Genetic Algorithm-Based Small Cell Switch-Off at Low Load Traffic for Energy-Efficient Heterogeneous Network

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Addis Ababa University


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.



Small cells, energy consumption, base station, HetNets, energy efficiency,Genetic Algorithm, network capacity.