期刊
IEEE TRANSACTIONS ON COMMUNICATIONS
卷 68, 期 6, 页码 3640-3653出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2020.2979124
关键词
Resource management; Interference; Computer architecture; Device-to-device communication; Decision making; Vehicle dynamics; Machine learning; Vehicular networks; deep reinforcement learning; spectrum sharing; binary feedback
资金
- National Natural Science Foundation of China [61601273, 61872228, 61771296]
- Fundamental Research Funds for the Central Universities [GK202003075]
- China Postdoctoral Science Foundation [2016M600761]
- Natural Science Basic Research Plan in Shaanxi Province of China [2018JQ6048]
- China Scholarship Council (CSC) scholarship
- National Science Foundation [1731017, 1815637]
Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of centralized decision making and distributed resource sharing (the C-Decision scheme) 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 observed information that is thereafter fed back to the centralized decision making unit. The centralized decision unit 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. In addition, we devise a mechanism to balance the transmission of vehicle-to-vehicle (V2V) links and vehicle-to-infrastructure (V2I) links. To further facilitate distributed spectrum sharing, we also propose a distributed decision making and spectrum sharing architecture (the D-Decision scheme) for each V2V link. Through extensive simulation results, we demonstrate that the proposed C-Decision and D-Decision schemes can both achieve near-optimal performance and are robust to feedback interval variations, input noise, and feedback noise.
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