A Low Complexity Learning-based Channel Estimation for OFDM Systems with Online Training

TL;DR

This paper introduces a fast, low-complexity machine learning-based channel estimator for OFDM systems that trains online using data constructed from LS estimates, improving robustness and adaptability over traditional methods.

Contribution

It proposes a novel online training data construction method for machine learning-based channel estimation in OFDM systems, reducing training time and data requirements.

Findings

The method achieves robust performance under practical imperfections.

It outperforms conventional MMSE and existing ML-based estimators.

Two training schemes offer flexibility with or without extra pilot overhead.

Abstract

In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel construction approach is verified by theoretical analysis and simulations. Based on this construction approach, two alternative training data generation schemes are proposed. One scheme transmits additional block pilot symbols to create training data, while the other scheme adopts a decision-directed method and does not require extra pilot overhead. Simulation results show the robustness of the proposed channel estimation method. Furthermore, the proposed method shows better adaptation to practical imperfections compared with the conventional minimum mean-square error (MMSE) channel estimation. It outperforms the existing machine learning-based channel estimation techniques under varying channel conditions.