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Simulation results reveal that more than 50% of the energy is consumed by the computation power at 5G small cell base stations (BSs). Moreover, the computation power of 5G small cell BS can approach 800 watt when the massive MIMO (e.g., 128 antennas) is deployed to transmit high volume traffic.
The Small Cell Forum predicts the installed base of small cells to reach 70.2 million in 2025 and the total installed base of 5G or multimode small cells in 2025 to be 13.1 million. “A 5G base station is generally expected to consume roughly three times as much power as a 4G base station.
To get the energy efficiency, in this research work, we have addressed the total power consumption and delay of User Requests (URs) in the small cell as well as 5G small cell BSs with sleeping strategy and N limited scheme. One of the effective ways to reduce the power consumption is introduce BSs sleeping strategy.
Base line small cell base station In cellular networks, to meet the increasing demand of high-data-rate for wireless applications, small cell BSs provide a promising and feasible approach but that consumes more power. The base line of small cell BSs is shown in Fig. 1.
Outdoor base stations integrate all essential systems into a single Integrated Cabinet, designed to endure harsh conditions like direct sunlight, rain, and extreme temperatures. These units protect the equipment while ensuring efficient functionality. Towers are crucial for mounting antennas at high elevations, ensuring wide signal reach.
It becomes a top priority during power outages to maintain data flow. Outdoor base stations integrate all essential systems into a single Integrated Cabinet, designed to endure harsh conditions like direct sunlight, rain, and extreme temperatures. These units protect the equipment while ensuring efficient functionality.
Moreover, we propose a dynamically adjusted quantum genetic algorithm (DAQGA) to optimize base station layout, with coverage and construction cost as objective functions. A signal reception strength metric is introduced to evaluate the effectiveness of the optimal layout.
Therefore, the base station coverage optimization method proposed in this paper effectively mirrors real-world scenarios, visually exposes signal blind spots, and accurately identifies instances where users cannot connect to base stations due to complex environmental factors such as high-rise obstructions or areas beyond the coverage range. Fig. 9.
