A Non-stochastic Learning Approach to Energy Efficient Mobility Management

TL;DR

This paper introduces a non-stochastic online learning method for energy-efficient mobility management in ultra-dense wireless networks, minimizing handovers and adapting to network dynamics without relying on statistical assumptions.

Contribution

It proposes the BREW algorithm for SBS selection with proven sublinear regret and introduces RE and CRE strategies for dynamic SBS on/off scenarios, addressing complex network behaviors.

Findings

BREW reduces unnecessary handovers and energy consumption.

RE and CRE algorithms handle SBS dynamics with proven regret bounds.

Proposed methods are robust and effective in practical ultra-dense networks.

Abstract

Energy efficient mobility management is an important problem in modern wireless networks with heterogeneous cell sizes and increased nodes densities. We show that optimization-based mobility protocols cannot achieve long-term optimal energy consumption, particularly for ultra-dense networks (UDN). To address the complex dynamics of UDN, we propose a non-stochastic online-learning approach which does not make any assumption on the statistical behavior of the small base station (SBS) activities. In addition, we introduce handover cost to the overall energy consumption, which forces the resulting solution to explicitly minimize frequent handovers. The proposed Batched Randomization with Exponential Weighting (BREW) algorithm relies on batching to explore in bulk, and hence reduces unnecessary handovers. We prove that the regret of BREW is sublinear in time, thus guaranteeing its convergence to the optimal SBS selection. We further study the robustness of the BREW algorithm to delayed or missing feedback. Moreover, we study the setting where SBSs can be dynamically turned on and off. We prove that sublinear regret is impossible with respect to arbitrary SBS on/off, and then develop a novel learning strategy, called ranking expert (RE), that simultaneously takes into account the handover cost and the availability of SBS. To address the high complexity of RE, we propose a contextual ranking expert (CRE) algorithm that only assigns experts in a given context. Rigorous regret bounds are proved for both RE and CRE with respect to the best expert. Simulations show that not only do the proposed mobility algorithms greatly reduce the system energy consumption, but they are also robust to various dynamics which are common in practical ultra-dense wireless networks.