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 Qnetwork method and 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 success probability of payload delivery for V2V links. Index Terms-Vehicular networks, distributed spectrum access, spectrum and power allocation, multi-agent reinforcement learning
A multi-agent reinforcement learning based spectrum sharing method was proposed in REF to jointly optimize the sum capacity of V2I and payload delivery rate of V2V links in vehicular networks.
Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
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Computer Science Mathematics