Topology-aware Self-Energy Configurations Employing Neuron Control for Two-tier Femtocell Networks

Abstract

Exploiting the effective utilization of the small-cell size will become an enabler to meet the demands posed by data explosion and energy crises for the ongoing 5G cellular networks. In this paper, we propose selfoptimizing based energy-efficient solutions for densely deployed and on/off-aided femtocell networks. The proposed scheme aims at achieving high SINR with a tradeoff between energy efficiency (EE) and spectrum efficiency (SE) by adaptively coordinating the downlink transmit power based on a Neuron control process. The optimal coverage of femtocells can be obtained from flexible cell sizing to cater for the instantaneous traffic requirements. Accordingly, unnecessary femtocells can be switched into sleep mode for more energy savings. To cope with the topology uncertainty and deliver the most appropriate access operation, the femtocells are dynamically configured as clusters in which a modified K-means algorithm is performed. The utilities of all the action candidates are jointly estimated by which the coverage extension and on/off operations can be fairly performed with a maximization of the cell-level and cluster-level EE. The numerical results show that the proposed self-optimized solutions can effectively reduce the downlink energy impact with less active ratio and optimal coverage of the femtocells meanwhile satisfying the throughput requirements compared to the conventional configurations with regular and predefined patterns.