Ensuring the integrity and reliability of optical fiber networks is essential for modern global communication systems, as faults such as fiber cuts, malicious tapping, and connector degradation can result in significant service disruptions and security vulnerabilities. Traditional fault detection methods, such as optical time-domain reflectometry (OTDR), face challenges due to noisy signals, signal attenuation, and reliance on expert interpretation. Additionally, existing machine learning approaches often struggle with generalization across diverse fault types and imbalanced datasets. This paper introduces a novel Hybrid CNN-Ensemble framework, combining convolutional neural networks (CNNs) for deep feature extraction with ensemble learning techniques including XGBoost, Support Vector Machines (SVM), and Random Forest (RF). The framework processes CNN-extracted features from OTDR traces, with predictions aggregated through a meta-classifier. Validated on a comprehensive OTDR dataset covering eight distinct fault categories, the proposed method achieves 99.5% accuracy and an F1-score of 0.99, offering exceptional precision in detecting and classifying fiber faults. The results demonstrate the framework’s ability to improve fault detection performance, streamline fiber monitoring processes, reduce operational costs, and enhance network resilience to both physical attacks and accidental failures.

Non-Line-of-Sight (NLOS) reception is acknowledged as a primary source of positioning error in Global Navigation Satellite System (GNSS) applications, particularly within dense urban settings. When the direct satellite path is obstructed by buildings and other structures, receivers must depend on the reflected signals. This reliance results in significant pseudorange biases and a diminished localization accuracy. Methods based on learning have been developed to classify signals as Line-of-Sight (LOS) or NLOS using signal features; however, many approaches depend on external sensors, heuristic thresholds, or hand-crafted features, restricting their scalability and adaptability. A hybrid deep learning (DL) framework is proposed to address these constraints, integrating a Convolutional Neural Network (CNN) with an Autoencoder to improve LOS/NLOS signal classification utilizing raw GNSS measurements. The model is trained and evaluated using the UrbanNav dataset, which offers dual-frequency multi-GNSS observations from urban canyons in Hong Kong and Tokyo. The training process encompasses feature normalization, class balance, and structured learning, with optimization conducted through the Adam optimizer and dynamic learning rate scheduling. It has been demonstrated through experimental results that the proposed approach achieves a global classification accuracy of 99.90%. For LOS signals, an accuracy of 99.95% is recorded, along with a precision, recall, and F1 score of 100%. In contrast, NLOS signals are accurately captured with 100% in all evaluation criteria. These outcomes highlight the robustness and precision of the proposed method, rendering it highly suitable for applications necessitating reliable urban positioning, such as autonomous driving, UAV localization, and pedestrian navigation.

This work presents a comprehensive investigation of a Multiple-Input Multiple-Output (MIMO) antenna incorporating a Perforated Dielectric Gradient Surface (PDGS) and a Partially Reflective Surface (PRS) for millimeter-wave (mmWave) applications. The proposed design is based on a single cylindrical resonator antenna (CDRA) as a resonant element. It operates over the 24 GHz to 29 GHz frequency range and addresses critical challenges such as gain enhancement, mutual coupling suppression, and directional beam shaping. The PDGS introduces localized electromagnetic bandgaps, suppressing surface wave propagation and minimizing inter-port interference, while the PRS facilitates constructive interference, enhancing radiation efficiency and directivity. Extensive simulation and measurement results confirm the effectiveness of the combined PRS and PDGS integration, demonstrating improved reflection parameters, enhanced realized gain (with a peak gain increase of 8 dBi at 27 GHz), and separated radiation patterns ±30° at 26.5 GHz and 28.5 GHz. The design achieves excellent isolation, with coupling parameters consistently below −20 dB and impedance matching across all ports. These results highlight the suitability of the proposed MIMO antenna for next-generation 5G in mmWave spectrum.

This paper tackles significant challenges in the design of millimeter-wave MIMO (Multi-input Multi-output) antennas for the Ka-band (26-30 GHz) as utilized in 5G applications. Current antenna designs suffer from limitations such as constrained bandwidth, inadequate gain, and restricted beam scanning capabilities, adversely affecting performance, particularly in high-density urban environments. A novel MIMO antenna featuring a 12-port dielectric lens has been proposed to address these issues, introducing several advancements to improve key performance metrics. The antenna demonstrates a peak realized gain of approximately 14 dBi and achieves a bandwidth exceeding 4 GHz. Importantly, it features full 360∘ beam scanning capabilities and maintains an exceptionally low envelope correlation coefficient (ECC) of less than 0.0014. The design also shows impressive radiation efficiency consistently exceeding 95%, complemented by excellent isolation characteristics with mutual coupling maintained below -30 dB. This research provides a scalable solution that could significantly enhance the performance of next-generation wireless networks, addressing the high demands of urban 5G deployments.