Search Results (759)

Free-space optical (FSO) communication is a promising alternative to radio-frequency (RF) and optical fiber systems due to its high data rates and large bandwidth. However, its performance is highly susceptible to atmospheric conditions such as fog, rain, snow, and haze. This paper analyzes fog-induced signal attenuation in the Morogoro and Mbeya regions of Tanzania using the Kim and Kruse attenuation models. To improve link performance, a quadrature amplitude modulation (QAM) multiple-input multiple-output (MIMO) FSO link was designed and analyzed using OptiSystem 22.0. In Mbeya, light fog conditions with 0.5 km visibility resulted in an attenuation of 32 dB/km, a bit error rate (BER) of 4.5 × 10⁻²³, and a quality factor of 9.79 over a 2.62 km link. In Morogoro, dense fog with 0.05 km visibility led to an attenuation of 339 dB/km, a BER of 1.12 × 10⁻¹⁵, and a maximum link range of 0.305 km. Experimental measurements were further conducted under clear, moderate, and dense fog conditions to systematically evaluate the FSO link performance. The results demonstrated that MIMO techniques significantly enhanced link performance by mitigating fog effects. Moreover, a dedicated application was developed to analyze transmission errors and evaluate system performance metrics. Additionally, a mathematical model of the FSO link was developed to describe and forecast the performance of the MIMO FSO system in atmospheric conditions impacted by fog. Full article

Cybersecurity in radio frequency (RF) technologies has become a critical concern, driven by the expansion of connected systems in urban and industrial environments. Although research on wireless networks and the Internet of Things (IoT) has advanced, comprehensive studies that provide a global and integrated view of cybersecurity development in this field remain limited. This work presents a scientometric and systematic review of international publications from 2009 to 2025, integrating the PRISMA protocol with semantic screening supported by a Large Language Model to enhance classification accuracy and reproducibility. The analysis identified two interdependent axes: one focusing on signal integrity and authentication in GNSS systems and cellular networks; the other addressing the resilience of IoT networks, both strongly associated with spoofing and jamming, as well as replay, relay, eavesdropping, and man-in-the-middle (MitM) attacks. The results highlight the relevance of RF cybersecurity in securing communication infrastructures and expose gaps in widely adopted technologies such as RFID, NFC, BLE, ZigBee, LoRa, Wi-Fi, and unlicensed ISM bands, as well as in emerging areas like terahertz and 6G. These gaps directly affect the reliability and availability of IoT and wireless communication systems, increasing security risks in large-scale deployments such as smart cities and cyber–physical infrastructures. Full article

The metric for probing the variation in atmospheric refractive indices is radio refractivity (RR), which is a key factor in determining the losses associated with a radio signal as it traverses from one atmospheric layer to another. Ten years (2015–2024) of surface hourly data of temperature (K), pressure (P), and relative humidity (RH) obtained from ERA-5 reanalysis were used for RR computations based on ITU-R models. Twelve major cities of South Africa were benchmarked for the study. Time series plots of the overall ten-year RR hourly mean were generated for the cities. The correlation coefficient (R) between RR and RH was investigated. The results indicate the highest and lowest RR of 360.94 and 301.09 (N-Units) in Pietermaritzburg and Kimberly, respectively, with a range of 59.85 over the country. In the southern coast, Pietermaritzburg recorded the highest and lowest values of 360.14 and 325.52 (N-Units) at 21:00 and 11:00 hrs., followed by Durban with 348.55 and 339.44 at 17:00 and 10:00 hrs., Bhisho with 346.88 and 320.622 at 00:00 and 11:00 hrs., and Cape Town with 328.54 and 322.47 (N-Units) at 00:00 and 10:00 hrs., respectively. In the central region, Bloemfontein recorded values of 344.97 and 305.58 at 04:00 and 13:00 hrs., respectively, while Kimberly recorded 338.06 and 301.09 at 04:00 and 13:00 hrs., respectively. In the northern region, Johannesburg recorded the highest and lowest values of 358.79 and 318.56 (N-Units) at 03:00 and 13:00 hrs., respectively; Pretoria recorded values of 352.25 and 316.76 at 04:00 and 13:00 hrs., respectively; Emalahleni recorded values of 358.79 and 318.95 at 03:00 and 13:00 hrs., respectively; and Polokwane recorded values of 357.59 and 320.82 at 03:00 and 13:00 hrs., respectively. Mahikeng recorded values of 346.70 and 311.37 at 04:00 and 13:00 h, while Mbombela recorded values of 360.11 and 329.17 (N-Units) at 00:00 and 12:00 h, respectively. The implications of these results are a higher refractive attenuation effect of terrestrial transmitted radio signals in cities with higher RR and during the early morning, evening, and night hours of the day. A high positive (R) of 0.84 to 0.99 was observed between RR and RH across the country. A geo-spatial RR contour map was generated for the study stations for practical applications and could be helpful in cities where the contour passes within South Africa. These findings should be taken into consideration in the design and reappraisal of terrestrial radio-link and power budgets to ensure quality of service. The overall findings provide practical applications for mitigating RR-prone attenuation on terrestrial radio channels, such as Radio and Television broadcasting, GSM, and microwave link systems, among others, across South Africa and other countries with similar geography and climate. Full article

Satellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into a predictive tool for rainfall prediction. Unlike conventional single-model approaches treating all atmospheric conditions uniformly, our methodology employs K-Means Clustering with the Elbow Method to identify four distinct atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns from a 12-m Ku-band satellite ground station at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, combined with absolute pressure and hourly rainfall measurements. The dataset comprises 98,483 observations collected with 30-s temporal resolutions, providing comprehensive coverage of diverse tropical atmospheric conditions. The experimental platform integrates three subsystems: a receiver chain featuring a Low-Noise Block (LNB) converter and Software-Defined Radio (SDR) platform for real-time data acquisition; a control system with two-axis motorized pointing incorporating dual-encoder feedback; and a preprocessing workflow implementing data cleaning, K-Means Clustering (k = 4), Synthetic Minority Over-Sampling Technique (SMOTE) for balanced representation, and standardization. Specialized Long Short-Term Memory (LSTM) networks trained for each identified cluster enable capture of regime-specific temporal dynamics. Experimental validation demonstrates substantial performance improvements, with cluster-specific LSTM models achieving R² values exceeding 0.92 across all atmospheric regimes. Comparative analysis confirms LSTM superiority over RNN and GRU. Classification performance evaluation reveals exceptional detection capabilities with Probability of Detection ranging from 0.75 to 0.99 and False Alarm Ratios below 0.23. This work presents a scalable approach to weather radar systems for tropical regions with limited ground-based infrastructure, particularly during rapid meteorological transitions characteristic of tropical climates. Full article

As global airspace operations grow increasingly complex, the risk of near-mid-air collisions (NMACs) poses a persistent and critical challenge to aviation safety. Traditional collision-avoidance systems, while effective in many scenarios, are limited by rule-based logic and reliance on transponder data, particularly in environments featuring diverse aircraft types, unmanned aerial systems (UAS), and evolving urban air mobility platforms. This paper introduces a novel, integrative machine learning framework designed to analyze NMAC incidents using the rich, contextual information contained within the NASA Aviation Safety Reporting System (ASRS) database. The methodology is structured around three pillars: (1) natural language processing (NLP) techniques are applied to extract latent topics and semantic features from pilot and crew incident narratives; (2) cluster analysis is conducted on both textual and structured incident features to empirically define distinct typologies of NMAC events; and (3) supervised machine learning models are developed to predict pilot decision outcomes (evasive action vs. no action) based on integrated data sources. The analysis reveals seven operationally coherent topics that reflect communication demands, pattern geometry, visibility challenges, airspace transitions, and advisory-driven interactions. A four-cluster solution further distinguishes incident contexts ranging from tower-directed approaches to general aviation pattern and cruise operations. The Random Forest model produces the strongest predictive performance, with topic-based indicators, miss distance, altitude, and operating rule emerging as influential features. The results show that narrative semantics provide measurable signals of coordination load and acquisition difficulty, and that integrating text with structured variables enhances the prediction of maneuvering decisions in NMAC situations. These findings highlight opportunities to strengthen radio practice, manage pattern spacing, improve mixed equipage awareness, and refine alerting in short-range airport area encounters. Full article

With the increasing demand for high throughput and ultra-dense small cell deployment in the next-generation communication networks, spectrum resources are becoming increasingly strained. At the same time, the security risks posed by eavesdropping remain a significant concern, particularly due to the broadcast-access property of optical fronthaul networks. To address these challenges, we propose a high-security, high-spectrum efficiency radio-over-fiber (RoF) system in this paper, which leverages compressive sensing (CS)-based algorithms and chaotic encryption. An 8 Gbit/s RoF system is experimentally demonstrated, with 10 km optical fiber transmission and 20 GHz radio frequency (RF) transmission. In our experiment, spectrum efficiency is enhanced by compressing transmission data and reducing the quantization bit requirements, while security is maintained with minimal degradation in signal quality. The system could recover the signal correctly after dequantization with 6-bit fronthaul quantization, achieving a structural similarity index (SSIM) of 0.952 for the legitimate receiver (Bob) at a compression ratio of 0.75. In contrast, the SSIM for the unauthorized receiver (Eve) is only 0.073, highlighting the effectiveness of the proposed security approach. Full article

This paper presents a novel method for the precise localization of remote radio-signal sources using a formation of unmanned aerial vehicles (UAVs). The approach is based on time-difference-of-arrival (TDoA) measurements and the geometric analysis of hyperbolas formed by pairs of UAVs. By studying the asymptotic intersections of these hyperbolas, the method ensures unique determination of the source position, even in the presence of multiple intersection points. Theoretical analysis confirms that the correct intersection point is located at a significantly larger distance from the UAV formation center compared to spurious intersections, providing a rigorous criterion for resolving localization ambiguity. The proposed framework also addresses secure inter-UAV communication via optical-fiber links and supports expansion of UAV groups with directional antennas and low-power signal relays. Additionally, the study discusses practical UAV configurations, including hybrid propulsion and jet-assisted kamikaze platforms, demonstrating the applicability of the method in contested environments. The results indicate that this approach provides a robust mathematical basis for unambiguous emitter localization and enables scalable, secure, and resilient multi-UAV systems, with potential applications in electronic-warfare scenarios, surveillance, and tactical operations. Full article

This work presents an experimental evaluation of a high-speed analog-to-digital conversion method based on passive reference waveform crossings combined with time-to-digital converter (TDC) time-tagging. Unlike conventional level-crossing event-driven analog-to-digital converters (ADCs) that require dynamically updated digital-to-analog converters (DACs), the proposed architecture compares the input waveform against a broadband periodic sampling function without active threshold control. Crossing instants are detected by a high-speed comparator and converted into rising and falling edge timestamps using a multi-channel TDC. A commercial ScioSense GPX2-based time-tagger with 30 ps single-shot precision was used for validation. A range of test signals—including 5 MHz sine, sawtooth, damped sine, and frequency-modulated chirp waveforms—were acquired using triangular, sinusoidal, and sawtooth sampling functions. Stroboscopic sampling was demonstrated using reference frequencies lower than the signal of interest, enabling effective undersampling of periodic radio frequency (RF) waveforms. The method achieved effective bandwidths approaching 100 MHz, with amplitude reconstruction errors of 0.05–0.30 RMS for sinusoidal signals and 0.15–0.40 RMS for sawtooth signals. Timing jitter showed strong dependence on the relative slope between the acquired waveform and sampling function: steep regions produced jitter near 5 ns, while shallow regions exhibited jitter up to 20 ns. The study has several limitations, including the bandwidth and dead-time constraints of the commercial TDC, the finite slew rate and noise of the comparator front-end, and the limited frequency range of the generated sampling functions. These factors influence the achievable timing precision and reconstruction accuracy, especially in low-gradient signal regions. Overall, the passive waveform-crossing method demonstrates strong potential for wideband, sparse, and rapidly varying signals, with natural scalability to multi-channel systems. Potential application domains include RF acquisition, ultra-wideband (UWB) radar, integrated sensing and communication (ISAC) systems, high-speed instrumentation, and wideband timed antenna arrays. Full article

Autonomous vehicles (AVs) are transforming transportation by improving safety, efficiency, and intelligence through integrated sensing, computing, and communication technologies. However, their growing reliance on Vehicle-to-Everything (V2X) communication exposes them to cybersecurity vulnerabilities, particularly at the physical layer. Among these, jamming attacks represent a critical threat by disrupting wireless channels and compromising message delivery, severely impacting vehicle coordination and safety. This work investigates the robustness of New Radio (NR)-V2X-enabled vehicular systems under jamming conditions through a dual-methodology approach. First, two Cooperative Intelligent Transport System (C-ITS) scenarios standardized by 3GPP—Do Not Pass Warning (DNPW) and Intersection Movement Assist (IMA)—are implemented in the OMNeT++ simulation environment using Simu5G, Veins, and SUMO. The simulations incorporate four types of jamming strategies and evaluate their impact on key metrics such as packet loss, signal quality, inter-vehicle spacing, and collision risk. Second, a complementary laboratory experiment is conducted using AnaPico vector signal generators (a Keysight Technologies brand) and an Anritsu multi-channel spectrum receiver, replicating controlled wireless conditions to validate the degradation effects observed in the simulation. The findings reveal that jamming severely undermines communication reliability in NR-V2X systems, both in simulation and in practice. These findings highlight the urgent need for resilient NR-V2X protocols and countermeasures to ensure the integrity of cooperative autonomous systems in adversarial environments. Full article

This paper presents an advanced signal processing framework for multi-channel forward-scatter radar (MC-FSR) systems based on the Multiple Signal Classification (MUSIC) algorithm. The proposed framework addresses the inherent limitations of FFT-based space-domain processing, such as limited angular resolution and the poor detectability of weak or closely spaced targets, which become particularly severe in low-cost FSR systems, which are typically operated with small antenna arrays. The MUSIC algorithm is adapted to operate on real-valued data obtained from the non-coherent, amplitude-based MC-FSR approach by reformulating the steering vectors and adjusting the degrees of freedom (DoFs). While compatible with arbitrary transmitting waveforms, particular emphasis is placed on Orthogonal Frequency Division Multiplexing (OFDM) signals, which are widely used in modern communication systems such as Wi-Fi and cellular networks. An analysis of the OFDM waveform’s autocorrelation properties is provided to assess their impact on target detection, including strategies to mitigate rapid target signature decay using a sub-band approach and to manage signal correlation through spatial smoothing. Simulation results, including multi-target scenarios under constrained array configurations, demonstrate that the proposed MUSIC-based approach significantly enhances angular resolution and enables reliable discrimination of closely spaced targets even with a limited number of receiving channels. Experimental validation using an S-band MC-FSR prototype implemented with software-defined radios (SDRs) and commercial Wi-Fi antennas, involving cooperative targets like people and drones, further confirms the effectiveness and practicality of the proposed method for real-world applications. Overall, the proposed MUSIC-based MC-FSR framework exhibits strong potential for implementation in low-cost, hardware-constrained environments and is particularly suited for emerging Integrated Sensing and Communication (ISAC) systems. Full article

Precise positioning is critical for autonomous uncrewed aerial vehicle (UAV) navigation, especially in GNSS-denied environments where radio-based signals are unreliable. This study presents an optical camera communication (OCC)-based positioning method that enables real-time 3D coordinate estimation using aviation obstruction light-emitting diodes (LEDs) as optical transmitters and a UAV-mounted camera as the receiver. In the proposed system, absolute positional identifiers are encoded into color-shift-keying-modulated optical signals emitted by fixed LEDs and captured by the UAV camera. The UAV’s 3D position is estimated by integrating the decoded LED information with geometric constraints through the Perspective-n-Point algorithm, eliminating the need for satellite or RF-based localization infrastructure. A virtual offshore wind farm, developed in Unreal Engine, was used to experimentally evaluate the feasibility and accuracy of the method. Results demonstrate submeter localization precision over a 50,000 cm flight path, confirming the system’s capability for reliable, real-time positioning. These findings indicate that OCC-based positioning provides a cost-effective and robust alternative for UAV navigation in complex or communication-restricted environments. The offshore wind farm inspection scenario further highlights the method’s potential for industrial operation and maintenance tasks and underscores the promise of integrating optical wireless communication into autonomous UAV systems. Full article

Many countries around the world repeatedly suffer from natural disasters such as earthquakes, tsunamis, floods, and hurricanes due to geographical factors, including plate boundaries, tropical cyclone zones, and coastal regions. Representative examples include Hurricane Katrina, which struck the United States in 2005, and the Great East Japan Earthquake in 2011. Both were large-scale disasters that occurred in developed countries and caused enormous human and economic losses regardless of disaster type or location. As the occurrence of such catastrophic events remains inevitable, establishing effective preparedness and rapid response systems for large-scale disasters has become an urgent global challenge. One of the critical issues in disaster response is the rapid estimation of the number of affected individuals required for effective rescue operations. During large-scale disasters, terrestrial communication infrastructure is often rendered unusable, which severely hampers the collection of situational information. If the population within a disaster-affected area can be estimated without relying on ground-based communication networks, rescue resources can be more appropriately allocated based on the estimated number of people in need, thereby accelerating rescue operations and potentially reducing casualties. In this study, we propose a population-estimation system that remotely senses radio signals emitted from smartphones in disaster areas using Low Earth Orbit (LEO) satellites. Through numerical analysis conducted in MATLAB R2023b, the feasibility of the proposed system is examined. The numerical results demonstrate that, under ideal conditions, the proposed system can estimate the number of smartphones within the observation area with an average error of 2.254 devices. Furthermore, an additional evaluation incorporating a 3D urban model demonstrates that the proposed system can estimate the number of smartphones with an average error of 19.03 devices. To the best of our knowledge, this is the first attempt to estimate post-disaster population using wireless LAN signals sensed by LEO satellites, offering a novel remote-sensing-based approach for rapid disaster response. Full article

We present an experimental demonstration of a daylight-capable Optical Amplify-and-Forward (OAF) relaying system designed to support flexible and high-capacity network topologies. The proposed architecture integrates fiber-based infrastructure with OAF Free Space Optics (FSO) relaying, enabling bidirectional optical communication over 460 m (x2) using SFP-compatible schemes, while addressing Non-Line-of-Sight (NLOS) constraints and fiber disruptions. This work achieves a Bit Error Rate (BER) below the Hard-Decision Forward Error Correction (HD-FEC) limit, validating the feasibility of high-speed urban FSO links. By leveraging low-cost fiber-coupled optical terminals, the system transmits single-carrier 120 Gbps Intensity Modulation/Direct Detection (IM/DD) signals using NRZ (Non-Return-to-Zero) and PAM4 (4-Pulse Amplitude Modulation) modulation formats. Operating entirely in the optical C-Band domain, this approach ensures compatibility with existing infrastructure, supporting scalable mesh FSO deployments and seamless integration with hybrid Radio Frequency (RF)/FSO systems. Full article

With the launch of an increasing number of satellites to establish complex satellite communication networks, automatic modulation recognition (AMR) plays a crucial role in satellite signal recognition and spectrum management. However, most existing AMR models struggle to handle signals in such complex satellite communication environments. Therefore, this paper proposes an adaptive multi-rate atrous convolution network with residual attentional feature fusion (RAFF-AMACNet) that employs the adaptive multi-rate atrous convolution (AMAC) module to adaptively extract and dynamically join more prominent multi-scale features, enhancing the model’s time-series context awareness and generating robust feature maps. On this basis, the pyramid backbone consists of multiple stacked residual attentional feature fusion (RAFF) modules, featuring a dual-attention collaborative mechanism designed to mitigate feature map shifts and increase the separation between feature clusters of different classes under significant Doppler effects and nonlinear influences. On our independently constructed RML24 dataset, a general-purpose dataset tailored for satellite cognitive radio systems, simulation results indicate that at a signal-to-noise ratio of 0 dB, the modulation recognition accuracy reaches 92.99%. Full article

Background: With the development of wireless communication technologies, the rapid advancement of 5G and 6G communication systems has spawned an urgent demand for low latency and high data rates. Orthogonal Frequency Division Multiplexing (OFDM) communication using high-order digital modulation has become a key technology due to its characteristics, such as high reliability, high data rate, and low latency, and has been widely applied in various fields. As a component of cognitive radios, automatic modulation classification (AMC) plays an important role in remote sensing and electromagnetic spectrum sensing. However, under current complex channel conditions, there are issues such as low signal-to-noise ratio (SNR), Doppler frequency shift, and multipath propagation. Methods: Coupled with the inherent problem of indistinct characteristics in high-order modulation, these currently make it difficult for AMC to focus on OFDM and high-order digital modulation. Existing methods are mainly based on a single model-driven approach or data-driven approach. The Adaptive Wavelet Mamba Network (AWMN) proposed in this paper attempts to combine model-driven adaptive wavelet transform feature extraction with the Mamba deep learning architecture. A module based on the lifting wavelet scheme effectively captures discriminative time–frequency features using learnable operations. Meanwhile, a Mamba network constructed based on the State Space Model (SSM) can capture long-term temporal dependencies. This network realizes a combination of model-driven and data-driven methods. Results: Tests conducted on public datasets and a custom-built real-time received OFDM dataset show that the proposed AWMN achieves a performance reaching higher accuracies of 62.39%, 64.50%, and 74.95% on the public Rml2016(a) and Rml2016(b) datasets and our formulated EVAS dataset, while maintaining a compact parameter size of 0.44 M. Conclusions: These results highlight its potential for improving the automatic modulation classification of high-order OFDM modulation in 5G/6G systems. Full article