A Review: Deep Learning Aided Channel Estimation Techniques for Wireless Communication System

Ahmed, Maryam Hamed; Jamel, Thamer M.; Khazaal, Hasan F.

doi:10.1109/iiccit55816.2022.10010434

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…With the help of deep neural networks (DNNs), they are able to learn complex nonlinear channel mapping relationships, thereby achieving more accurate and robust channel estimation. However, deep learning-based channel estimation methods require a large amount of training samples, which are often difficult to obtain in practice [23].…”

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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Section: Introductionmentioning

confidence: 99%