Location Management in LTE Networks using Multi-Objective Particle Swarm Optimization

Abstract

Long-term evolution (LTE) and LTE-advance (LTE-A) are widely used efficient network technologies serving billions of users, since they are featured with high spectrum efficiency, less latency, and higher bandwidth. Despite remarkable advantages offered by these technologies, signaling overhead remains a major issue in accessing the network. In particular, the load of signaling is mainly attributed to location management. This paper proposes an efficient approach for minimizing the total signaling overhead of location management in LTE networks using multi-objective particle swarm optimization (MOPSO). Tracking area update (TAU) and paging are considered to be the main elements of the signaling overhead of optimal location management in LTE. In addition, the total inter-list handover contributes significantly to the total signaling overhead. However, the total signaling cost of TAU and paging is adversely related to the total inter-list handover. Two cost functions should be minimized, the first is the total signaling cost of TAU and paging and the second is the total signaling overhead. The trade-off between these two objectives can be circumvented by MOPSO, which alleviates the total signaling overhead. A set of non-dominated solutions on the Pareto-optimal front is defined and the best compromise solution. The proposed algorithm results feasible compromise solution, minimizing the signaling overhead and the consumption of the power battery of a user. The efficacy and the robustness of the proposed algorithm have been proven using large scale environment problem illustrative example. The location management in LTE networks using MOPSO best compromise solution has been compared to a mixed integer non-linear programming (MINLP) algorithm. Location management mobility management entity MME pooling clustering SON Distributed Centralized pooling scheme fuzzy implementation setup LP-CPLEX