  Dense cellular networks (DenseNets) are fast becoming a reality with the
rapid deployment of base stations (BSs) aimed at meeting the explosive data
traffic demand. In legacy systems however this comes with the penalties of
higher network interference and energy consumption. In order to support network
densification in a sustainable manner, the system behavior should be made
'load-proportional' thus allowing certain portions of the network to activate
on-demand. In this work, we develop an analytical framework using tools from
stochastic geometry theory for the performance analysis of DenseNets where
load-awareness is explicitly embedded in the design. The model leverages on a
flexible cellular network architecture where there is a complete separation of
the data and signaling communication functionalities. Using the proposed model,
we identify the most energy- efficient deployment solution for meeting certain
minimum service criteria and analyze the corresponding power savings through
dynamic sleep modes. Based on state-of-the-art system parameters, a homogeneous
pico deployment for the data plane with a separate layer of signaling
macro-cells is revealed to be the most energy-efficient solution in future
dense urban environments.
