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Resource allocation has a direct and profound impact on the performance ofvehicle-to-everything (V2X) networks. Considering the dynamic nature ofvehicular environments, it is appealing to devise a decentralized strategy toperform effective resource sharing. In this paper, we exploit deep learning topromote coordination among multiple vehicles and propose a hybrid architectureconsisting of centralized decision making and distributed resource sharing tomaximize the long-term sum rate of all vehicles. To reduce the networksignaling overhead, each vehicle uses a deep neural network to compress its ownobserved information that is thereafter fed back to the centralizeddecision-making unit, which employs a deep Q-network to allocate resources andthen sends the decision results to all vehicles. We further adopt aquantization layer for each vehicle that learns to quantize the continuousfeedback. Extensive simulation results demonstrate that the proposed hybridarchitecture can achieve near-optimal performance. Meanwhile, there exists anoptimal number of continuous feedback and binary feedback, respectively.Besides, this architecture is robust to different feedback intervals, inputnoise, and feedback noise.

Learn to Allocate Resources in Vehicular Networks