A Comparative and Measurement-Based Study on Real-Time Network KPI Extraction Methods for 5G and Beyond Applications
Year: 2025
Type: conference-paper
Abstract: Key performance indicators (KPIs), which can be extracted from the standardized interfaces of network equipment defined by current standards, constitute a primary data source that can be leveraged in the development of non-standardized new equipment, architectures, and computational tools. In nextgeneration technologies, the demand for data has evolved beyond the conventional log generation or export capabilities provided by existing licensed network monitoring tools. There is now a growing need to collect such data at specific time intervals and with defined granularities. At this stage, the development of realtime KPI extraction methods and enabling their exchange between both standardized/commercialized and non-standardized components or tools has become increasingly critical. This study presents a comprehensive evaluation of three distinct KPI extraction methodologies applied to two commercially available customer premises equipment. By implementing all methods within an open-source computation and processing platform, the analysis aims to uncover the strengths, weaknesses, and overall efficacy of these approaches under varying conditions, and to highlight the critical insights into the practical capabilities and limitations. The findings serve as a foundational guide for the seamless integration and robust testing of novel technologies and approaches within commercial telecommunication networks. This work aspires to bridge the gap between technological innovation and real-world applicability, fostering enhanced decision-making in network deployment and optimization, and the outcomes of the study clearly demonstrates that the KPI extraction method–network equipment pair must be selected in accordance with the requirements of the beyond 5G application.
Authors: Batuhan Kaplan, Samed Keşır, Ahmet Faruk Coşkun
Institution: Turkcell (Turkey)
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416677602