  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.
