Abstract:
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The green communications paradigm has been receiving
much attention in wireless networks in recent years.
More specifically, in the context of cellular communications, the
concept of Cell Switch Off (CSO) has been recognized as a
promising approach to reduce the energy consumption. The need
is expected to be pressing especially in the next decade with the
increasing small cell deployment. However, the cell switch on/off
decisions compounded by the resource allocation task in CSO
constitute a highly challenging optimization problem due to the
fact that this problem can be viewed as a generalized version of
the resource allocation (scheduling) problem in the conventional
cellular networks without CSO, which itself is already difficult.
This paper introduces a novel framework to CSO based on
multiobjective evolutionary optimization.
The main contribution of this paper is that the proposed
multiobjective framework takes the traffic behaviour in both
space and time (known by operators) into account in the optimal
cell switch on/off decision making which is entangled with the
corresponding resource allocation task. The exploitation of this
statistical information in a number of ways, including through
the introduction of a weighted network capacity metric that
prioritizes cells which are expected to have traffic concentration,
results in on/off decisions that achieve substantial energy savings,
especially in dense deployments where traffic is highly unbalanced,
without compromising the QoS. The proposed framework
distinguishes itself from the CSO papers in the literature in two
ways: 1) The number of cell switch on/off transitions as well
as handoffs are minimized. 2) The computationally-heavy part
of the algorithm is executed offline, which makes the real-time
implementation feasible. |