Entropy-based location management in long-term evolution cellular systems

Abstract

The recently emerging location-based services in long-term evolution (LTE) systems require the accurate and efficient tracking of mobile users. An optimal information-theoretic framework is developed for tracking area update (TAU) for next-generation LTE cellular systems. Shannon's entropy is used to characterise the location uncertainty of the mobile users. Based on this entropy-based tracking framework, two practical location management schemes, Bayesian-based TAU and entropy-coding based TAU, are proposed. The proposed schemes capture the users' mobility patterns online and perform profile-based paging to optimise the TAU cost. Of the two proposed schemes, the Bayesian-based TAU operates as an independently identically distributed process and improves the paging cost with less storage and computational overhead, whereas the entropy-coding-based TAU using the Lempel'Ziv strategy asymptotically minimises both the update and paging costs with higher storage and computational overheads than the Bayesian-based one. There is some trade-off between the update/paging costs and storage/computational overheads in the two proposed schemes. The simulation results demonstrate that both proposed schemes outperform the existing comparable schemes for LTE systems in all of the performance metrics.