Mobility Load Balancing Method for Self-Organizing Wireless Networks Inspired by Synchronization and Matching With Preferences

Abstract

Mobility load balancing (MLB) aims to resolve the mismatch between the distribution of network resources and the traffic demands. In this paper, we identify two key policies that characterize jointly an MLB method and propose algorithms for them to stabilize a network and increase the resource efficiency of a network. We adopt the synchronization model observed in nature to devise the first policy called a load assignment policy that determines the amount of loads tradable among cells. We design the second policy called a target selection policy by using the matching theory with preferences to determine the optimal pairs of user equipment (UE) and their new serving cells when the UE in an overloaded cell need to be handed over to its neighboring cells. Through mathematical analysis, we show the stability and the optimality of the proposed MLB method. We also show through simulation studies that the proposed method can distribute loads among cells more evenly than the conventional methods, which increases the total network throughput and the quality of service provided to UE.