Mobile Traffic Offloading with Forecasting using Deep Reinforcement Learning

TL;DR

This paper presents a deep reinforcement learning approach combined with traffic forecasting to optimize mobile traffic offloading in heterogeneous networks, improving efficiency especially under high demand conditions.

Contribution

It introduces an energy-aware offloading scheme using deep Q networks and traffic forecasting, demonstrating superior performance over existing methods.

Findings

DQN-based offloading outperforms other methods across all traffic levels.

Accurate traffic prediction provides greater benefits during high demand periods.

The approach is validated on real telecom data, showing practical effectiveness.

Abstract

With the explosive growth in demand for mobile traffic, one of the promising solutions is to offload cellular traffic to small base stations for better system efficiency. Due to increasing system complexity, network operators are facing severe challenges and looking for machine learning-based solutions. In this work, we propose an energy-aware mobile traffic offloading scheme in the heterogeneous network jointly apply deep Q network (DQN) decision making and advanced traffic demand forecasting. The base station control model is trained and verified on an open dataset from a major telecom operator. The performance evaluation shows that DQN-based methods outperform others at all levels of mobile traffic demand. Also, the advantage of accurate traffic prediction is more significant under higher traffic demand.