Consequently, deploying more base stations is necessary for 5G base stations to cover the same area. Macro and micro base stations are currently being deployed for 5G network. The base station is categorized into micro base station, macro base station, and sub-system based on the coverage range.
In terms of energy consumption, 5G base stations require continuous operation and stability, which leads to significant electricity consumption (Guo et al., 2022a). This power is mainly supplied by transmission equipment and auxiliary equipment, such as transformers, UPS power supplies, and cooling equipment.
Thus, deploying macro base stations on a large scale is not feasible for 5G networks. Micro base stations, on the other hand, are smaller and more flexible, allowing them to supplement the peripheral communication that cannot be covered by macro stations, thereby improving communication quality and capacity.
The equipment of both 5G macro and micro base stations typically consist of baseband units, radio frequency units, antenna feeder systems, basic components, iron towers and poles, power supply, air conditioning, and computer rooms (Chen et al., 2010; Igor, 2007).
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Based on the microgrid operation structure, 5G base station and multi-objective problem algorithm, a multi-objective optimization operation model of microgrid access to 5G …
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As a key technology of the fifth-generation communication technology, 5G base stations bring high-speed communication and high electricity costs. The current development …
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This paper concludes that in the case of large-scale coverage of macro base stations, micro base stations supplement signal blind spots. Finally, the work gives forward …
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Base stations, which serve as the backbone of wireless networks, consume 60% of the total energy consumed by such networks, and 3G and 4G base stations alone account for …
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The 5G micro base station market will produce more suppliers to provide support for basic technology such as chips and software systems, Beijing New Energy Consulting said.
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It optimizes target values as are trade-offs at different user distribution probabilities to improve adaptation to different user distribution scenarios. An energy deployment algorithm …
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