Learning-Aided Network Association for Hybrid Indoor LiFi-WiFi Systems

Given the scarcity of spectral resources in traditional wireless networks, it has become popular to construct visible light communication (VLC) systems. They exhibit high energy efficiency, wide unlicensed communication bandwidth as well as innate security; hence, they may become part of future wireless systems. However, considering the limited coverage and dense deployment of light-emitting diode (LED) lamps, traditional network association strategies are not readily applicable to VLC networks. Hence, by exploiting the power of online learning algorithms, we focus our attention on sophisticated multi-LED access point selection strategies conceived for hybrid indoor LiFi-WiFi communication systems. We formulate a multi-armed bandit model for supporting the decisions on beneficially selecting LED access points. Moreover, the `exponential weights for exploration and exploitation' algorithm and the `exponentially weighted algorithm with linear programming' algorithm are invoked for updating the decision probability distribution, followed by determining the upper bound of the associated accumulation reward function. Significant throughput gains can be achieved by the proposed network association strategies.