Deep Reinforcement Learning-aided Transmission Design for Energy-efficient Link Optimization in Vehicular Communications

TL;DR

This paper introduces a novel deep reinforcement learning method, SI-D3QN, for optimizing energy-efficient vehicle-to-vehicle communication links by jointly designing modulation, coding, and power control in dynamic environments.

Contribution

The paper proposes a new DRL algorithm, SI-D3QN, that combines double and Dueling deep Q-Networks with scenario identification for improved link optimization in vehicular communications.

Findings

Achieves 29.6% increase in link throughput at the same energy level.

Outperforms benchmark algorithms in link performance and decision accuracy.

Enhances energy efficiency in V2V communication links.

Abstract

This letter presents a deep reinforcement learning (DRL) approach for transmission design to optimize the energy efficiency in vehicle-to-vehicle (V2V) communication links. Considering the dynamic environment of vehicular communications, the optimization problem is non-convex and mathematically difficult to solve. Hence, we propose scenario identification-based double and Dueling deep Q-Network (SI-D3QN), a DRL algorithm integrating both double deep Q-Network and Dueling deep Q-Network, for the joint design of modulation and coding scheme (MCS) selection and power control. To be more specific, we employ SI techique to enhance link performance and assit the D3QN agent in refining its decision-making processes. The experiment results demonstrate that, across various optimization tasks, our proposed SI-D3QN agent outperforms the benchmark algorithms in terms of the valid actions and link performance metrics. Particularly, while ensuring significant improvement in energy efficiency, the agent facilitates a 29.6% enhancement in the link throughput under the same energy consumption.