A perception-aided adaptive threshold channel estimation method

Abstract

The application relates to the field of the sixth generation mobile communication technology, and particularly discloses a sensing-assisted adaptive threshold channel estimation method, which comprises the following steps: step S1: a base station acquires a scatterer echo signal through radar sensing, estimates scatterer angle information, and constructs a sensing-assisted orthogonal sparse base; step S2: the base station sends a pilot signal to a communication user, the user performs compression and quantization processing after receiving the pilot signal, and feeds back a feedback signal to the base station; step S3: adaptive threshold iteration support detection is started, parameter initialization, sparse coefficient estimation, adaptive threshold updating and support set iteration optimization are completed; step S4: whether the iteration stopping condition is met is judged, if yes, step S5 is executed, and if not, step S3 is returned for a new round of iteration; and step S5: according to the final sparse coefficient estimation value, a channel estimation result of an integrated sensing and communication system is restored and calculated. The application improves the precision and robustness of downlink channel estimation of an ISAC system.

Description

Technical Field

[0001] This invention belongs to the field of sixth-generation mobile communication (6G) technology, specifically relating to sparse channel estimation technology in integrated sensing and communication (ISAC) systems, and particularly to a sensing-assisted adaptive threshold channel estimation method. Background Technology

[0002] 6G wireless networks require the seamless integration of communication, sensing, and computing to support ubiquitous intelligence, high reliability, and ultra-low latency service requirements. Integrated Sensing and Communication (ISAC), as a core enabling technology of 6G, can achieve high-precision environmental perception and high-speed data transmission through unified spectrum and hardware resources, and has important application value in fields such as autonomous driving, intelligent manufacturing, and target tracking.

[0003] Accurate Channel State Information (CSI) estimation is a crucial prerequisite for reliable communication and precise sensing in ISAC systems. Traditional Frequency Division Duplex (FDD) systems rely on downlink pilot transmission and uplink CSI feedback, resulting in high feedback overhead. In contrast, sensing-assisted channel estimation methods utilize the scatterer geometry information (angle, time delay) extracted by the base station from radar echo signals. By combining the joint sparsity of sensing and communication channels, the downlink channel is represented as a sparse combination of steering vectors, transforming channel estimation into a sparse signal recovery problem. This enables CSI estimation with no or low feedback, significantly reducing overhead.

[0004] The development of compressed sensing (CS) technology has provided an effective solution for sparse channel estimation in high-dimensional ISAC systems. Various sparse signal recovery algorithms have emerged in existing technologies, mainly categorized as follows: 1) Algorithms based on Sparse Bayesian Learning (SBL), which improve estimation performance through structural priors, but rely on accurate prior knowledge and complex probabilistic reasoning and matrix operations. Performance degrades significantly when prior knowledge is unreliable or computational resources are limited; 2) Greedy algorithms such as Orthogonal Matching Pursuit (OMP), which are simple to implement but have non-optimal greedy selection strategies, limiting recovery accuracy without sparse priors; 3) Iterative Support Detection (ISD) algorithms, which achieve sparse signal recovery through iterative estimation and support set updates. They require no external prior knowledge, have moderate computational complexity, and are low-complexity algorithms suitable for practical communication scenarios.

[0005] However, traditional ISD algorithms have inherent technical flaws: their detection process relies on a fixed threshold, which cannot adaptively match the dynamic propagation conditions and noise levels of the wireless channel. When there are weak signal paths or high noise levels in the channel, the fixed threshold can easily lead to missed detections of effective paths or false detection of noise as signals, thereby causing error propagation during the iteration process and resulting in a sharp deterioration in the normalized mean square error (NMSE) performance. This instability is particularly prominent in ISAC scenarios with low signal-to-noise ratio (SNR) or rapidly changing channels.

[0006] Furthermore, traditional linear estimation algorithms such as Minimum Mean Square Error (MMSE) and Zero Forcing (ZF) do not utilize the sparsity of the channel, resulting in a high error floor in high-dimensional ISAC systems. Random Detection (RD) algorithms suffer from high detection randomness, making it difficult to guarantee estimation accuracy. In summary, existing sparse channel estimation algorithms cannot simultaneously meet the comprehensive requirements of ISAC systems for weak path detection capability, noise suppression effect, robustness, and low complexity. Therefore, a sparse channel estimation algorithm adapted to the dynamic channel environment of ISAC is urgently needed. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a perception-assisted adaptive threshold channel estimation method. It utilizes the geometric information of scatterers obtained by radar perception to construct an orthogonal sparse basis, transforms channel estimation into a sparse coefficient recovery problem, and achieves accurate estimation of sparse coefficients through adaptive threshold iteration support detection, thereby improving the accuracy and robustness of downlink channel estimation in the ISAC system.

[0008] The objective of this invention is achieved through the following technical solution: a perception-assisted adaptive threshold channel estimation method, based on an integrated sensing and communication system, wherein the integrated sensing and communication system includes a base station configured with a uniform rectangular array, the base station comprising... One vertical antenna element and There are [number] horizontal antenna elements, with a total of [number] antennas. The base station is used for downlink communication with single-antenna communication users, and at the same time, it obtains the geometric information of scatterers in the wireless environment through radar sensing function.

[0009] The method includes the following steps:

[0010] Step S1: The base station obtains the echo signal of the scatterer through radar sensing, estimates the angle information of the scatterer, and constructs a sensing-assisted orthogonal sparse basis;

[0011] Step S2: The base station sends pilot signals to the communication user. After receiving the signals, the user performs compression and quantization processing and sends the feedback signal back to the base station.

[0012] Step S3: Start adaptive threshold iterative support detection, complete parameter initialization, sparse coefficient estimation, adaptive threshold update and support set iterative optimization;

[0013] Step S4: Determine whether the iteration stopping condition is met. If it is met, proceed to step S5. If not, return to step S3 to start a new round of iteration.

[0014] Step S5: Based on the final sparse coefficient estimate, the channel estimation result of the integrated sensing and communication system is calculated and restored.

[0015] The beneficial effects of the present invention are: (1) The present invention combines the characteristics of sensing and communication fusion of ISAC system, uses the scatterer angle information obtained by radar sensing to construct sensing-assisted orthogonal sparse basis, models the downlink channel as a sparse signal, transforms the channel estimation problem into a sparse coefficient recovery problem, and combines the compression quantization processing of the user end to greatly reduce CSI feedback overhead and improve the system spectrum utilization.

[0016] (2) The present invention designs an adaptive threshold iteration support detection process, which dynamically updates the detection threshold based on the number of iterations, instantaneous signal energy and noise standard deviation, replacing the fixed threshold of the traditional algorithm, effectively balancing the detection of sparse components corresponding to weak scatterers and noise suppression, and solving the problems of weak path missed detection, noise false detection and iteration error propagation caused by fixed threshold.

[0017] (3) The adaptive threshold iteration support detection process of the present invention only includes basic operations such as least squares estimation, norm calculation, and threshold update. It does not require complex matrix inversion or probabilistic reasoning, has low computational complexity, and is easy to implement on existing base station hardware platforms. At the same time, it sets dual stopping conditions during the iteration process to avoid invalid iteration and improve the algorithm's running efficiency.

[0018] (4) This invention transforms the environmental information sensed by radar into structured constraints for channel estimation, making the recovery of sparse coefficients more targeted. In typical ISAC scenarios with low signal-to-noise ratio, few pilots, and a high proportion of weak paths, it can effectively improve the accuracy of channel estimation and enhance the robustness of system channel estimation. Attached Figure Description

[0019] Figure 1 This is a flowchart of a perception-assisted adaptive threshold channel estimation method according to the present invention;

[0020] Figure 2 These are curves showing the normalized mean square error of each channel estimation method as a function of the signal-to-noise ratio. Detailed Implementation

[0021] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0022] like Figure 1As shown, this invention addresses the issue that in traditional ISAC system downlink channel estimation, large-scale antenna arrays lead to a dramatic increase in CSI dimension, and full feedback results in significant spectral overhead and transmission delay. Existing sparse channel estimation algorithms, such as the Iterative Support Detection (ISD) algorithm, use a fixed threshold for support set detection, which is prone to weak path misses and noise false detections in dynamic channel environments. Furthermore, high-precision sparse recovery algorithms have high computational complexity, making them difficult to implement in engineering. To solve these problems, this invention utilizes the radar sensing capabilities of the ISAC system to acquire the geometric information of scatterers in the wireless environment and construct a sensing-assisted orthogonal sparse basis, transforming channel estimation into a sparse coefficient recovery problem and reducing feedback overhead. Simultaneously, an adaptive threshold iterative support detection algorithm is designed to dynamically adjust the detection threshold, balancing weak component detection and noise suppression. This ensures estimation accuracy while controlling computational complexity, making it suitable for practical application scenarios of ISAC systems.

[0023] Furthermore, considering the resource sharing characteristics of sensing and communication in the ISAC system, this invention multiplexes radar detection signals and communication pilot signals. The base station simultaneously completes the reception of scatterer echo signals and the transmission of communication pilot signals through a co-located uniform rectangular array (URA), achieving deep integration of sensing and communication without the need for additional hardware and spectrum resources, thus improving the utilization rate of system resources. Moreover, this invention sets a dual stopping condition of maximum number of iterations and support set convergence during the iterative support detection process. This avoids the algorithm from iterating infinitely due to channel mutations and can terminate the iteration in time when the support set converges, reducing invalid calculations and improving the algorithm's operating efficiency.

[0024] Specifically, in an Integrated Sensing and Communication (ISAC) system, there exists a base station configured with a uniform rectangular array (URA), the base station comprising... One vertical antenna element and There are [number] horizontal antenna elements, with a total of [number] antennas. This base station is used for downlink communication with single-antenna users, and simultaneously acquires geometric information of scatterers in the wireless environment through radar sensing. The base station transmits detection signals via radar sensing and acquires echo signals from M radar-detectable scatterers. It uses a two-dimensional multiple signal classification (2D-MUSIC) algorithm to estimate the elevation and azimuth angles of the scatterers. Based on this angle information, a sensing-aided dictionary matrix is ​​constructed, and orthogonal sparse bases are obtained through singular value decomposition (SVD). U∈CN×M, the downlink channel vector from the base station to the communication user is represented as... ,in It is a sparse coefficient vector, whose non-zero elements are effective scatterers that significantly contribute to the communication channel; the base station sends pilot signals of length to the communication user during the downlink training phase. pilot matrix The pilot signal received by the communication user is ,in To obtain the feedback signal, which is additive complex Gaussian noise, the user compresses or quantizes the received pilot signal. Then, it is transmitted back to the base station via a finite-rate feedback link. The base station is based on an orthogonal sparse base. We construct a measurement matrix and transform the channel estimation problem into a perception-assisted sparse channel estimation problem.

[0025] Referring to Figure 1, a channel estimation method for a perception-assisted integrated sensing and communication (ISAC) system is described in detail below:

[0026] Step S1: Construct a perceptual-assisted orthogonal sparse basis and sparsify the channel;

[0027] The base station first activates its radar sensing function, transmitting downlink detection signals into the wireless environment. It then captures the radar echo signals reflected from various scatterers using a co-located uniform rectangular array (URA). The received echo at time t is:

[0028] ;

[0029] in For the first The reflection coefficient of each scatterer For the first The elevation angle of arrival of the echo from each scatterer represents the angle between the incident direction and the horizontal plane of the array. For the first The azimuth angle of arrival of the echo from each scatterer represents the angle between the projection of the incident direction onto the horizontal plane and the array reference axis. It is Gaussian noise. For a unit matrix that matches the dimension of the received signal, The variance is the preset Gaussian noise. The guide vector for a two-dimensional array is expressed as follows:

[0030] ;

[0031] In the formula The vertical steering component, being the Kronecker product, is:

[0032] ;

[0033] The horizontal guidance component is:

[0034] ;

[0035] and These represent the vertical and horizontal antenna element spacing, respectively. For carrier wavelength, collect The echo matrix was obtained from the second observation. ;

[0036] Based on echo matrix A two-dimensional multi-signal classification algorithm is used to estimate the elevation and azimuth angles of arrival for all scatterers: First, the sample covariance matrix of the echo signal is calculated:

[0037] ;

[0038] In the formula This represents the conjugate transpose operation. The number of snapshots, i.e., the number of observations, is then used to calculate the covariance matrix. Perform eigenvalue decomposition, sort the eigenvalues ​​in descending order, and then... The eigenvectors corresponding to the large eigenvalues ​​span the signal subspace. The eigenvectors corresponding to the remaining eigenvalues ​​span the noisy subspace. Finally, construct the two-dimensional spatial spectral function.

[0039] ;

[0040] A two-dimensional mesh traversal search is performed within the physical range of pitch and azimuth angles to find the angle corresponding to the spatial spectrum peak. That is, the first Estimated elevation and azimuth angles of each scatterer.

[0041] Construct a perception-aided dictionary matrix based on the estimated angle information. Substitute each of the scatterer angle pairs estimated by the two-dimensional multiple signal classification algorithm into the two-dimensional array steering vector formula:

[0042] ;

[0043] The column vector of the guiding vector corresponding to each scatterer is calculated;

[0044] In the formula For vertical guiding components, For horizontal guiding components;

[0045] By concatenating the steering vectors corresponding to all scatterers column by column, a perception-aided dictionary matrix is ​​constructed:

[0046] ;

[0047] right Perform singular value decomposition: Take the unitary matrix Orthogonal sparse bases as a sensory aid;

[0048] The downlink channel vector from the base station to the communication user is represented as: ,in It is a sparse coefficient vector whose non-zero elements are effective scatterers that make a significant contribution to the communication channel.

[0049] Step S2: Pilot transmission feedback and sparse coefficient recovery problem transformation. After completing the sparse basis construction, the base station enters the downlink training phase, sending preset pilot signals to communication users. The pilot signals received by the users, after channel transmission and noise superposition, can be represented as follows: In the formula For pilot matrix, The pilot signal is additive complex Gaussian noise in the communication channel. After receiving the pilot signal, the user compresses or quantizes it to reduce the data transmission volume of the feedback link and generate a feedback signal. The signal is then transmitted back to the base station via a finite-rate feedback link. Upon receiving the feedback signal, the base station uses the orthogonal sparse basis obtained in step 1... and the transmitted pilot matrix Construct a measurement matrix, and then convert the sparse coefficients The recovery problem is transformed into a standard compressed sensing problem, with the optimization objective and constraints as follows:

[0050] ;

[0051] in This represents the noise tolerance level.

[0052] Step S3: Perform adaptive threshold iterative support detection. The base station first completes the initialization settings of iteration-related parameters, including the initial value of the iteration count, the initial estimated value of the sparse coefficients, the initial support set, and key parameters such as scaling parameters, maximum number of iterations, and noise standard deviation. After initialization, the base station starts the adaptive threshold iterative support detection process, first in the current support set... Within the range, for feedback signals Performing least squares estimation yields the estimated sparse coefficients for the current iteration, expressed as follows:

[0053] ;

[0054] in, Indicates the first The support set of the next iteration is the set of indices of the non-zero elements in the sparse coefficients determined in the current iteration. For the submatrix of the support set corresponding to the measurement matrix; It is constructed jointly by the downlink pilot matrix and the sensing-assisted orthogonal sparse basis, i.e. Set the estimated sparsity coefficients outside the support set to 0, i.e. .

[0055] Then, the squared 2-norm of the current sparse coefficient estimate is calculated to obtain the instantaneous signal energy. Then, the adaptive detection threshold is dynamically updated by combining the number of iterations and the noise standard deviation. Finally, the effective sparse components are selected based on the updated threshold, and the support set is updated. This completes the dynamic adjustment for this round of iterations.

[0056] Step S4: Iteration Stop Condition Judgment and Iteration Control. After each iteration, the base station will perform a real-time judgment on whether the iteration process meets the preset stop condition. If the stop condition is met, the subsequent channel restoration step will be entered; otherwise, it will return to step 3 to continue a new round of iteration. The iteration stop condition mainly includes two types: one is that the number of iterations reaches the preset maximum number of iterations. Another type is where the support sets obtained in two consecutive iterations are completely identical, i.e. This indicates that the support set has stabilized and the sparse coefficient estimation results have converged. If the maximum number of iterations is reached but the support set has not yet converged, to avoid the algorithm getting stuck in infinite iteration, the sparse coefficient estimates obtained in this iteration will be used directly. This serves as the final estimate of the sparsity coefficients.

[0057] Step S5: Channel estimation result restoration and output. After the iteration terminates, the base station obtains the final sparse coefficient estimate. Combining the sparsed channel model $$h=U\tilde{\gamma}$$ constructed in step 1, the final sparse coefficient estimate $$\hat{\gamma}$$ is substituted into the model for linear operation to restore the downlink channel estimation result of the ISAC system, which is expressed as follows: The base station uses this channel estimation result as the final output for subsequent key downlink communication processes such as precoding design and resource scheduling, thereby completing the entire channel estimation process of the perception-assisted ISAC system.

[0058] In the embodiments of this application, the feasibility of a sparse Bayesian signal reconstruction method based on a multiple measurement vector model is verified through simulation experiments.

[0059] Simulation parameters: This invention uses a uniform rectangular array (URA) with 8 antennas (Nv=8) configured as the base station. The base station coordinates are The coordinates of the communication user are Total number of scatterers in the channel environment Number of effective scatterers The distance between the scatterer and the base station is Uniformly distributed within, pitch angle azimuth Both the sensing channel and the communication channel employ path loss and complex Gaussian reflection coefficient models, with pilot length... The signal-to-noise ratio (SNR) range covers the low to high SNR range. Normalized mean square error (NMSE) is used as the performance evaluation metric.

[0060] Simulation results: Figure 2 The figures show the NMSE versus SNR curves for various channel estimation methods. It can be seen that the Adaptive Threshold Iterative Support Detection (AT-ISD) method proposed in this invention outperforms traditional channel estimation methods such as ISD, OMP, MMSE, RD, and ZF. This is because traditional ISD methods use a fixed threshold, which is difficult to effectively detect weak scattering paths in low to medium signal-to-noise ratio environments. In contrast, the method in this invention dynamically adjusts the threshold using instantaneous signal energy and noise variance, achieving a better balance between detection sensitivity and noise suppression. Furthermore, this method fully utilizes the structured sparse prior information provided by radar sensing. Compared to algorithms such as OMP and MMSE that do not fully exploit sparse characteristics, it can more accurately recover channel information, thus exhibiting superior channel estimation performance.

[0061] The above describes the specific embodiments and simulation verifications of the present invention. It should be noted that those skilled in the art can clearly understand that the above embodiments and simulations of the unlicensed joint active user and data detection method of the present invention are only used to illustrate and verify the rationality and feasibility of the method, and are not intended to limit the method of the present invention. Although the embodiments effectively illustrate and describe the present invention, many variations exist without departing from the spirit of the present invention. Those skilled in the art can make various corresponding changes or modifications according to the method of the present invention without departing from the spirit and essence of the method, but these corresponding changes or modifications all fall within the protection scope claimed by the method of the present invention.

Claims

1. A sensing-assisted adaptive threshold channel estimation method, based on an integrated sensing and communication system, characterized in that: The integrated sensing and communication system includes a base station configured with a uniform rectangular array, the base station comprising... One vertical antenna element and There are [number] horizontal antenna elements, with a total of [number] antennas. The base station is used for downlink communication with single-antenna communication users, and at the same time, it obtains the geometric information of scatterers in the wireless environment through radar sensing function. The method includes the following steps: Step S1: The base station obtains the echo signal of the scatterer through radar sensing, estimates the angle information of the scatterer, and constructs a sensing-assisted orthogonal sparse basis; Step S2: The base station sends pilot signals to the communication user. After receiving the signals, the user performs compression and quantization processing and sends the feedback signal back to the base station. Step S3: Start adaptive threshold iterative support detection, complete parameter initialization, sparse coefficient estimation, adaptive threshold update and support set iterative optimization; Step S4: Determine whether the iteration stopping condition is met. If it is met, proceed to step S5. If not, return to step S3 to start a new round of iteration. Step S5: Based on the final sparse coefficient estimate, the channel estimation result of the integrated sensing and communication system is calculated and restored.

2. The perception-assisted adaptive threshold channel estimation method according to claim 1, characterized in that: Step S1 includes: S101. The base station transmits downlink detection signals to the wireless environment and receives radar echo signals from the scatterer through a co-located uniform rectangular array. The received echo at time t is: ； in For the first The reflection coefficient of each scatterer For the first The elevation angle of arrival of the echo from each scatterer represents the angle between the incident direction and the horizontal plane of the array. For the first The azimuth angle of arrival of the echo from each scatterer represents the angle between the projection of the incident direction onto the horizontal plane and the array reference axis. It is Gaussian noise. For a unit matrix that matches the dimension of the received signal, The variance is Gaussian noise. The guide vector for a two-dimensional array is expressed as follows: ； In the formula The vertical steering component is the Kronecker product: ； The horizontal guidance component is: ； and These represent the vertical and horizontal antenna element spacing, respectively. For carrier wavelength, collect The echo matrix was obtained from the second observation. ; S102. Based on echo matrix A two-dimensional multi-signal classification algorithm is used to estimate the elevation and azimuth angles of arrival for all scatterers: First, the sample covariance matrix of the echo signal is calculated: ； In the formula This represents the conjugate transpose operation. The number of snapshots, i.e., the number of observations, is then used to calculate the covariance matrix. Perform eigenvalue decomposition, sort the eigenvalues ​​in descending order, and then... The eigenvectors corresponding to the large eigenvalues ​​span the signal subspace. The eigenvectors corresponding to the remaining eigenvalues ​​span the noisy subspace. Finally, construct the two-dimensional spatial spectral function. ； A two-dimensional mesh traversal search is performed within the physical range of pitch and azimuth angles to find the angle corresponding to the spatial spectrum peak. That is, the first Estimated elevation and azimuth angles of each scatterer; S103. Construct a perception-aided dictionary matrix based on the estimated angle information. Substitute each of the scatterer angle pairs estimated by the two-dimensional multi-signal classification algorithm into the two-dimensional array steering vector formula: ； The column vector of the guiding vector corresponding to each scatterer is calculated; In the formula For vertical guiding components, For horizontal guiding components; By concatenating the steering vectors corresponding to all scatterers column by column, a perception-aided dictionary matrix is ​​constructed: ； right Perform singular value decomposition: Take the unitary matrix Orthogonal sparse bases as a sensory aid; S104. The downlink channel vector from the base station to the communication user is represented as follows: ,in It is a sparse coefficient vector whose non-zero elements are effective scatterers that make a significant contribution to the communication channel.

3. The perception-assisted adaptive threshold channel estimation method according to claim 1, characterized in that: Step S2 includes: S201. The base station sends pilot matrices to communication users during the downlink training phase. The pilot signal received by the communication user is: in It is additive Gaussian noise; S202. Communication user's received pilot vector The feedback signal is obtained by compression or quantization. ; S203. The communication user sends the feedback signal through a limited-rate feedback link. The data is transmitted to the base station, which constructs a measurement matrix, transforming the sparse coefficient recovery problem into a standard compressed sensing problem. ； in This represents the noise margin level, whose value is related to the standard deviation of the communication channel noise. Positive correlation.

4. The perception-assisted adaptive threshold channel estimation method according to claim 1, characterized in that: Step S3 includes: S301. Initialize iteration parameters: number of iterations Initial sparse coefficient estimate Initial support set Set scaling parameters Maximum number of iterations Signal length Standard deviation of communication channel noise ; S302. In the current support set Feedback signal Performing least squares estimation yields the current estimated values ​​for the sparse coefficients: ； in, Indicates the first The support set of the next iteration is the set of indices of the non-zero elements in the sparse coefficients determined in the current iteration. For the submatrix of the support set corresponding to the measurement matrix; It is constructed jointly by the downlink pilot matrix and the sensing-assisted orthogonal sparse basis, i.e. Set the estimated sparsity coefficients outside the support set to 0, i.e. ; S303. Calculate the squared 2-norm of the current sparse coefficient estimate to obtain the instantaneous signal energy: ； The adaptive detection threshold is dynamically updated based on the number of iterations, instantaneous signal energy, and noise standard deviation. ； S305. Select sparse coefficient estimates whose absolute values ​​are greater than the current threshold. The index is included in the support set, and the update yields: And order .

5. The perception-assisted adaptive threshold channel estimation method according to claim 1, characterized in that: In step S4, the iteration stopping condition is: when the number of iterations reaches the maximum number of iterations. Or the support sets of two adjacent iterations satisfy When the iteration converges, stop the iteration; if the number of iterations reaches... However, if the support set does not converge, the iteration is stopped directly, and the 1st iteration is... The sparse coefficient estimates obtained in the next iteration are used as the final estimates.

6. The perception-assisted adaptive threshold channel estimation method according to claim 1, characterized in that: Step S5 includes: estimating the sparse coefficients obtained from the final iteration. Substitute into the channel model The downlink channel estimate of the system is calculated and the channel estimate result is output.

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