A Self-organizing Base Station Sleeping Strategy in Small Cell Networks Using Local Stable Matching Games

A distributed small-base stations (s-BSs) sleeping approach is proposed for optimizing energy efficiency (EE) in small cell networks (SCNs). Different from the existing studies, the associating preferences of both s-BSs and user equipments (UEs) are considered to enhance the flexibility of optimization. The SCNs sleeping problems are modeled as matching markets. The s-BSs sleeping problem is disassembled into two subproblems: optimization of the associations and turning off of the superfluous s-BSs. Based on the matching game theory, we put forward a Matching Game Based UE Association Algorithm (MGBAA) in settling the first sub-problem and a local matching sleeping algorithm (LMSA) in solving the second. The proposed sleeping strategy optimizes the irrational associations as well as moderates the redundant s-BSs without whittling down network throughput. The simulation results verify that the suggested sleeping approach deployment scheme surpasses the existing cell planning scheme with the objective of the energy efficiency ratio (EER) under various scenarios.