication frequency bands, and near-field target detection, the system will inevitably enter the near-field propagation paradigm, where electromagnetic waves exhibit spherical wave characteristics, and the channel response depends on both angle and distance parameters. Under these conditions, far-field modeling will lead to serious performance degradation, including problems such as beam energy leakage, sparsity loss, and upper limit of estimation error, thus affecting the overall performance of communication and sensing.

[0004] To address the above problems, existing research has attempted to introduce spherical wave models and high-resolution codebooks to improve near-field channel estimation, but there are still problems such as severe oversampling, high inter-column correlation, and high computational complexity. Discrete Prolate Spheroidal Sequences (DPSS) are introduced to construct a sparse beam codebook due to their good spectral energy concentration and low cross-correlation, and have better near-field channel representation ability and estimation accuracy.

[0005] On the other hand, the ISAC system also faces the problem of joint waveform design, that is, maximizing the sensing accuracy while meeting the communication performance constraints. Traditional communication system design emphasizes throughput and channel capacity, while the sensing system pays more attention to beam directivity and delay resolution ability. There are significant conflicts between the performance indicators of the two when resources are limited. Therefore, an efficient communication-sensing collaborative waveform optimization strategy is urgently needed. In addition, the currently widely used hybrid beamforming architecture, while reducing the hardware complexity, also introduces the problem of non-convex constraints in signal design, further increasing the technical difficulty of joint waveform optimization. Therefore, a solution with efficient near-field channel modeling, low-complexity estimation, and communication-sensing collaborative optimization capabilities is needed to promote the application of the ISAC system in actual complex near-field environments. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method for channel estimation and waveform optimization of a DPSS near-field ISAC system. Through DPSS codebook construction technology, a two-stage near-field channel estimation algorithm, joint waveform optimization modeling, and an alternating iteration algorithm, the dual goals of ensuring communication performance and improving sensing accuracy are achieved, which is applicable to near-field ISAC application scenarios under full-digital and hybrid beamforming architectures.

[0007] The present invention adopts the following technical solutions to solve the above technical problems: A method for channel estimation and waveform optimization of a DPSS near-field ISAC system, the near-field ISAC system includes a base station with a uniform linear array equipped with antenna elements, communication users, and a sensing target. The system operates in the millimeter-wave band. The method includes the following steps: Step 1, establish a near-field ISAC system model, including the channels of each communication user, the channel of the sensing target, the transmission signal of the base station under the near-field full-digital architecture, and the transmission signal model of the base station under the near-field hybrid beam architecture; Step 2, introduce a sparse representation of the channel models of each communication user, introduce a compensation matrix to construct a DPSS codebook, and use the orthogonal matching pursuit algorithm to estimate the channels of each communication user to obtain the near-field channel estimation values of each communication user; Step 3, calculate the overall channel estimation error of all communication users based on the near-field channel estimation values of each communication user, and construct a joint communication and sensing waveform optimization problem based on the overall channel estimation error of all communication users and the lower limit of the estimation error of the sensing target; Step 4, under the near-field full-digital architecture, use the alternating optimization method to solve the joint communication and sensing waveform optimization problem constructed in Step 3 to achieve waveform optimization; Step 5, under the near-field hybrid beamforming architecture, the alternating direction method of multipliers is used to solve the waveform optimization problem for joint communication and sensing constructed in Step 3, achieving waveform optimization.

[0008] Compared with the prior art, the present invention adopting the above technical solutions has the following technical effects: 1. The present invention introduces a sparse modeling method based on discrete prolate spheroidal sequences (DPSS), which can effectively improve the accuracy of channel estimation. Especially in the near-field ISAC system, through the optimization of the DPSS codebook, the energy leakage and inter-column correlation are significantly reduced, thus improving the reconstruction performance of channel estimation.

[0009] 2. The present invention adopts the compressive sensing theory and combines it with the DPSS sparse modeling method. While ensuring the accuracy of channel estimation, it effectively reduces the required pilot resource overhead. This technical advantage enables the system to complete high-precision channel estimation with a lower pilot overhead, greatly improving the spectral efficiency.

[0010] 3. In terms of waveform optimization, the present invention introduces a joint optimization framework for communication and sensing functions, solving the non-convexity and coupling problems of traditional methods in high-dimensional parameter spaces. By adopting the alternating optimization and alternating direction method of multipliers algorithms, more efficient resource allocation can be achieved, optimizing the balance between communication rate and sensing accuracy, and enhancing the overall performance of the system.

[0011] 4. Under near-field conditions, the present invention improves the traditional far-field plane wave model. By combining the DPSS codebook with the near-field channel model, it can more accurately represent the distance and angle information of the target, thus overcoming the limitations of traditional methods that cannot adapt to the propagation characteristics of near-field waves. Description of the Drawings

[0012] Figure 1 is a model diagram of the near-field ISAC system of the present invention; Figure 2 is a flowchart of the present invention; Figure 3 is a graph showing the variation of the channel estimation NMSE of the DPSS codebook of the present invention with the pilot signal-to-noise ratio compared with the DFT and spherical wave codebooks; Figure 4 is a trade-off curve of the single-user single-target communication rate and sensing error (RCRB); Figure 5 is a sparse representation diagram of the near-field channel under different codebooks; Figure 6 is a comparison curve of the root of the Cramer-Rao lower bound (RCRB) for distance estimation between the all-digital architecture and the hybrid architecture; Figure 7 is a comparison curve of the root of the Cramer-Rao lower bound (RCRB) for angle estimation between the all-digital architecture and the hybrid architecture; Figure 8 are the near - field channel estimation error and compression ratio under different codebooks and the performance ratio diagram of iterations; Figure 9 are the near - field channel estimation error and codebook oversampling rate under different codebooks and the performance ratio diagram of iterations. Specific implementation manners

[0013] The following details the implementation manners of the present invention, and the examples of the implementation manners are shown in the drawings. The implementation manners described below with reference to the drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0014] As Figure 1 and Figure 2 shown, the present invention proposes a method for joint optimization of near - field ISAC system channel estimation and waveform based on the DPSS codebook. The near - field ISAC system based on the DPSS codebook includes a base station (Base Station, BS) equipped with a uniform linear array with array elements, and is respectively equipped with communication users and 1 sensing target at the same time. The system operates in the millimeter - wave band with a working frequency of , and the corresponding wavelength is . The element spacing is , then the array length is ; the position coordinates of the th antenna element in the array are , the polar coordinates of the communication user's position are , and the corresponding rectangular coordinates are . The polar coordinates of the sensing user's position are , and the corresponding rectangular coordinates are ; the distance between the communication user and the th antenna element is , the channel of the communication user is , and the channel of the sensing target is ; in the near - field all - digital architecture, the transmitted signal of the BS at time is , and the corresponding transmit covariance matrix is ; in the near - field hybrid beamforming architecture, the transmitted signal of the BS at time is , and the corresponding transmit covariance matrix is .

[0015] The specific process of the method is as follows: Step 1, establish the near - field ISAC channel and signal model; The position coordinates of the th antenna element in the array are expressed as , , and the rectangular coordinates of the positions of the communication user and the sensing user are respectively expressed as , ; The distance between the communication user and the th antenna element is , and the channel corresponding to this user is expressed as , where is the complex attenuation coefficient, and the near-field channel vector of user is expressed as , where is the near-field array response vector; The channel of the sensing target can be expressed as , where is the target complex scattering coefficient, is the near-field array response vector.

[0016] In the ISAC near-field all-digital architecture, the transmit signal of the BS at time is , where is the beamforming vector of user , is the communication signal of user , satisfying , is the transmit signal for sensing, and the corresponding transmit covariance matrix is , where is the sensing signal covariance matrix.

[0017] In the ISAC near-field hybrid beam architecture, the transmit signal of the BS at time is , where is the analog precoding matrix, satisfying the constant modulus constraint , where is the digital precoding vector, is the baseband domain sensing signal, with covariance ; The transmit covariance matrix is .

[0018] Step 2, design the channel estimation scheme; Step 2.1. The user vector in the near field contains distance and angle information. Introduce the sparse representation channel model , is the near-field sparse codebook constructed based on DPSS, is the sparse coefficient vector, represents the set of complex numbers, is a Gaussian noise vector. The goal is to reconstruct under the condition of limited pilot resources , expressed as: , where is the received observation matrix, is the pilot measurement matrix, is the coefficient for controlling sparsity; Step 2.2. Introduce a compensation matrix: , where is the reference distance on the axis, is the position of the th array element; According to multiple near-field angle sampling points in the system, generate the phase-compensated channel response matrix , Perform eigenvalue decomposition on , extract the first main eigenvectors , and construct the DPSS codebook: ; Step 2.3. In the coarse estimation stage, use the traditional spherical wave codebook to construct a polar coordinate network , for each grid point , calculate the matching energy index: , Select the grid point corresponding to the largest as the rough position estimate for the local search range in the fine estimation stage; Step 2.4. In the fine estimation stage, based on the constructed DPSS codebook, use the OMP compressive sensing algorithm to solve: , Use the prior coarse estimation result to continuously iterate and correct until the reconstruction error converges to a predetermined threshold, and then construct an accurate channel .

[0019] Step 3. Design a joint communication and sensing optimization problem; Step 3.1. Through the DPSS two-stage estimation, the near-field channel estimation value of the th communication user is , then its normalized mean square error (NMSE) can be expressed as: , Restore using compressive sensing algorithms such as OMP After that, the estimated channel can be obtained , and the overall channel estimation error is written as: , The error will directly affect the effectiveness of communication beamforming and the accuracy of the Fisher information matrix of the sensing link. Therefore, it should be incorporated into the overall optimization objective; Step 3.2. It is necessary to ensure the quality of service (QoS) of all communication users, with the lower bound of the communication rate as the metric. In the all-digital architecture, the transmit beam can be freely set, and the communication rate is: , where represents the noise vector of the channel of the th communication user; for simplicity in solving, this rate constraint can be transformed into the form of a second-order cone constraint (SOC): , where is the minimum signal-to-noise ratio threshold; Step 3.3. The target performance of the sensing link is evaluated by the lower bound of the target positioning error (CRB). According to the Cramér-Rao lower bound theory, the lower bound of the estimation error of the sensing target is: , where the Fisher information matrix is defined as: , where the parameter ; the signal model depends on the covariance of the sensing signal and the echo channel ; and are linearly related, and minimizing the CRB is a convex function optimization problem with respect to ; Step 3.4. Introduce a weighting factor to control the trade-off between communication and sensing performance, and construct a joint optimization objective function expressed as: , where the first term of the optimization objective ensures the minimization of the channel estimation error, and the second term ensures the reduction of the CRB.

[0020] Step 4. Based on the designed joint communication and sensing optimization problem, design an all-digital architecture joint optimization algorithm; Step 4.1. In the all-digital architecture, the optimization variables include the transmit beam vector of each user , the covariance matrix of the sensing signal , the channel estimation result obtained by the OMP method . To solve this non-convex problem, an alternating optimization method is adopted to iteratively optimize , , respectively; Step 4.2. Perform channel coefficient reconstruction. For each user , fix the pilot observation matrix , the observation matrix , and the codebook , and solve the sparse function: , The OMP algorithm can be used to quickly solve and recover the channel ; Step 4.3. Fix the channel and the sensing covariance , optimize the beam vector , and perform communication beam optimization. The communication rate constraint is: , Introduce auxiliary variables, and this constraint can be transformed into the following SOC form: , The optimization objective is a quadratic function and can be transformed into: , The problem is a standard second-order cone programming (SOCP) problem; Step 4.4. Fix the channel and the beam vector , optimize the sensing covariance , and the optimization problem is transformed into: , where the Fisher information matrix and are linearly related, and the problem is a convex function minimization, that is, a standard semi-definite programming (SDP) problem.

[0021] Step 5. Based on the designed joint communication and sensing optimization problem, design a joint optimization algorithm for the hybrid beam architecture.

[0022] Step 5.1. In the hybrid beam architecture, the transmit signal of the base station is expressed as: , In this architecture, the joint optimization problem is transformed into: , Since and the product of forms the beam vector, resulting in non-convex coupling. Therefore, an auxiliary variable is introduced: , Using the alternating direction method of multipliers (ADMM), the optimization objective is transformed into a block form with equality constraints: , where is the Lagrange multiplier variable introduced in ADMM, used to update the consistency error term, is the penalty factor in the ADMM iteration, used to control the variable update amplitude and convergence speed; the augmented Lagrangian function is expressed as: ; Step 5.2. For each user , fix the analog beam and the auxiliary variable, and update the digital precoder , and the optimization objective is transformed into: , This optimization problem is a standard least squares problem and can directly obtain a closed-form solution: ; Step 5.3. Fix the digital beam and the auxiliary variable, and update the analog beam matrix , making it closest to the target matrix under the constant modulus constraint. The auxiliary matrix is constructed as: , This problem is a projection problem with a constant modulus constraint and can be updated element by element: ; Step 5.4. Fix the analog beam and the digital beam, and update the auxiliary variable , and the goal is to optimize the trade-off between the communication error and the consistency error. The optimization problem is transformed into: , This problem is a weighted quadratic function, and the closed-form solution is: ; Step 5.5. Update the Lagrange multiplier , and adjust the multiplier to make the variable constraint satisfy: , If the gap between the current variables is large, increase the multiplier; if they have tended to be consistent, the change in the multiplier tends to be stable; Step 5.6. After fixing all the beams, update the sensing covariance , to minimize the CRB: , The problem is transformed into an SDP problem.

[0023] The following is a simulation with an embodiment, and the parameter settings of the embodiment are as follows: The transmitter uses 192 transmit antennas, the number of communication users , the user distance is , the user angle is , the base station operating frequency , the corresponding wavelength , the element spacing , the total transmit power budget , the minimum communication rate requirement for each user .

[0024] Figure 3 is the graph of the channel estimation NMSE of the present invention, DFT, and spherical codebook varying with the pilot signal-to-noise ratio. The three curves in the figure: the blue curve represents the DFT codebook; the red curve represents the spherical codebook; the yellow curve represents the DPSS codebook. It can be seen from the figure that at low signal-to-noise ratios, noise dominates the error, and the estimation performances of the three codebooks converge. In the high signal-to-noise ratio region, due to model mismatch, the DFT codebook will have energy leakage, and OMP cannot accurately represent the spherical wave channel with a single DFT beam even in the absence of noise, resulting in a higher error floor; the spherical wave codebook has no mismatch but has column correlation, and OMP may misselect or require multiple atoms to represent the same path, which will also bring errors; the DPSS codebook approximately optimally compresses the channel subspace within the design range, greatly reducing leakage and correlation, so it has a lower error floor.

[0025] Figure 4 is the trade-off curve between the single-user single-target communication rate and the sensing error (RCRB) obtained from simulation verification. It can be seen from the figure that in the single-user single-target scenario, the RCRB of near-field beamforming and traditional far-field beamforming varies with the communication rate. It can be seen that the far-field design reaches performance saturation at low rates and it is difficult to ensure both a high communication rate and sensing accuracy simultaneously; the near-field design provides a better Pareto compromise, maintaining a lower RCRB while meeting the user rate requirements.

[0026] Figure 5It is the sparse representation diagram of the near-field channel under different codebooks obtained through simulation verification. It can be seen from the figure the channel sparse representation forms of the DFT codebook, spherical wave codebook, and the proposed codebook. The traditional DFT codebook has serious energy leakage problems in the near-field region, while the spherical wave codebook can improve this problem to a certain extent through polar domain sampling; at the same time, the proposed DPSS-based feature codebook shows the sparsest representation form among the three codebooks; in addition, different from the DFT and spherical wave codebooks, the non-zero components of the sparse representation of the proposed codebook are mainly concentrated in the first few index positions because singular value decomposition (SVD) always preferentially arranges non-zero singular values.

[0027] Figure 6 and Figure 7 It is the comparison curve of the root of the Cramér-Rao lower bound (RCRB) for distance and angle estimation between the all-digital architecture and the hybrid architecture obtained through simulation verification. It can be seen from the figure that the RCRB increases with the increase of the minimum communication rate, which indicates that there is a trade-off between sensing and communication performance; however, it is worth noting that even when the minimum rate becomes quite high, the RCRB still remains at a low level, which confirms the effectiveness of combining near-field sensing and communication; in addition, at the same communication rate, compared with the all-digital architecture, the hybrid architecture reduces the sensing performance, but its power consumption is much lower.

[0028] Figure 8 is the near-field channel estimation error and compression ratio under different codebooks obtained through simulation verification and the performance ratio diagram of iterations, the codebook oversampling rate is set to 1, and the compression ratio keeps the sizes of the three considered codebooks the same. It can be seen from the figure that the reconstruction accuracy increases with the increase of; at , since the coarse positioning is regarded as one iteration, the method proposed in the present invention shows the same performance as the spherical wave method; at the slight performance degradation is also due to the sudden codebook switching in the second step of the proposed CE scheme; starting from , due to the excellent ability to sparsify the near-field channel using mutually orthogonal codewords, the DPSS-based feature codebook proposed in the present invention is much better than the baseline and converges to the lowest NMSE among the considered codebooks.

[0029] Figure 9 is the near-field channel estimation error and codebook oversampling rate under different codebooks obtained through simulation verification and the performance ratio diagram of iterations, the compression ratio , the oversampling rate . It can be seen from the figure that by increasing , the performance of all the schemes has been significantly improved, and the DPSS-based codebook proposed in the present invention still achieves the highest reconstruction accuracy within sufficient iterations; however, the performance improvement of the DFT and spherical codebooks comes at the cost of a larger codebook size; The increase only affects the positioning accuracy of the first step of the proposed CE scheme. The CE performance achieved by the DPSS-based codebook is much better than the two baselines, even though the codebook size is much smaller.

[0030] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned DPSS-based near-field ISAC system channel estimation and waveform optimization method.

[0031] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the aforementioned DPSS-based near-field ISAC system channel estimation and waveform optimization method.

[0032] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0033] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0034] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions in the flowFigure 1 one process or multiple processes and / or blocks Figure 1 the functions specified in one block or multiple blocks

[0035] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing the steps of the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 the steps of the functions specified in one block or multiple blocks

[0036] The above embodiments are only used to illustrate the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the present invention

Claims

1. Channel Estimation and Waveform Optimization Method for DPSS Near-Field ISAC System, the near-field ISAC system includes a base station equipped with a uniform linear array with antenna elements, The method includes the following steps: Step 1, establish a near-field ISAC system model, including the channels of each communication user, the channels of the sensing targets, the transmitted signals of the base station under the near-field all-digital architecture, and the transmitted signal model of the base station under the near-field hybrid beam architecture; Step 2, introduce a channel model that sparsely represents each communication user, and introduce a compensation matrix to construct a DPSS codebook. Use the orthogonal matching pursuit algorithm to estimate the channels of each communication user to obtain the near-field channel estimation values of each communication user; Step 3, calculate the overall channel estimation error of all communication users based on the near-field channel estimation values of each communication user. Based on the overall channel estimation error of all communication users and the lower bound of the estimation error of the sensing target, construct a waveform optimization problem for joint communication and sensing; Step 4, under the near-field all-digital architecture, use the alternating optimization method to solve the waveform optimization problem for joint communication and sensing constructed in Step 3 to achieve waveform optimization; Step 5, under the near-field hybrid beam architecture, use the alternating multiplier method to solve the waveform optimization problem for joint communication and sensing constructed in Step 3 to achieve waveform optimization.

2. The channel estimation and waveform optimization method for the DPSS near-field ISAC system according to claim 1, characterized in that The specific process of Step 1 is as follows: Near-field communication users whose near-field channel vectors are expressed as: ， wherein, is the complex attenuation coefficient, is the near-field array response vector, is the position polar coordinates of the communication user ; is the distance between the communication user and the transmitting antenna, is the angle between the communication user and the transmitting antenna; , , , is the wavelength, is the channel between the communication user and the th antenna element, is the imaginary unit, is the distance between the communication user and the th antenna element; , is the antenna element spacing; Channel of the perceived target Expressed as: ， wherein, is the target complex scattering coefficient, is the near-field array response vector, denotes conjugate transpose; Under the ISAC near-field all-digital architecture, the base station's transmitted signal at time is as follows: ， wherein, is the beamforming vector of the communication user , is the communication signal of the communication user , satisfying , denotes the mathematical expectation is the transmitted signal for sensing The corresponding transmit covariance matrix is , where is the covariance matrix of the sensing signal part; Under the ISAC near-field hybrid beamforming architecture, the base station's transmitted signal at time is as follows: ， Among them, is the analog beamforming matrix, satisfying the constant modulus constraint , is an element in is the digital precoder, is the baseband domain sensing signal with covariance , denotes a complex Gaussian distribution; The corresponding transmit covariance matrix is .

3. The method for channel estimation and waveform optimization of the DPSS-based near-field ISAC system according to claim 2, wherein The specific process of Step 2 is as follows: Step 2.1, introduce a channel model that sparsely represents each communication user: ， Among them, is a near-field sparse codebook constructed based on DPSS, is a sparse coefficient vector, is a Gaussian noise vector; Reconstruction The objective function is as follows: ， Among them, is the signal-to-noise ratio of the communication user , is the received observation matrix is the pilot measurement matrix is the coefficient for controlling the sparsity; Step 2.2, introducing a compensation matrix : ， Among them, is the reference distance on the axis, is the position of the nth antenna element; according to the preset near-field angle sampling points in the system, generate the phase-compensated channel response matrix , and construct the autocorrelation matrix as: ， Pair Perform eigenvalue decomposition to obtain the eigenvectors corresponding to all eigenvalues , and construct a DPSS codebook: ； Step 2.3, using a spherical wave codebook Construct a polar coordinate network , for each grid point on , calculate the matching energy index: ， For the grid points of the spherical wave codebook, select the grid point corresponding to the largest as the rough position estimate for the local search range in the fine estimation stage; Step 2.4, Fine Estimation Phase, Based on the DPSS Codebook Constructed in Step 2.2 , the orthogonal matching pursuit algorithm is used to solve and iteratively correct the estimated sparse coefficient vector. When the reconstruction error converges to a preset threshold, the iteration stops, and the communication user channel is reconstructed using the estimated sparse coefficient vector obtained in the last iteration, that is ; where the estimated sparse coefficient vector is expressed as: 。 4. The channel estimation and waveform optimization method for the DPSS near-field ISAC system according to claim 3, characterized in that In Step 3, the objective function of the waveform optimization problem for joint communication and sensing is as follows: ， wherein, is a weighting factor, , is the overall channel estimation error of all communication users, is the lower limit of the estimation error of the sensing target, is the maximum transmit power of the base station, is the covariance matrix of the sensing signal, is the lower limit of the communication rate; is the Fisher information matrix, The elements in are as follows: ， Among them, is the probability density function of the random variable , and are the -th and components of the parameter vector to be estimated; For a communication user communication rate, and: ， wherein, is the beamforming vector of the communication user , and is the noise vector of the channel of the communication user .

5. The channel estimation and waveform optimization method for the DPSS near-field ISAC system according to claim 4, characterized in that The specific process of Step 4 is as follows: Step 4.1, for communication users , fixed pilot observation matrix , observation matrix , near-field sparse codebook , use the orthogonal matching pursuit algorithm to solve the following sparse function for channel coefficient reconstruction: ， Restore the channel according to the reconstructed channel coefficients ; Step 4.2, fix the channel estimation result and the covariance matrix of the sensing signal , optimize the beamforming vector , and the waveform optimization problem for joint communication and sensing constructed in Step 3 is transformed into the following second-order cone programming problem: ， Solve the above second-order cone programming problem to obtain the optimal to minimize the overall channel estimation error of all communication users; Step 4.3, fix the channel estimation result and the beamforming vector , optimize the sensing signal covariance matrix , and the waveform optimization problem for joint communication and sensing constructed in Step 3 is transformed into the following semidefinite programming problem: ， Solve the above semi-definite programming problem to obtain the optimal lower bound of the sensing target estimation error.

6. The channel estimation and waveform optimization method for the DPSS near-field ISAC system according to claim 4, characterized in that The specific process of Step 5 is as follows: Step 5.1, under the near-field hybrid beam architecture, the waveform optimization problem for joint communication and sensing constructed in Step 3 is transformed into: ， Among them, is the transmission power matrix; Introduce auxiliary variables , and use the alternating direction method of multipliers to transform the optimization objective into a block form with equality constraints: ， Among them, is the penalty factor, is the Lagrange multiplier variable; Step 5.2, for communication users , fix the analog beamforming matrix and the auxiliary variable , update the digital precoder , and the optimization objective is transformed into a least squares problem: ， Solve the above least squares problem to obtain a closed-form solution: ， Among them, is the digital precoder for the communication user in the th iteration, is the auxiliary variable in the th iteration, is the Lagrange multiplier in the th iteration; Step 5.3, fix the digital precoder and the auxiliary variable , update the analog beam matrix , construct the auxiliary matrix : ， The optimization objective is transformed into a projection problem with a constant modulus constraint, and is updated element by element: , is an element in is an element in; Step 5.4, fix the analog beamforming matrix and the digital precoder , update the auxiliary variable , and the optimization objective is transformed into a weighted quadratic function: ， Solve the above weighted quadratic function to obtain a closed-form solution: ； Step 5.5, if , where is the error tolerance threshold, then update the Lagrange multiplier through the following formula and return to Step 5.2 for the th iteration: ， wherein, is the Lagrange multiplier variable of the communication user at the -th iteration, is the auxiliary variable at the -th iteration, is the analog beamforming matrix at the -th iteration; Otherwise, no longer update the Lagrange multiplier variable, and the iteration ends; Step 5.6, after the iteration ends, update the perceptual signal covariance matrix using the following formula :[[]]END]] ， Minimize the lower bound of the estimation error of the sensing target.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the DPSS-based near-field ISAC system channel estimation and waveform optimization method according to any one of claims 1 to 6.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the DPSS-based near-field ISAC system channel estimation and waveform optimization method according to any one of claims 1 to 6.

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