Deep Reinforcement Learning based Resource Allocation for V2V Communications

TL;DR

This paper proposes a decentralized deep reinforcement learning approach for resource allocation in V2V communications, enabling autonomous decision-making for sub-band and power level selection to meet latency constraints with minimal interference.

Contribution

It introduces a novel decentralized DRL-based resource allocation mechanism for V2V communications applicable to unicast and broadcast scenarios, reducing overhead and improving latency compliance.

Findings

Agents effectively learn to meet latency constraints.

The method minimizes interference to V2I communications.

Decentralized approach reduces transmission overhead.

Abstract

In this paper, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to both unicast and broadcast scenarios. According to the decentralized resource allocation mechanism, an autonomous agent', a V2V link or a vehicle, makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Since the proposed method is decentralized, it incurs only limited transmission overhead. From the simulation results, each agent can effectively learn to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure (V2I) communications.