Aging-Resistant Wideband Precoding in 5G and Beyond Using 3D Convolutional Neural Networks

TL;DR

This paper introduces two deep learning frameworks using 3D CNNs to address frequency and time selectivity challenges in 5G/6G wideband precoding, significantly improving performance with minimal added complexity.

Contribution

It presents novel deep learning-based methods for wideband precoding that effectively handle channel frequency and time selectivity in high-frequency, large-antenna systems.

Findings

Enhanced channel estimation accuracy

Reduced pilot overhead

Improved precoding performance

Abstract

To meet the ever-increasing demand for higher data rates, 5G and 6G technologies are shifting transceivers to higher carrier frequencies, to support wider bandwidths and more antenna elements. Nevertheless, this solution poses several key challenges: i) increasing the carrier frequency and bandwidth leads to greater channel frequency selectivity in time and frequency domains, and ii) the greater the number of antennas the greater the the pilot overhead for channel estimation and the more prohibitively complex it becomes to determine the optimal precoding matrix. This paper presents two deep-learning frameworks to solve these issues. Firstly, we propose a 3D convolutional neural network (CNN) that is based on image super-resolution and captures the correlations between the transmitting and receiving antennas and the frequency domains to combat frequency selectivity. Secondly, we devise a deep learning-based framework to combat the time selectivity of the channel that treats channel aging as a distortion that can be mitigated through deep learning-based image restoration techniques. Simulation results show that combining both frameworks leads to a significant improvement in performance compared to existing techniques with little increase in complexity.