A Simple Cooperative Diversity Method Based on Deep-Learning-Aided Relay Selection

TL;DR

This paper introduces a deep-learning-based predictive relay selection method that enhances cooperative diversity in wireless networks, especially under fast fading conditions, by improving relay selection accuracy and maintaining simplicity.

Contribution

It proposes a novel deep-learning-aided relay selection scheme that effectively predicts channel states, enabling reliable relay selection in both slow and fast fading environments.

Findings

Achieves full diversity gain in slow fading channels.

Substantially outperforms existing schemes in fast fading channels.

Maintains simplicity by selecting a single relay without complex coordination.

Abstract

Opportunistic relay selection (ORS) has been recognized as a simple but efficient method for mobile nodes to achieve cooperative diversity in slow fading channels. However, the wrong selection of the best relay arising from outdated channel state information (CSI) in fast time-varying channels substantially degrades its performance. With the proliferation of high-mobility applications and the adoption of higher frequency bands in 5G and beyond systems, the problem of outdated CSI will become more serious. Therefore, the design of a novel cooperative method that is applicable to not only slow fading but also fast fading is increasingly of importance. To this end, we develop and analyze a deep-learning-aided cooperative method coined predictive relay selection (PRS) in this article. It can remarkably improve the quality of CSI through fading channel prediction while retaining the simplicity of ORS by selecting a single opportunistic relay so as to avoid the complexity of multi-relay coordination and synchronization. Information-theoretic analysis and numerical results in terms of outage probability and channel capacity reveal that PRS achieves full diversity gain in slow fading wireless environments and substantially outperforms the existing schemes in fast fading channels.