Research on Energy Efficiency Optimization Based on User Access in Dense Small Cell Network

To address the high energy consumption brought by dense deployment of base stations in Small Cell Network (SCN), this paper proposes an Energy Efficiency (EE) optimization algorithm based on user access. The algorithm divides adjacent base stations in SCN into a cluster, and introduces merge factors to balance the size of clusters. Based on clustering results as well as user access rate and base station loads, the algorithm uses an improved Chaotic Quantum Particle Swarm Optimization (CQPSO) algorithm to find the optimal user connection matrix. Thus, the dynamic switching management of base station switches can be implemented based on network traffic changes. Simulation results show that the proposed optimization algorithm can improve system energy efficiency while keeping QoS of users, which is applicable to 5G wireless network.