  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outdated channel state information (CSI) in fast time-varying channels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systems, the problem of outdated CSI will become more serious.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.
