In this article, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning. Each V2V link is supported by an autonomous "agent", which makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Hence, the proposed method is decentralized, with minimum transmission overhead. From the simulation results, each agent can effectively learn how to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-toinfrastructure (V2I) communications.
In REF , a deep reinforcement learning (DRL) based decentralized resource allocation approach is developed for V2V communications, and each V2V transmitter acts as an autonomous agent who makes decisions based on local observations.
Deep Reinforcement Learning for Resource Allocation in V2V Communications
2018 IEEE International Conference on Communications (ICC)
2018 IEEE International Conference on Communications (ICC)
Mathematics Computer Science