Predictive Relay Selection: A Cooperative Diversity Scheme Using Deep Learning

TL;DR

This paper introduces a deep learning-based cooperative relay selection scheme that enhances diversity gains and reduces relay selection errors in fast-fading channels, with minimal added computational complexity.

Contribution

It presents a novel DL-based channel prediction method integrated into relay selection, improving performance in fast-varying wireless channels.

Findings

Achieves full diversity gain in slow-fading channels.

Outperforms existing schemes in fast-fading environments.

Computational complexity is negligible.

Abstract

In this paper, we propose a novel cooperative multi-relay transmission scheme for mobile terminals to exploit spatial diversity. By improving the timeliness of measured channel state information (CSI) through deep learning (DL)-based channel prediction, the proposed scheme remarkably lowers the probability of wrong relay selection arising from outdated CSI in fast time-varying channels. It inherits the simplicity of opportunistic relaying by selecting a single relay, avoiding the complexity of multi-relay coordination and synchronization. Numerical results reveal that it can achieve full diversity gain in slow-fading channels and substantially outperforms the existing schemes in fast-fading wireless environments. Moreover, the computational complexity brought by the DL predictor is negligible compared to off-the-shelf computing hardware.