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