Large-scale testing infrastructures are critical for validating telecommunication systems, yet their growing complexity makes efficient resource utilization and anomaly detection increasingly challenging. In reservation-based testbed environments, errors in resource allocation or preparation often manifest as abrupt spikes or regime changes in time-based metrics. This paper proposes a scalable, unsupervised framework for real-time anomaly detection in such environments. We introduce CALM (Continuous Anomaly Localization for univariate and Multivariate data), a nonparametric method based on kernel density estimation and bootstrap-based thresholding, designed for anomaly detection at the individual testbed level. To address system-wide visibility, we further propose AggCALM, an aggregation framework that consolidates local anomaly signals across multiple testbeds to detect statistically significant global anomalies while mitigating alarm fatigue. The methodology is evaluated using simulated multivariate data and real-world data from a large-scale base station testing platform. Results demonstrate that the proposed framework enables timely, flexible, and accurate anomaly detection without requiring labeled data, supporting reliable operation of complex test environments. Although the proposed methodology is presented within the context of a telecommunication testing labs, it can be effectively used to other applications, such as condition monitoring, where anomaly detection serves as a pivotal pre-processing step for diagnostic signals.
Integrated Sensing and Communication (ISAC) combines sensing and communication to efficiently utilize wireless resources and is emerging as a key paradigm for next-generation wireless networks. By leveraging the wide bandwidth, high frequencies, and massive antenna arrays of 5G-Advanced and 6G systems, ISAC enables physical-layer sensing using Channel State Information (CSI). The 3rd Generation Partnership Project (3GPP) Release 19 identifies 32 potential ISAC use cases, with particular emphasis on detecting and tracking moving objects. In this work, we address the Sensing for Railway Intrusion Detection use case, where intruders, including wildlife, entering a railway track can pose serious collision risks. We generated 22,695 CSI matrices with corresponding ground truth using a 3D-rendered railway environment and the Sionna radio simulator. We developed a machine learning model combining a three-dimensional Convolutional Neural Network (3D CNN) and Bidirectional Long Short-Term Memory (BiLSTM) network to detect intruders in the track danger zone and estimate their real-time position relative to the train, velocity, and time to collision. On synthetic CSI data, the model achieves 99.57
The proliferation of heterogeneous traffic with divergent Quality of Service (QoS) requirements in 5G/6G networks necessitates fundamentally new resource allocation strategies. The existing deep reinforcement learning methods suffer from high training complexity, while heuristic approaches lack adaptability. We propose COATI (Chaotic Optimization Algorithm with Traffic Intelligence), a metaheuristic framework that synergizes the Coati Optimization Algorithm (COA) with chaotic dynamics and traffic-aware operators. Formulated as a mixed integer nonlinear program (MINLP), COATI introduces: (i) logistic map-based chaotic initialization for diversity preservation, (ii) adaptive Lévy flights guided by buffer status reports for dynamic exploration–exploitation trade-offs, and (iii) a QoS-aware fitness function incorporating proportional fairness constraints. Simulations based on 3GPP scenarios demonstrate that COATI reduces URLLC latency violations by 38
Aluminum alloy AA6061 is broadly adopted in automobile, avionic, marine, military, plus telecommunication industries on account of its excellent specific strength and deterioration resistance. However, its limited wear resistance restricts its broader application in high-friction environments. This study investigates the influence of lanthanum oxide (La2O3) nanoparticles as reinforcement on the mechanical as well as wear traits of AA6061. Compounds were fabricated via stir casting followed by extrusion, with La2O3 weight fractions ranging from 0.5 to 1.5 wt.
5G+ (5G and beyond) mobile networks are increasingly complex, making their monitoring and management challenging. This paper presents a PhD research focused on automating 5G+ network performance assessment through a family of Multimodal Data Analysis Methods (MDAM) in 5G+ cellular networks. We identify limitations of unimodal approaches, describe the design-science methodology adopted, and present the Module for Automated and Context-Preserving Telecommunication Network State Analysis (MACNSA) – a modular analytics platform hosting MDAM. The first MDAM implemented within MACNSA is an agentic RAG-based system (Agentic-RAG-MDAM) that fuses numerical KPI data with textual feature documentation. The evaluation protocol, based on both traditional information retrieval and LLM-as-a-judge metrics, is described. Preliminary experiments on real operator data demonstrate that Agentic-RAG-MDAM significantly reduces per-feature analysis time compared to traditional human expert approaches. Current limitations and planned future work are discussed.
