Energy efficient small-cell discovery using users’ mobility prediction

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Abstract

Deployment of small cells (i.e., picocells and femtocells) within macrocell coverage is seen as a cost-effective way to increase system capacity and to equip wireless WANs with the ability to keep up with the increasing demand for data capacity. Existing cell discovery mechanisms are tailored for homogeneous networks (macrocells only). User Equipment (UE) cannot efficiently save energy in the process of small cells detection in order to exploit offloading opportunities provided by such heterogeneous deployments. In this paper, we propose a Mobility Prediction aware Scanning Start Time Estimation (MPSTE) scheme to discover/detect small cells efficiently in terms of energy. Based on the current data on road segments (e.g., density of road segment, UEs’ speeds and physical aspects of road segment) and current behaviour of UEs on the road segment, MPSTE allows deriving the time interval UE will spend in the small cell and making decision to perform handoff or no; if handoff is necessary, MPSTE derives the best time to begin the scanning process to discover small cells. Simulation results show the benefits of MPSTE over existing schemes in terms of energy saving by UEs.

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APA

Nadembega, A., Hafid, A., & Brisebois, R. (2015). Energy efficient small-cell discovery using users’ mobility prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9143, pp. 330–343). Springer Verlag. https://doi.org/10.1007/978-3-319-19662-6_23

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