Telecom

Validation geometry changes what a received-power prediction error describes. We benchmark established cross-validation designs on 2,967,199 aggregated 25 m location records from four UK operators and three Ofcom 5G SS-RSRP campaigns. A 10 km blocked, 1 km buffered design, selected using a separate development campaign, is compared with random, training-count-matched random, and unbuffered blocked validation using fixed inverse distance weighting (IDW), LightGBM, and XGBoost models. The buffered design has lower absolute disagreement with a prespecified same-campaign 20 km reference in all 36 original endpoints, reducing mean signed shortfall from 5.31 to 0.71 dB. The mean disagreement benefit is 1.83 dB for the boosted trees and 10.13 dB for IDW; blocking contributes more than the additional buffer. Post-outcome analyses correct exact-distance boundaries and endpoint weights, separate directional coverage errors, and add 12 coordinate-only random forest endpoints. The reference and distance diagnostics remain dependent on the same measurements, and only three campaigns support campaign-level sensitivity. Buffered validation still understates measured error for large contiguous-region holdout and later-campaign transfer by 4.69 and 4.47 dB. These results support reporting realized separation, training counts, and model-specific effects, while choosing validation geometry for the intended transfer setting rather than treating internal reference agreement as a universal deployment guarantee.

The rapid growth of corporate networks and the wide use of “Bring Your Own Device” (BYOD) policies have made these networks harder to secure and manage. One visible consequence is the spread of unauthorized, unmanaged switches and wireless routers. Network Management Systems (NMSs) differ in their polling, topology discovery, and multivendor capabilities. This paper presents an automated framework for multivendor network topology discovery and Shadow IT detection, together with a topological mapping architecture based on concurrent routines written in the Go programming language. The contribution lies in the integration and operational evaluation of established techniques on a heterogeneous campus network rather than in new individual algorithms. A hybrid asynchronous scanning algorithm combining the Internet Control Message Protocol (ICMP), Simple Network Management Protocol (SNMP), Link Layer Discovery Protocol (LLDP), and Address Resolution Protocol (ARP) is used to overlap discovery-stage response waits. We use graph-theoretic models and a heuristic “Health Score” function to classify network anomalies such as loopbacks, Spanning Tree Protocol (STP) conflicts, and unauthorized devices. Virtualization-prefix and cached ESXi-signature checks exclude candidate ports associated with virtualization; Time-To-Live (TTL) values separately estimate the operating system (OS) family. Experiments conducted on a live university campus network showed that the 100-worker asynchronous pool achieved a 94.9-fold discovery-stage speedup over sequential polling. In a replay of a recent forwarding database (FDB) snapshot, the virtualization checks reduced the number of candidate ports with multiple media access control (MAC) addresses from 96 to 72. Detection accuracy against independent labels remains to be evaluated. The framework was further evaluated through a fixed 24-h monitoring window and a functional comparison with established network management platforms. The results demonstrate faster discovery on the evaluated campus network; detection accuracy, resource overhead, and generalization require further validation.

Ensuring Quality of Service (QoS) in complex, large scale, and centralized Software-Defined Networking (SDN) environments remains a major challenge for the scientific community, network administrators or operators, and industry. The SDN networking model promises to improve and optimize QoS in dynamic environments by enhancing network applications related to traffic management. It provides centralized control, flexibility, abstraction, and programmability of network resources. However, these expectations have not been fully met, primarily due to the difficulties associated with integrating or adapting traditional QoS mechanisms (routing, queue management, resource reservation) into the SDN architecture, the limitations of the OpenFlow protocol, and the highly dynamic nature of modern networks. Given the wide variety of networking environments, such as Internet of Things (IoT), data centers, cloud compounding, fog computing, 5G/6G networks and others, the core challenge is the lack of a standardized workflow for the integration of QoS mechanisms into SDN. Previous studies primarily focus on specific QoS mechanisms in SDN, particular application domains, or architectural aspects, offering limited comprehensive comparisons of QoS requirements, implementation approaches, evaluation methodologies, and the limitations of individual studies. To address these gaps, this study combines bibliometric analysis with a systematic review of selected publications from 2020 to 2026, providing an up-to-date and comprehensive overview of the latest research advances, emerging trends, and technological developments in the field. Furthermore, it critically examines the QoS requirements, implementation approaches, evaluation methodologies, and limitations reported in the selected studies, thereby offering a structured synthesis of the current state of the art and identifying promising directions for future research.