Deep OFDM Channel Estimation: Capturing Frequency Recurrence
Abstract
In this paper, we propose a deep-learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. Our proposed method, named Single Slot Recurrence Along Frequency Network (SisRafNet), is based on a novel study of recurrent models for exploiting sequential behavior of channels across frequencies. Utilizing the fact that wireless channels have a high degree of correlation across frequencies, we employ recurrent neural network techniques within a single OFDM slot, thus overcoming the latency and memory constraints typically associated with recurrence based methods. The proposed SisRafNet delivers superior estimation performance compared to existing deep-learning-based channel estimation techniques and the performance has been validated on a wide range of 3rd Generation Partnership Project (3GPP) compliant channel scenarios at multiple signal-to-noise ratios.
Keywords
Cite
@article{arxiv.2401.05436,
title = {Deep OFDM Channel Estimation: Capturing Frequency Recurrence},
author = {Abu Shafin Mohammad Mahdee Jameel and Akshay Malhotra and Aly El Gamal and Shahab Hamidi-Rad},
journal= {arXiv preprint arXiv:2401.05436},
year = {2024}
}
Comments
Accepted for publication at IEEE Communications Letters