Journal
IEEE ACCESS
Volume 6, Issue -, Pages 33275-33284Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2844882
Keywords
Indoor network selection; visible light communication; context-awareness; reinforcement learning; transfer learning
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Funding
- NSF of China [61601490, 61671477]
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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 finegrained 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.
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