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

Canghai Song, Xiao Zhou (), Chengyou Wang and Zhun Ye
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Canghai Song: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Xiao Zhou: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Chengyou Wang: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Zhun Ye: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China

Mathematics, 2023, vol. 11, issue 15, 1-22

Abstract: 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.

Keywords: channel estimation; orthogonal frequency division multiplexing (OFDM); adaptive frame statistics (AFS); double threshold (DT) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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