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

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