Mattu, SR and Chockalingam, A (2022) Learning-Based Channel Estimation and Phase Noise Compensation in Doubly-Selective Channels. In: IEEE Communications Letters, 26 (5). pp. 1052-1056.
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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.
| Item Type: | Journal Article |
|---|---|
| Publication: | IEEE Communications Letters |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Channel estimation; Convolution; Deep learning; Fading channels; Frequency estimation; Frequency selective fading; Neural networks; Orthogonal frequency division multiplexing; Phase noise, 2d convolutional neural network; Convolutional neural network; Deep learning; Doubly-selective fading; Orthogonal frequency-division multiplexing; Phase noise compensations; Phase-noise; Time-domain analysis; Time-frequency Analysis, Time domain analysis |
| Department/Centre: | Division of Electrical Sciences > Electrical Communication Engineering |
| Date Deposited: | 24 Jun 2022 11:27 |
| Last Modified: | 24 Jun 2022 11:27 |
| URI: | https://eprints.iisc.ac.in/id/eprint/73660 |
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