UAV-Aided Interference Assessment for Private 5G NR Deployments: Challenges and Solutions

Industrial automation has created a high demand for private 5G networks, the deployment of which calls for an efficient and reliable solution to ensure strict compliance with the regulatory emission limits. While traditional methods for measuring outdoor interference include collecting real-world data by walking or driving, the use of unmanned aerial vehicles (UAVs) offers an attractive alternative due to their flexible mobility and adaptive altitude. As UAVs perform measurements quickly and semiautomatically, they can potentially assist in near realtime adjustments of the network configuration and fine-tuning its parameters, such as antenna settings and transmit power, as well as help improve indoor connectivity while respecting outdoor emission constraints. This article offers a firsthand tutorial on using aerial 5G emission assessment for interference management in nonpublic networks (NPNs) by reviewing the key challenges of UAV-mounted radio-scanner measurements. Particularly, we (i) outline the challenges of practical assessment of the outdoor interference originating from a local indoor 5G network while discussing regulatory and other related constraints and (ii) address practical methods and tools while summarizing the recent results of our measurement campaign. The reported proof of concept confirms that UAV-based systems represent a promising tool for capturing outdoor interference from private 5G systems.Comment: 7 pages, 4 figure

5G-SMART D1.5 Evaluation of radio network deployment options

This deliverable results from the work on the radio network performance analysis of the identified use cases and deployment options. Covered topics include latency reduction and mobility features of the 5G NR itself, as well as detailed analysis of the radio network KPIs, such as latency, reliability, throughput, spectral efficiency and capacity. Corresponding trade-offs for the identified deployment options and industrial use cases are quantified with an extensive set of technical results. Also, this deliverable is looking into co-channel coexistence performance analyzed through a real-life measurement campaign and considers performance optimization in presence of a special micro-exclusion zone within a factory.Comment: Deliverable D1.5 of the project 5G For Smart Manufacturing (5G-SMART

Advanced performance monitoring for self-healing cellular mobile networks

This dissertation is devoted to development and validation of advanced per- formance monitoring system for existing and future cellular mobile networks. Knowledge mining techniques are employed for analysis of user specific logs, collected with Minimization of Drive Tests (MDT) functionality. Ever increas- ing quality requirements, expansion of the mobile networks and their extend- ing heterogeneity, call for effective automatic means of performance monitoring. Nowadays, network operation is mostly controlled manually through aggregated key performance indicators and statistical profiles. These methods are are not able to fully address the dynamism and complexity of modern mobile networks. Self-organizing networks introduce automation to the most important network functions, but the opportunity of processing large arrays of user reported perfor- mance data is underutilized. Advanced performance monitoring system developed in the presented re- search considers both numerical and sequential properties of the MDT data for detection of faults. Network malfunctions analyzed in this study are sleeping cells in either physical or medium access layer. A full data mining cycle is em- ployed for identification of problematic regions in the network. Pre-processing with statistical normalization and sliding window methods, both linear and non- linear transformation and dimensionality reduction algorithms, together with clustering and classification methods are used in the discussed research. Sev- eral post-processing and detection quality evaluation methods are proposed and applied. The developed system is capable of fast and accurate detection of non- trivial network dysfunctions and is suitable for future mobile networks, even in combination with cognitive self-healing. As a result, operation of modern mo- bile networks would become more robust, increasing quality of service and user experience

An approach for network outage detection from drive-testing databases

A data-mining framework for analyzing a cellular network drive testing database is described in this paper. The presented method is designed to detect sleeping base stations, network outage, and change of the dominance areas in a cognitive and self-organizing manner. The essence of the method is to find similarities between periodical network measurements and previously known outage data. For this purpose, diffusion maps dimensionality reduction and nearest neighbor data classification methods are utilized. The method is cognitive because it requires training data for the outage detection. In addition, the method is autonomous because it uses minimization of drive testing (MDT) functionality to gather the training and testing data. Motivation of classifying MDT measurement reports to periodical, handover, and outage categories is to detect areas where periodical reports start to become similar to the outage samples. Moreover, these areas are associated with estimated dominance areas to detected sleeping base stations. In the studied verification case, measurement classification results in an increase of the amount of samples which can be used for detection of performance degradations, and consequently, makes the outage detection faster and more reliable.peerReviewe