2019/07/30 by Liang Wang, Wang, Liang, Hao Ye +5 · 1 citation
Computer Science · Engineering · Mathematics · #Distributed systems and fault tolerance #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #Transportation and Mobility Innovations #Vehicular Ad Hoc Networks (VANETs) #cs.IT #cs.LG #cs.NI #eess.SP #electronic engineering #information engineering #math.IT
paper · pdf · doi:10.48550/arxiv.1908.03447
arxiv created 2019/07/30 · openalex publication_date 2019/07/30 · arxiv updated 2019/08/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.