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.