Traffic Steering for 5G Multi-RAT Deployments using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based traffic steering method for 5G networks that dynamically optimizes user experience across multiple radio access technologies, outperforming traditional algorithms.

Contribution

It presents a novel Deep Q-learning approach for traffic steering in 5G, improving system throughput and reducing network delay compared to baseline methods.

Findings

6% higher average system throughput than Q-learning

10% higher system throughput than heuristic algorithms

23-33% lower network delay compared to baselines

Abstract

In 5G non-standalone mode, traffic steering is a critical technique to take full advantage of 5G new radio while optimizing dual connectivity of 5G and LTE networks in multiple radio access technology (RAT). An intelligent traffic steering mechanism can play an important role to maintain seamless user experience by choosing appropriate RAT (5G or LTE) dynamically for a specific user traffic flow with certain QoS requirements. In this paper, we propose a novel traffic steering mechanism based on Deep Q-learning that can automate traffic steering decisions in a dynamic environment having multiple RATs, and maintain diverse QoS requirements for different traffic classes. The proposed method is compared with two baseline algorithms: a heuristic-based algorithm and Q-learningbased traffic steering. Compared to the Q-learning and heuristic baselines, our results show that the proposed algorithm achieves better performance in terms of 6% and 10% higher average system throughput, and 23% and 33% lower network delay, respectively.