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

• Published in: IEEE communications letters Vol. 26; no. 5; pp. 1052 - 1056
• Main Authors: Mattu, Sandesh Rao, Chockalingam, A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 2-D convolutional neural network; Artificial neural networks; Channel estimation; Channels; Compensation; deep learning; doubly-selective fading; Estimation; Frequency variation; Learning; Noise; OFDM; Orthogonal Frequency Division Multiplexing; Phase noise; Radio frequency; Robustness (mathematics); Selective fading; Time-domain analysis; Time-frequency analysis; Training
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2022.3155186

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.