HOME / huawei''s world''s first 5g a smart base station technology
It plays a central role in enabling wireless communication between user devices (such as smartphones, IoT devices, etc.) and the core network. The base station in a 5G network is designed to provide high data rates, low latency, massive device connectivity, and improved energy efficiency compared to its predecessors.
5G networks divide coverage areas into smaller zones called cells, enabling devices to connect to local base stations via radio. Each station connects to the broader telephone network and the Internet through high-speed optical fiber or wireless backhaul.
These names originate from the 3GPP study of 5G radio access technologies documented within 3GPP Technical Report 38.801. Both architectures have Base Stations that connect to the 5G Core Network. The 'option 2' architecture is based on a gNode B connected to the 5G Core Network.
5G Base Stations: Compared to 4G base stations, 5G brings higher data throughput and power density, significantly increasing heat generation. Therefore, the performance requirements for thermal materials are much higher. ● Small/Micro Base Stations: These base stations are compact, with limited space, making thermal design more challenging.
The denseness and dispersion of 5G base stations make the distance between base station energy storage and power users closer. When the user's load loses power, the relevant energy storage can be quickly controlled to participate in the power supply of the lost load.
This work explores the factors that affect the energy storage reserve capacity of 5G base stations: communication volume of the base station, power consumption of the base station, backup time of the base station, and the power supply reliability of the distribution network nodes.
For 5G base station energy storage participation in distribution network power restoration, this paper intends to compare four aspects. 1) Comparison between the fixed base station backup time and the methods in this paper.
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.
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