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Cellular vehicle-to-everything (V2X) communication is crucial to supportfuture diverse vehicular applications. However, for safety-criticalapplications, unstable vehicle-to-vehicle (V2V) links and high signallingoverhead of centralized resource allocation approaches become bottlenecks. Inthis paper, we investigate a joint optimization problem of transmission modeselection and resource allocation for cellular V2X communications. Inparticular, the problem is formulated as a Markov decision process, and a deepreinforcement learning (DRL) based decentralized algorithm is proposed tomaximize the sum capacity of vehicle-to-infrastructure users while meeting thelatency and reliability requirements of V2V pairs. Moreover, consideringtraining limitation of local DRL models, a two-timescale federated DRLalgorithm is developed to help obtain robust model. Wherein, the graph theorybased vehicle clustering algorithm is executed on a large timescale and in turnthe federated learning algorithm is conducted on a small timescale. Simulationresults show that the proposed DRL-based algorithm outperforms otherdecentralized baselines, and validate the superiority of the two-timescalefederated DRL algorithm for newly activated V2V pairs.

Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications