Genetic Algorithm-Based Small Cell Switch-Off at Low Load Traffic for Energy-Efficient Heterogeneous Network
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Date
2023-09
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Addis Ababa University
Abstract
The capacity of macro base stations (MBS) is insufficient to handle
the growing traffic, particularly in densely populated cities like Addis
Ababa, Ethiopia. To address this, small cells are deployed then the cellular
network become heterogeneous cellular networks (HetNets). Het-
Nets combines MBS with low power small cells to enhance system capacity.
However, the increasing energy consumption of cellular networks
poses a challenge, leading to a growing interest in energy efficiency
among network operator.
To mitigate this, a technique called small base station (SBS) on or off
switching is employed to conserve energy and improve network efficiency.
By turning off or putting unused cells into sleep mode during
periods of low traffic, power consumption can be reduced. When traffic
or user demand increases, these cells can be switched back on to accommodate
the higher demand.
The thesis proposes a switch-on and off technique based on traffic load
fluctuations using a Genetic Algorithm (GA). It balances energy savings
and network performance, returning the base station to operational status
when traffic or demand increases. A simulation using Matlab (2021a)
shows the algorithm can save 24% of energy consumption, enhancing
system efficiency. The performance improvement of small cells is evaluated.
Description
Keywords
Small cells, energy consumption, base station, HetNets, energy efficiency,Genetic Algorithm, network capacity.