A Comparative and Measurement-Based Study on Real-Time Network KPI Extraction Methods for 5G and Beyond Applications

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 next-generation 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 real-time 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 devices. The analysis aims to uncover the strengths, weaknesses, and overall efficacy of these approaches under varying conditions, and highlights 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.

Rapid CNN-Assisted Iterative RIS Element Configuration

Reconfigurable Intelligent Surfaces (RISs) are becoming one of the fundamental building blocks of next-generation wireless communication systems. To that end, RIS phase configuration optimization is an important issue, where finding the most suitable configuration becomes a challenging and resource-consuming task, especially as the number of RIS elements increases. Since exhaustive search is not practical, iterative algorithms are utilized to determine the RIS configuration by sequentially considering all RIS elements, where the best-performing phase shift configuration is obtained for each element. However, each configuration attempt requires receiver performance feedback, leading to higher delay and signaling overhead. Thus, in this paper, a convolutional neural network (CNN) based solution is formulated to rapidly find the phase configurations of the RIS elements. The simulation results for a RIS with $40\times40$ elements imply that the proposed algorithm reduces the number of steps dramatically e.g., from 3200 to 160 for the particular setup. Furthermore, such improvement in complexity is achieved with a slight degradation in performance.

Measurement-based Characterization of Physical Layer Security for RIS-assisted Wireless Systems

There have been recently many studies demonstrating that the performance of wireless communication systems can be significantly improved by a reconfigurable intelligent surface (RIS), which is an attractive technology due to its low power requirement and low complexity. This paper presents a measurement-based characterization of RISs for providing physical layer security, where the transmitter (Alice), the intended user (Bob), and the eavesdropper (Eve) are deployed in an indoor environment. Each user is equipped with a software-defined radio connected to a horn antenna. The phase shifts of reflecting elements are software controlled to collaboratively determine the amount of received signal power at the locations of Bob and Eve in such a way that the secrecy capacity is aimed to be maximized. An iterative method is utilized to configure a Greenerwave RIS prototype consisting of 76 passive reflecting elements. Computer simulation and measurement results demonstrate that an RIS can be an effective tool to significantly increase the secrecy capacity between Bob and Eve.