[Paper Review] Mobile Traffic Offloading with Forecasting using Deep Reinforcement Learning

Chih–Wei Huang, Po‐Chen Chen|arXiv (Cornell University)|Nov 18, 2019

Advanced MIMO Systems Optimization14 references5 citations

TL;DR

This paper proposes a deep reinforcement learning (DRL)-based mobile traffic offloading scheme in heterogeneous networks (HetNet) that integrates traffic demand forecasting using convolutional and recurrent neural networks. By leveraging a deep Q-network (DQN) with predicted traffic statistics, the method achieves superior energy efficiency and offloading performance, outperforming Q-learning and static schemes by up to 10% in energy efficiency under high traffic loads.

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.

Motivation & Objective

• Address the challenge of energy-efficient mobile traffic offloading in heterogeneous networks (HetNet) under dynamic, unpredictable traffic demands.
• Overcome limitations of reactive network management by enabling proactive decisions through accurate traffic forecasting.
• Improve decision-making efficiency and energy savings by training a DQN agent on predicted traffic statistics rather than raw traffic data.
• Demonstrate the effectiveness of combining deep reinforcement learning with multitask learning and advanced forecasting on real-world telecom data.

Proposed method

• Employ a deep Q-network (DQN) to make energy-aware offloading decisions, mapping complex state spaces to discrete actions (e.g., activate/deactivate small base stations).
• Integrate a hybrid forecasting model combining CNN and RNN with multitask learning to predict future traffic demand and provisioning statistics.
• Use predicted traffic statistics (e.g., peak load, demand trends) as input features to the DQN agent, improving training stability and decision accuracy.
• Train the DQN agent end-to-end on a real-world open dataset from a major telecom operator, simulating macrocell and small cell interactions.
• Optimize for energy efficiency by minimizing energy consumption while maximizing data throughput and maintaining quality of service.
• Implement a self-organizing network (SON) framework that enables proactive, adaptive offloading based on predicted traffic patterns.

Experimental results

Research questions

• RQ1 Can a DQN-based agent effectively manage mobile traffic offloading in HetNet while optimizing for energy efficiency?
• RQ2 How does integrating traffic demand forecasting improve the performance of DRL-based offloading compared to non-forecasting approaches?
• RQ3 To what extent does traffic forecasting enhance decision-making under high network load conditions?
• RQ4 How does the proposed DQN-F scheme compare to conventional Q-learning and static offloading policies in terms of energy efficiency and data throughput?

Key findings

• The DQN-F scheme achieved 7.37% energy efficiency (0.0737 Mb/Joule), outperforming DQN (6.89%), Q-learning (6.79%), and static offloading (6.31%) in energy efficiency.
• Under high traffic demand (loading rate > 0.7), DQN-F improved energy efficiency by 8% over Q-learning and 6% over DQN.
• The DQN-F model reduced macrocell overload failures to 0.6% in high-demand groups, compared to 5.2% for Q-learning and 4.6% for DQN.
• The integration of traffic forecasting enabled DQN-F to activate fewer small cells while maintaining or improving performance, reducing energy consumption by 3% compared to DQN.
• In heavily loaded scenarios (loading group 2–10), DQN-F transmitted 5% more data and consumed 3% less energy than DQN, achieving 10% better energy efficiency than Q-learning.
• The forecasting component provided a measurable advantage, with performance gains increasing under higher traffic loads, confirming its role in proactive network management.