User-Centric Mobility Management in Ultra-Dense Cellular Networks under Spatio-Temporal Dynamics

By Jihong Park, S. Jung, Seong-Lyun Kim, Medhi Bennis, Merouane Debbah. 2016

39 citations recorded by the source corpus.

This article investigates the mobility management of an ultra dense cellular network (UDN) from an energy-efficiency (EE) point of view. Many dormant base stations (BSs) in a UDN do not transmit signals, and thus a received power based handover (HO) approach as in traditional cellular networks is hardly applicable. In addition, the limited front/backhaul capacity compared to a huge number of BSs makes it difficult to implement a centralized HO and power control. For these reasons, a novel user-centric association rule is proposed, which jointly optimizes HO and power control for maximizing EE. The proposed mobility management is able to cope not only with the spatial randomness of user movement but also with temporally correlated wireless channels. The proposed approach is implemented over a HO time window and tractable power con- trol policy by exploiting mean-field game (MFG) and stochastic geometry (SG). Compared to a baseline with a fixed HO interval and transmit power, the proposed approach achieves the 1.2 times higher long-term average EE at a typical active BS.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Wireless Network Intelligence at the Edge (2018, background): “With the wireless channel dynamics, MFG has shown its effectiveness in transmission power control and resource management particularly under ultra-dense cellular networks (UDNs) [166].”
URLLC-eMBB Slicing to Support VR Multimodal Perceptions over Wireless Cellular Systems (2018, background): “With the channel information, one can improve the average rate by adjusting the transmit power [17], [21], [22] and/or the coding rate [18], [20]–[22].”
Ultra-dense edge caching under spatio-temporal demand and network dynamics (2017, background): “In UDNs, the interference normalized by BS densification becomes deterministic [9], [10], which allows us to simplify the spatial dynamics of UDCNs.”
EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks (2017, methodology): “To address this challenge, an energy-efficient association and power control policy is proposed in [17], while a learningbased mobility management scheme is proposed in [18] based on the multi-armed bandits (MAB) theory [19].”
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, background): “Mean-field game (MFG) is a useful framework to greatly reduce the complexity [118]–[122].”