Cell Association With User Behavior Awareness in Heterogeneous Cellular Networks

Abstract

In heterogeneous cellular networks (HetNets) with macro base station (BS) and multiple small BSs (SBSs), cell association of user equipment (UE) affects UE transmission rate and network throughput. Conventional cell association rules are usually based on UE received signal-to-interference-and-noise-ratio (SINR) without being aware of other UE statistical characteristics, such as user movement and distribution. User behaviors can indeed be exploited for improving long-term network performance. In this paper, we investigate UE cell association in HetNets by exploiting both individual and clustering user behaviors with the aim to maximize long-term system throughput. We model the problem as a stochastic optimization problem, and prove that it is PSPACE-hard. For mathematical tractability, we solve the problem in two steps. In the first step, we investigate UE association for a specific SBS. We use a restless multiarmed bandit model to derive an association priority index for the SBS. In the second step, we develop an index enabled association (IDEA) policy for making the cell association decisions in general HetNets based on the indices derived in the first step. IDEA determines a set of admissible BSs for a UE based on SINR, and then associates the UE with the BS that has the smallest index in the set. We conduct simulation experiments to compare IDEA with other three cell association policies. Numerical results demonstrate the significant advantages of IDEA in typical scenarios.