Boosting Efficiency in 5G Base Stations
In 5G macro base stations, nanocrystalline inductors have reduced overall power consumption by 18%, according to GSMA Intelligence''s 2025 5G Infrastructure Efficiency
According to the energy consumption characteristics of the base station, a 5G base station energy consumption prediction model based on the LSTM network is constructed to provide data support for the subsequent BSES aggregation and collaborative scheduling.
In this paper, we thoroughly study the base station control problem in 5G ultra-dense networks and propose an innovative MAPPO algorithm. The algorithm significantly reduces the overall power consumption of the system by optimizing inter-base station collaboration and interference management while guaranteeing user QoS.
• The 5G base station energy consumption prediction model based on LSTM proposed in this paper takes into account the energy consumption characteristics of 5G base stations. The prediction results have high accuracy and provide data support for the subsequent research on BSES aggregation and optimal scheduling.
With the rapid development of 5G base station construction, significant energy storage is installed to ensure stable communication. However, these storage re...
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