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Cooperative relays improve reliability and coverage in wireless networks byproviding multiple paths for data transmission. Relaying will play an essentialrole in vehicular networks at higher frequency bands, where mobility andfrequent signal blockages cause link outages. To ensure connectivity in arelay-aided vehicular network, the relay selection policy should be designed toefficiently find unblocked relays. Inspired by recent advances in beammanagement in mobile millimeter wave (mmWave) networks, this paper address thequestion: how can the best relay be selected with minimal overhead from beammanagement? In this regard, we formulate a sequential decision problem tojointly optimize relay selection and beam management. We propose a joint relayselection and beam management policy based on deep reinforcement learning (DRL)using the Markov property of beam indices and beam measurements. The proposedDRL-based algorithm learns time-varying thresholds that adapt to the dynamicchannel conditions and traffic patterns. Numerical experiments demonstrate thatthe proposed algorithm outperforms baselines without prior channel knowledge.Moreover, the DRL-based algorithm can maintain high spectral efficiency underfast-varying channels.

Joint Relay Selection and Beam Management Based on Deep Reinforcement Learning for Millimeter Wave Vehicular Communication