Low Complexity Online Radio Access Technology Selection Algorithm in LTE-WiFi HetNet

In an offload-capable Long Term Evolution (LTE)Wireless Fidelity (WiFi) Heterogeneous Network (HetNet), we consider the problem of maximization of the total system throughput under voice user blocking probability constraint. The optimal policy is threshold in nature. However, computation of optimal policy requires the knowledge of the statistics of system dynamics, viz., arrival processes of voice and data users, which may be difficult to obtain in reality. Motivated by the Post-Decision State (PDS) framework to learn the optimal policy under unknown statistics of system dynamics, we propose, in this paper, an online Radio Access Technology (RAT) selection algorithm using Relative Value Iteration Algorithm (RVIA). However, the convergence speed of this algorithm can be further improved if the underlying threshold structure of the optimal policy can be exploited. To this end, we propose a novel structureaware online RAT selection algorithm which reduces the feasible policy space, thereby offering lesser storage and computational complexity and faster convergence. This algorithm provides a novel framework for designing online learning algorithms for other problems and hence is of independent interest. We prove that both the algorithms converge to the optimal policy. Simulation results demonstrate that the proposed algorithms converge faster than a traditional scheme. Also, the proposed schemes perform better than other benchmark algorithms under realistic network scenarios.