Self-Organized Energy Management in Energy Harvesting Small Cell Networks

Abstract

Small cell networks (SCNs) are envisioned as a promising solution to increase the network capacity and coverage. The densely deployments of SCNs in 5G networks pose new challenges for energy-efficient network management. Energy harvesting technique is put forward as a relatively new energy saving concept. However, due to the opportunistic nature of energy harvesting, the uncertainty and complexity will be introduced in energy harvesting SCNs (EH-SCNs) network management. In this paper, we study the self- organized cell operation management problem with different quality of service (QoS) requirements of users, in which the EH-SCNs needs to perform cell activation operation in a distributed manner with the uncertainty of harvested energy. With the assumption of Markovian energy harvesting process, multi-armed bandit game (MAB) based Thompson Sampling algorithm is developed to solve the small cell activation problem with a self-organized manner in EH-SCNs. Simulation results show that our proposed approach is particularly suitable to manage the large-scale EH-SCNs more efficiently under uncertain environment with incomplete information.