Power Control Based on Multi-Agent Deep Q Network for D2D Communication

TL;DR

This paper introduces a multi-agent deep Q network approach for adaptive power control in D2D communication, aiming to reduce interference and enhance system throughput in LTE networks.

Contribution

It proposes a novel reinforcement learning algorithm for power control in D2D communication, outperforming traditional methods in LTE simulations.

Findings

Improved system throughput with the proposed RL-based power control.

Reduced interference to cellular users.

Outperforms traditional algorithms in LTE simulations.

Abstract

In device-to-device (D2D) communication under a cell with resource sharing mode the spectrum resource utilization of the system will be improved. However, if the interference generated by the D2D user is not controlled, the performance of the entire system and the quality of service (QOS) of the cellular user may be degraded. Power control is important because it helps to reduce interference in the system. In this paper, we propose a reinforcement learning algorithm for adaptive power control that helps reduce interference to increase system throughput. Simulation results show the proposed algorithm has better performance than traditional algorithm in LTE (Long Term Evolution).