Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
By Le Liang, Hao Ye, Geoffrey Y. Li. IEEE Journal on Selected Areas in Communications, 2019
444 citations recorded by the source corpus.
This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. Fast channel variations in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In response, we model the resource sharing as a multi-agent reinforcement learning problem, which is then solved using a fingerprint-based deep Q-network method that is amenable to a distributed implementation. The V2V links, each acting as an agent, collectively interact with the communication environment, receive distinctive observations yet a common reward, and learn to improve spectrum and power allocation through updating Q-networks using the gained experiences. We demonstrate that with a proper reward design and training mechanism, the multiple V2V agents successfully learn to cooperate in a distributed way to simultaneously improve the sum capacity of V2I links and payload delivery rate of V2V links.
Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/0ac70f97142e121ac13df35f82865a5b817036a7
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In Automotive Engineering
In Control and Systems Engineering
In Electrical and Electronic Engineering
- Xuemin Shen
- Yonghui Li
- Chenyang Yang
- H. Vincent Poor
- Branka Vucetic
- Hai-xia Peng
- Zhiguo Ding
- Yuanwei Liu
Citations in the corpus
- Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks (2021, background): “To achieve a cooperative Markov game, we assume the same immediate reward r is returned to each agent [6], i.”
- A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, background): “In a system, where users and BSs make decisions in a distributed way, the problem can be formulated as a fully cooperative game to improve the global network performance [121].”