Abstract
Energy efficiency (EE) in cellular networks is a growing concern for cellular operators regarding maintaining profitability and reducing their overall environmental impact. Because evolved Node Bs (eNBs) for long-term evolution wireless cellular networks are deployed to accommodate peak traffic, they are underutilized most of the time, especially under low-traffic conditions. Hence, switching eNBs on/off in accordance with traffic-pattern variations is considered an effective method of improving EE in cellular networks. However, two main concerns of network operators when applying this technique are coverage issues and securing radio service for the entire area in response to the increased size of some cells to provide coverage for cell areas that are switched off. This study focuses on the parameters that affect coverage to find a balance between cellular network energy consumption and cell-coverage area. To achieve this goal, particle swarm optimization, a bio-inspired computational method, has been adopted in this study to maximize the cell-coverage area under constraints for the transmission power of the eNB (Ptx), the total antenna gain (G), the bandwidth (BW), the signal-to-interference-plus-noise ratio (SINR), and shadow fading (σ). The results show that this method can achieve daily energy savings of up to 34.77, while guaranteeing full coverage within the cell area.
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