| dc.description.abstract | Mobile tra c demand has been increasing exponentially over the last few years and forecasts show that this trend will continue in the foreseeable future. As a result, operators are forced to expand and upgrade their networks to meet this demand and enhance capacity. Since cellular networks are designed to support peak tra c at all times, this means that even during the low tra c periods, the idle cells will still operate which wastes energy and hence the need for energy- e cient techniques. In this project, MATLAB has been used to simulate a spatiotemporal pro le created based on real-time tra c that was obtained from one of the mobile network operators. Deployment optimization for the HetNet was then performed based on coverage probability constraints. Using Monte Carlo simulations, an adoptive approach that determines the required BS density in response to a variable long term tra c pro le was designed. A novel sleep mode technique called the strategic sleep mode technique is analyzed to determine its ability to adapt energy consumption to variable BS density. The results of the strategic sleep mode strategy are compared with the existing sleep mode techniques which are; random and conventional sleep mode. The results obtained show that sleep mode is a viable solution for managing network energy consumption in dense HetNets. | en_US |
