Sparsity Domain Smoothing Based Thresholding Recovery Method for OFDM Sparse Channel Estimation

Bahonar, Mohammad Hossein; Zefreh, Reza Ghaderi; Amiri, Rouhollah

doi:10.1109/icee55646.2022.9827343

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…In the sixth step, the mean square error of the residual is calculated until it is less than the threshold . Where the threshold is derived based on prior information of the received signal [19], while the threshold used in reference [23] is chosen as an appropriate value to serve as the stopping criterion for the sparse recovery algorithm.and then the algorithm ends, completing the channel estimation. The specific Algorithm1 process is summarized as follows.…”

Section: The Proposed Swomp Channel Estimation Methods For Joint Hybr...mentioning

confidence: 99%

Low Complexity Hybrid-Field Channel Estimation Based on Simultaneous Weighted OMP Algorithm in Extreme Large-Scale MIMO Systems

Huang,

Zhang,

Jiang

2024

IEEE Access

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Extreme large-scale multiple-input multiple-output (XL-MIMO) is one of the key technologies for future 6G communications. Channel estimation plays a crucial role in XL-MIMO systems, as accurate Channel State Information (CSI) is essential for effective signal transmission. The existing channel estimation methods mainly distinguish between far-field channel estimation and near-field channel estimation. In the case where the sparsity of the channel is known, the traditional Orthogonal Matching Pursuit (OMP) algorithm is relied upon to estimate the hybrid-field channel in XL-MIMO systems.To overcome these limitations, in this paper, we propose a joint hybrid-field channel estimation scheme and adopt the Simultaneous Weighted Orthogonal Matching Pursuit (SWOMP) algorithm to effectively address these issues. Specifically, to more effectively estimate the hybrid-field channel, we propose a joint channel estimation approach that no longer distinguishes between far-field and near-field channel estimation methods. In the case where the sparsity of the hybrid-field channel is unknown, we employ the SWOMP algorithm to accurately estimate the channel state information. Furthermore, we substitute the Sherman-Morrison-Woodbury transform for the matrix inversion operation in the SWOMP algorithm, which does not reduce computational complexity but provides a novel approach to matrix inversion. Based on this, finally, we further propose a low-complexity SWOMP algorithm based on the Richardson method transformation. Simulation results demonstrate that the proposed approach can obtain more accurate channel state information compared to traditional methods in XL-MIMO systems.INDEX TERMS Extreme large-scale multiple-input multiple-output,channel estimation,hybrid-field, lowcomplexity simultaneous weighted orthogonal matching pursuit

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Section: The Proposed Swomp Channel Estimation Methods For Joint Hybr...mentioning

confidence: 99%

Low Complexity Hybrid-Field Channel Estimation Based on Simultaneous Weighted OMP Algorithm in Extreme Large-Scale MIMO Systems

Huang,

Zhang,

Jiang

2024

IEEE Access

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“…To some extent, the MSE degradation problem caused by the estimation of priori channel information is overcome. Bahonar et al [38] proposed a sparse recovery method based on sparse domain smoothing. It is mainly divided into three parts: time domain residue computation, sparsity domain smoothing, and adaptive thresholding sparsifying.…”

Section: Introductionmentioning

confidence: 99%

A Double-Threshold Channel Estimation Method Based on Adaptive Frame Statistics

Song,

Zhou,

Wang

et al. 2023

Mathematics

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Channel estimation is an important module to enhance the performance of orthogonal frequency division multiplexing (OFDM) systems. However, the presence of a large amount of noise in time-varying multipath fading channels significantly affects the channel estimation accuracy and thus the recovery quality of the received signals. Therefore, this paper proposes a double-threshold (DT) channel estimation method based on adaptive frame statistics (AFS). The method first adaptively determines the number of statistical frames based on the temporal correlation of the received signals, and preliminarily detects the channel structure by analyzing the distribution characteristics of multipath sampling points and noise sampling points during adjacent frames. Subsequently, a multi-frame averaging technique is used to expand the distinction between multipath and noise sampling points. Finally, the DT is designed to better recover the channel based on the preliminary detection results. Simulation results show that the proposed adaptive frame statistics-double-threshold (AFS-DT) channel estimation method is effective and has better performance compared with many existing channel estimation methods.

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Target Detection and Interference Cancellation in Passive Radar Sensors via Group-Sparse Regression

2023

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Sparsity Domain Smoothing Based Thresholding Recovery Method for OFDM Sparse Channel Estimation

Cited by 4 publications

References 24 publications

Low Complexity Hybrid-Field Channel Estimation Based on Simultaneous Weighted OMP Algorithm in Extreme Large-Scale MIMO Systems

Low Complexity Hybrid-Field Channel Estimation Based on Simultaneous Weighted OMP Algorithm in Extreme Large-Scale MIMO Systems

A Double-Threshold Channel Estimation Method Based on Adaptive Frame Statistics

Target Detection and Interference Cancellation in Passive Radar Sensors via Group-Sparse Regression

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