DRAG: Deep Reinforcement Learning Based Base Station Activation in Heterogeneous Networks

TL;DR

This paper introduces a deep reinforcement learning approach to optimize the activation of small cell base stations in HetNets, significantly reducing energy consumption while maintaining service quality.

Contribution

It formulates SBS on/off switching as a Markov Decision Process and employs a deep actor-critic method with action refinement for scalable, efficient network management.

Findings

Outperforms existing methods in energy efficiency

Reduces computational costs significantly

Scales well to large systems with polynomial complexity

Abstract

Heterogeneous Network (HetNet), where Small cell Base Stations (SBSs) are densely deployed to offload traffic from macro Base Stations (BSs), is identified as a key solution to meet the unprecedented mobile traffic demand. The high density of SBSs are designed for peak traffic hours and consume an unnecessarily large amount of energy during off-peak time. In this paper, we propose a deep reinforcement-learning based SBS activation strategy that activates the optimal subset of SBSs to significantly lower the energy consumption without compromising the quality of service. In particular, we formulate the SBS on/off switching problem into a Markov Decision Process that can be solved by Actor Critic (AC) reinforcement learning methods. To avoid prohibitively high computational and storage costs of conventional tabular-based approaches, we propose to use deep neural networks to approximate the policy and value functions in the AC approach. Moreover, to expedite the training process, we adopt a Deep Deterministic Policy Gradient (DDPG) approach together with a novel action refinement scheme. Through extensive numerical simulations, we show that the proposed scheme greatly outperforms the existing methods in terms of both energy efficiency and computational efficiency. We also show that the proposed scheme can scale to large system with polynomial complexities in both storage and computation.