Traffic prediction enabled dynamic access points switching for energy saving in dense networks

Abstract

To meet the ever-increasing traffic demand and enhance the coverage of cellular networks, network densification is one of the crucial paradigms of 5G and beyond mobile networks, which can improve system capacity by deploying a large number of Access Points (APs) in the service area. However, since the energy consumption of APs generally accounts for a substantial part of the communication system, how to deal with the consequent energy issue is a challenging task for a mobile network with densely deployed APs. In this paper, we propose an intelligent AP switching on/off scheme to reduce the system energy consumption with the prerequisite of guaranteeing the quality of service, where the signaling overhead is also taken into consideration to ensure the stability of the network. First, based on historical traffic data, a long short-term memory method is introduced to predict the future traffic distribution, by which we can roughly determine when the AP switching operation should be triggered; second, we present an efficient three-step AP selection strategy to determine which of the APs would be switched on or off; third, an AP switching scheme with a threshold is proposed to adjust the switching frequency so as to improve the stability of the system. Experiment results indicate that our proposed traffic forecasting method performs well in practical scenarios, where the normalized root mean square error is within 10%. Furthermore, the achieved energy-saving is more than 28% on average with a reasonable outage probability and switching frequency for an area served by 40 APs in a commercial mobile network.

Keywords

• Access points switching on/off
• Energy-saving
• Green network
• Long short-term memory
• Traffic prediction