An Energy-Efficient Ultra-Dense Network Cell Coverage Adjustment Algorithm

Abstract

Ultra-dense network (UDN) plays a key role in 5G networks to provide ultra-high speed, ultra-low latency data services by densely deploying multiple small cells in specific areas. Numerous small cells can increase network capacity and improve quality of service (QoS), while the network structure has become more complex. Due to the large number of mobile users and frequent handover in these areas, the traffic demand varies rapidly over time. It induces a severe imbalance of mobile traffic load among small cells. The users suffering from load imbalance require frequent handover, which implies a significant increment in energy consumption. In this paper, we propose the cell range adjustment by biasing reference signal received power (RSRP) to achieve load balancing and higher energy efficiency. The values of bias are determined by considering the amount of cell traffic and cell range inversely proportional to the amount of cell traffic. To estimate the amount of cell traffic, the traffic prediction is performed based on long short-term memory (LSTM) algorithm. Simulation results show that our proposed cell range adjustment algorithm increases the throughput of edge users at the cost of a slight decrease in average signal-to-noise ratio (SNR).