6 research outputs found
A Non-Stochastic Learning Approach to Energy Efficient Mobility Management
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 (UDNs). 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. © 1983-2012 IEEE
EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks
Merging mobile edge computing (MEC) functionality with the dense deployment
of base stations (BSs) provides enormous benefits such as a real proximity, low
latency access to computing resources. However, the envisioned integration
creates many new challenges, among which mobility management (MM) is a critical
one. Simply applying existing radio access oriented MM schemes leads to poor
performance mainly due to the co-provisioning of radio access and computing
services of the MEC-enabled BSs. In this paper, we develop a novel user-centric
energy-aware mobility management (EMM) scheme, in order to optimize the delay
due to both radio access and computation, under the long-term energy
consumption constraint of the user. Based on Lyapunov optimization and
multi-armed bandit theories, EMM works in an online fashion without future
system state information, and effectively handles the imperfect system state
information. Theoretical analysis explicitly takes radio handover and
computation migration cost into consideration and proves a bounded deviation on
both the delay performance and energy consumption compared to the oracle
solution with exact and complete future system information. The proposed
algorithm also effectively handles the scenario in which candidate BSs randomly
switch on/off during the offloading process of a task. Simulations show that
the proposed algorithms can achieve close-to-optimal delay performance while
satisfying the user energy consumption constraint.Comment: 14 pages, 6 figures, an extended version of the paper submitted to
IEEE JSA