Learning based Channel Estimation and Phase Noise Compensation in Doubly-Selective Channels

TL;DR

This paper introduces a learning-based channel estimation method using 2D CNNs for OFDM systems affected by phase noise in doubly-selective channels, improving robustness and accuracy.

Contribution

It presents a novel 2D CNN approach for joint channel estimation and phase noise compensation in doubly-selective OFDM channels, with a unique training scheme for phase noise robustness.

Findings

Achieves robust OFDM performance under phase noise conditions

Effective channel estimation across the entire time-frequency grid

Improves phase noise compensation accuracy

Abstract

In this letter, we propose a learning based channel estimation scheme for orthogonal frequency division multiplexing (OFDM) systems in the presence of phase noise in doubly-selective fading channels. Two-dimensional (2D) convolutional neural networks (CNNs) are employed for effective training and tracking of channel variation in both frequency as well as time domain. The proposed network learns and estimates the channel coefficients in the entire time-frequency (TF) grid based on pilots sparsely populated in the TF grid. In order to make the network robust to phase noise (PN) impairment, a novel training scheme where the training data is rotated by random phases before being fed to the network is employed. Further, using the estimated channel coefficients, a simple and effective PN estimation and compensation scheme is devised. Numerical results demonstrate that the proposed network and PN compensation scheme achieve robust OFDM performance in the presence of phase noise.