A time domain approach to sleep modes in Dense Cellular Networks

Abstract

Data traffic continues to increase exponentially and operators are continuously upgrading their networks to meet this demand. The resulting capital and operational expenditures have limited operator revenues. The associated energy costs and carbon-dioxide emissions have raised economic and ecological concerns. In this paper, we use system-level simulations to investigate different sleep mode mechanisms that can address both capacity and energy efficiency (EE) objectives in dense small cell networks (DenseNets). Using real network traffic, we determine a long-term traffic profile that can be used to adapt energy consumption to the prevailing traffic conditions. We then design an adoptive approach that determines the required base station (BS) density in response to variable long-term traffic profile. Our analysis shows that significant energy savings are achieved when sleep mode schemes are made traffic-aware.