Density-Aware, Energy- and Spectrum-Efficient Small Cell Scheduling

Abstract

Future mobile networks have to be densified by employing small cells to handle the upsurge in traffic load. Although the amount of energy each small cell consumes is low, the total energy consumption of a large-scale network may be enormous. To enhance energy efficiency, we have to adapt the number of active base stations to the offered traffic load. Deactivating base stations may cause coverage holes, degrade the quality of service and throughput while redundant base stations waste energy. That is why we have to adapt the network to an effective density. In this paper, we show that achieving an optimal solution for adapting the density of base stations to the demand is NP-hard. We propose a solution that consists of two heuristic algorithms: a base station density adaptation algorithm and a cell-zooming algorithm that determines which base stations must be kept active and adapts transmit power of base stations to enhance throughput, energy, and spectral efficiency. We employ a multi-access edge cloud for taking a snapshot of the network state in nearly real time with a wider perspective and for collecting network state over a large area. We show that the proposed algorithm conserves energy up to 12% while the spectral efficiency and network throughput can be enhanced up to 30% and 26% in comparison with recent works, respectively.