An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks
This addresses spectrum management for vehicular networks, but it appears incremental as it extends existing ideas with a known method.
The paper tackles spectrum sharing in heterogeneous vehicular networks by using multi-agent reinforcement learning to enable distributed channel allocation among vehicle-to-vehicle links, aiming to improve collaboration and efficiency in a fast-changing environment.
This paper presents the extension of the idea of spectrum sharing in the vehicular networks towards the Heterogeneous Vehicular Network(HetVNET) based on multi-agent reinforcement learning. Here, the multiple vehicle-to-vehicle(V2V) links reuse the spectrum of other vehicle-to-interface(V2I) and also those of other networks. The fast-changing environment in vehicular networks limits the idea of centralizing the CSI and allocate the channels. So, the idea of implementing ML-based methods is used here so that it can be implemented in a distributed manner in all vehicles. Here each On-Board Unit(OBU) can sense the signals in the channel and based on that information runs the RL to decide which channel to autonomously take up. Here, each V2V link will be an agent in MARL. The idea is to train the RL model in such a way that these agents will collaborate rather than compete.