Context-Aware Indoor VLC/RF Heterogeneous Network Selection: Reinforcement Learning With Knowledge Transfer

For the converged use of LTE, WLAN, and visible light communication in indoor scenarios, fine-grained and intelligent network selection is essential for ensuring high user quality of experience. To tackle the challenges associated with dynamic environments and complicated service requirements, we propose a context-aware solution for indoor network selection. Specifically, three-level contextual information is revealed and exploited in both the utility and algorithm designs. In particular, the contextual information about the asymmetric downlink-uplink features of network performance is used to design a fine-grained utility model. A context-aware learning algorithm sensitive to traffic type-location-time information is proposed. The time-location dependent periodic changing rule of load statistical distributions is further used to realize efficient online network selection via knowledge transfer. The simulation results show that the proposed algorithm can achieve much better performance with faster convergence speed than traditional reinforcement learning.