Deep Reinforcement Learning for Resource Allocation in V2V Communications

Understand

In this article, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communication systems based on deep reinforcement learning.

• Each V2V link is considered as an agent, making its own decisions to find optimal sub-band and power level for transmission.
• Since the proposed method is decentralized, the global information is not required for each agent to make its decisions, hence the transmission overhead is small.
• From the simulation results, each agent can learn how to satisfy the V2V constraints while minimizing the interference to vehicle-to-infrastructure (V2I) communications.