Clustering-based time-domain power control algorithm for improving energy efficiency in dense small cell network

As traditional wireless cellular networks are facing a rapid growth of traffic demand, the future networks may consist of a large number of small cells which are densely deployed. However, a large number of small cell Base Stations (BSs) will consume a lot of energy, which is not conductive to improve the network Energy Efficiency (EE). The purpose of our research is to optimize the dense small cell network´s power consumption and improve the network throughput at the same time. To solve the problem, we use the Graph Coloring Algorithm (GCA) to divide the small cells into different clusters to mitigate the serious co-tier interference between the dense small cells, and accordingly reducing the transmission power of small cell BSs in some subframes based on the proposed utility function. The existence of the optimal solutions to the problem is discussed, then the Differential Evolution (DE) is applied to search the optimal transmission power which maximizes the proposed utility function. System level simulation results show that the proposed algorithm can significantly improve the network EE by improving the network throughput and optimizing the network´s power consumption.