DBSCAN-based energy consumption pattern clustering identification method for 5G base-station

Lijun Zhong; Minda Shi; Zhenyu Huang; Peizhe Xin; Jing Jiang; Guocheng Li; Jun Lu

doi:10.1117/12.2631595

1 May 2022 DBSCAN-based energy consumption pattern clustering identification method for 5G base-station

Lijun Zhong, Minda Shi, Zhenyu Huang, Peizhe Xin, Jing Jiang, Guocheng Li, Jun Lu

Proceedings Volume 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021); 121711D (2022) https://doi.org/10.1117/12.2631595
Event: Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 2021, Shanghai, China

Abstract

To fully understand the energy consumption characteristics of 5G base-station, a DBSCAN-based energy consumption pattern clustering identification method is proposed for 5G base-station. Firstly, this paper analyzes the daily-curve characteristics of power consumption behavior in typical application scenarios of 5G base-station for further pattern clustering identification. Then, the proposed pattern clustering identification method is depicted based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering decision, which is composed of the feature extraction for power consumption daily-curve of 5G base-station. Finally, the experiment is implemented using actual operation data of 5G base-station as data source. The experiment results illustrate that the proposed method can effectively identify the clustering characteristics of the energy consumption behavior for 5G base-station.

Citation Download Citation

Lijun Zhong, Minda Shi, Zhenyu Huang, Peizhe Xin, Jing Jiang, Guocheng Li, and Jun Lu "DBSCAN-based energy consumption pattern clustering identification method for 5G base-station", Proc. SPIE 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 121711D (1 May 2022); https://doi.org/10.1117/12.2631595

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