A Simple Cooperative Diversity Method Based on Deep-Learning-Aided Relay Selection
This work is significant for mobile communication systems, particularly in 5G and beyond, by improving the reliability and capacity of cooperative diversity in high-mobility scenarios where outdated CSI is a critical problem.
This paper addresses the performance degradation of opportunistic relay selection (ORS) in fast fading channels due to outdated channel state information (CSI). The authors propose predictive relay selection (PRS), a deep-learning-aided method that improves CSI quality through channel prediction, achieving full diversity gain in slow fading and outperforming existing schemes in fast fading channels.
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 However, the wrong selection of the best relay arising from outdated channel state information (CSI) in fast time-varying channels substantially degrades its performance.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of high-mobility applications and the adoption of higher frequency bands in 5G and beyond systems, the problem of outdated CSI will become more serious.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.