Reinforcement Learning Based Network Selection for Hybrid VLC and RF Systems

Abstract

For hybrid indoor network scenario with LTE, WLAN and Visible Light Communication (VLC), selecting network intelligently based on user service requirement is essential for ensuring high user quality of experience. In order to tackle the challenge due to dynamic environment and complicated service requirement, we propose a reinforcement learning solution for indoor network selection. In particular, a transfer learning based network selection algorithm, i.e., reinforcement learning with knowledge transfer, is proposed by revealing and exploiting the context information about the features of traffic, networks and network load distribution. The simulations show that the proposed algorithm has an efficient online learning ability and could achieve much better performance with faster convergence speed than the traditional reinforcement learning algorithm.