A Non-Stationary Online Learning Approach to Mobility Management

Efficient mobility management is an important problem in modern wireless networks with heterogeneous cell sizes and increased node densities. We show that optimization-based mobility protocols cannot achieve long-term optimal performance, particularly for ultra-dense networks in a time-varying environment. To address the complex system dynamics, especially the possible change of statistics due to user movement and environment changes, we propose piece-wise stationary online-learning algorithms to learn the varying throughput distribution and solve the frequent handover problem. The proposed MMBD/MMBSW algorithms are proved to achieve sublinear regret performance in finite time horizon and a linear, non-trivial rigorous regret bound for infinite time horizon. We also study the robustness of the MMBD/MMBSW algorithms under delayed or missing feedback. The simulations show that the proposed algorithms can outperform 3GPP protocols with optimal thresholds. More importantly, they are more robust to system dynamics which are commonly present in practical ultra-dense wireless networks.