Search Results (382)

Non-orthogonal multiple access (NOMA) has been identified as one of the key technologies for 6G capacity and latency gains. However, existing implementation challenges of the NOMA technique, related to carrier, timing, and phase offsets, successive interference cancellation (SIC) error propagation, packet loss dynamics, and host to software defined radios processing jitter, create obstacles in the practical implementation of NOMA. This paper bridges the gap between theory and hardware by introducing a complete two-user NOMA transmit–receive chain on a low-cost ADALM-Pluto software defined radio (SDR) platform. The proposed implementation integrates matched filtering, offset estimation and correction, SIC with waveform reconstruction and subtraction, and reliability reinforcement via rate-1/2 convolutional coding with Viterbi decoding. We have performed a complete validation of the proposed design in both downlink and uplink modes. We collected data regarding the packet-level and system-related metrics, such as end-to-end latency, bit error rate (BER), and success rate. Moreover, we demonstrate the implementation of the uplink NOMA without need for expensive GPS-disciplined oscillators by leveraging the Pluto Rev-C dual-transmit channels that share a common oscillator. We present detailed experimental results at 915 MHz with BPSK modulation for the downlink performance, and also show a full implementation of the uplink NOMA. We observe excellent reliability for the downlink setup and good reliability for the uplink system. Full article

Spectrum Sensing (SS) is expected to play a crucial role in forthcoming 6G Cognitive Radio Networks (CRNs), where unlicensed users will be able to dynamically access the spectrum and perform opportunistic transmissions without generating interference for licensed users. In this work, we investigate multiple-antenna SS techniques by analyzing the performance of several widely used detection schemes—namely, Roy’s Largest Root Test (RLRT), the Generalized Likelihood Ratio Test (GLRT), the Eigenvalue Ratio Detector (ERD), and the Energy Detector (ED)—under varying false-alarm probabilities and signal-to-noise ratios (SNRs). We assume there are a fixed number of sensors at the secondary-user receiver, namely, four. To evaluate the behavior of these detectors in realistic conditions, we developed a software-defined radio (SDR) testbed using Universal Software Radio Peripherals (USRPs), enabling both primary-user signal transmission and secondary-user data acquisition. The experimental results, illustrated through Receiver Operating Characteristic (ROC) and performance curves, are compared with simulation outcomes. The analysis is complemented by a detailed state-of-the-art listing of the available analytical characterizations of the false-alarm probabilities for the considered SS schemes. In particular, the GLRT false-alarm probability, previously unavailable in explicit form for a four-antenna equipped receiver, is computed as well. These results validate the superior detection capability of RLRT over the other schemes tested, confirming its effectiveness not only in theoretical analysis but also in practical SDR-based implementations. Full article

The proliferation of Low Earth Orbit (LEO) satellite constellations, driven by the NewSpace economy and reduced launch costs, has opened new opportunities for positioning, navigation, and timing (PNT) applications. Compared to traditional GNSS systems operating in Medium Earth Orbit, LEO satellites offer several advantages: higher received signal power, better satellite geometry and visibility in urban environments, and greater Doppler dynamics—enabling approaches such as single-satellite and Doppler-based positioning. Although dedicated LEO-PNT constellations are still under development, existing commercial LEO satellites can already be leveraged for experimental positioning applications. This paper presents a portable, multi-constellation testbed built using commercial off-the-shelf (COTS) hardware and software-defined radio (SDR) technologies. The platform enables the synchronous acquisition and processing of LEO signals from Orbcomm, Iridium, and Starlink, allowing for the extraction of key positioning observables. A comprehensive measurement campaign is conducted across both indoor and outdoor environments to evaluate signal visibility and Doppler tracking performance. Results highlight the potential of opportunistic LEO-based positioning, particularly in challenging scenarios such as indoor environments where traditional GNSS solutions are unreliable. Full article

In all-digital transmitters (ADTx), the in-band quantization noise generated by pulse coding provides only limited clean bandwidth (CBW), significantly increasing the difficulty of analog filter design. To address the constrained CBW of RF pulse sequences in ADTx, this paper proposes an optimization strategy for suppressing noise across a broader frequency domain. Distinguished from traditional schemes with limited noise suppression range, the expansion of CBW is innovatively achieved by setting multiple groups of frequency observation points near the carrier frequency, enabling more comprehensive constraints of in-band noise. Meanwhile, aiming at the problems of large look-up table scale and slow query speed, a partitioned look-up strategy is proposed. During a look-up, traversal is confined only to the partition containing the input point, eliminating the need to scan all elements. This strategy substantially reduces the number of error calculations and comparisons, significantly improving the real-time performance of mapping look-up and lowering the computational demands on digital processing devices. Through the collaborative optimization of noise suppression and query efficiency, this study highlights its breakthrough contributions and provides technical support for the optimization of RF pulse sequences in ADTx. Full article

This paper introduces the GRIPP (GNSS/SBAS Receiver, Independent and Portable PVT) system, an open-source SDR oriented GNSS/SBAS receiver. Composed of a Pocket SDR FE device, an L-band antenna and a computer, this system aims to ease the deployment and test of future GNSS and SBAS evolutions, providing a fully documented and customizable receiver. Acting like a generic navigation toolbox, the main idea is to be able to quickly adapt it for research and development purposes, introducing new filtering methods or PVT algorithms. Besides these engineering applications, the goal is also to use it for educational purposes to introduce GNSS and SBAS to the general audience. Full article

This paper explores the feasibility of using acoustic wave propagation, particularly in the ultrasonic range, as a solution for data transmission in environments where traditional radio frequency (RF) communication is ineffective due to signal attenuation—such as in liquids or dense media like metal or stone. Leveraging GNU Radio and commercially available audio hardware, a low-cost, SDR (Software Defined Radio) system was developed to transmit data blocks (e.g., images, text, and audio) through various substances. The system employs BFSK (Binary Frequency Shift Keying) and BPSK (Binary Phase Shift Keying), operates at ultrasonic frequencies (typically 40 kHz), and has performance validated under real-world conditions, including water, viscous substances, and flammable liquids such as hydrocarbon fuels. Experimental results demonstrate reliable, continuous communication at Nyquist–Shannon sampling rates, with effective demodulation and file reconstruction. The methodology builds on concepts originally developed for Ad Hoc Sensor Networks in shipping containers, extending their applicability to submerged and RF-hostile environments. The modularity and flexibility of the GNU Radio platform allow for rapid adaptation across different media and deployment contexts. This work provides a reproducible and scalable communication solution for scenarios where RF transmission is impractical, offering potential applications in underwater sensing, industrial monitoring, railways, and enclosed infrastructure diagnostics. Across controlled laboratory experiments, the system achieved 100% successful reconstruction of transmitted image files up to 100 kB and sustained packet delivery success exceeding 98% under stable coupling conditions. Full article

Insulation defects are the main cause of transformer faults, and the partial discharge phenomenon generated by defects under high-voltage excitation can reflect the internal characteristics of the defects. Therefore, studying the characteristics of partial discharge signals can provide an important basis for the analysis of transformer partial discharge problems. This article proposes a transformer partial discharge ultra-high-frequency signal classification and recognition method based on the Adaptive Hybrid Attention Fusion Network. The feature extraction of partial discharge waveform is carried out through a dual flow network structure, where the ResNet branch focuses on extracting local features and the Swin Transformer branch focuses on extracting global features. Then, a new Adaptive Hybrid Attention Fusion Network fusion model is used to weight the extracted features according to adaptive allocation weights, ultimately achieving the classification and recognition of transformer partial discharge ultra-high-frequency signals. The experiment shows that this method achieves a fault detection accuracy of 99.58%, with a loss rate of only 0.73%. Compared to various existing network models, the accuracy of the proposed model reached 99.58%, the recall was 99.58%, and the F1 score was 99.58%, which is significantly better than other model methods, indicating that the model has significant advantages in detection performance. Full article

This work presents the design and experimental validation of a 2 × 2 MIMO communication system assisted by a directly modulated analog radio-over-fiber (A-RoF) fronthaul, targeting low-complexity connectivity solutions for underserved/remote regions. The study details the complete end-to-end architecture, including a wireless access segment to complement the 20-km optical fronthaul link. The system is implemented on an software defined radio (SDR) platform using GNU Radio 3.7.11, running on Ubuntu 18.04 with kernel 4.15.0-213-generic. It also employs adaptive modulation driven by real-time signal-to-noise ratio (SNR) estimation to keep bit error rate (BER) close to zero while maximizing throughput. Performance is characterized over 20 km of single-mode fiber (SMF) using coarse wavelength division multiplexing (WDM) and assessed through root mean square error vector magnitude (${\mathrm{EVM}}_{\mathrm{RMS}}$), throughput, and spectral integrity. The results identify an optimum radio-frequency drive region around $-16$ dBm enabling high-order modulation (e.g., 256-QAM), whereas RF input powers above approximately $-10$ dBm increase ${\mathrm{EVM}}_{\mathrm{RMS}}$ due to nonlinearity in the RF front-end/low-noise amplifier (LNA) and direct modulation stage, forcing the adaptive scheme to reduce modulation order and throughput. Over the optical-power sweep, when the incident optical power exceeds approximately $-8$ dBm, the system reaches ∼130 Mbps (24-MHz channel) with ${\mathrm{EVM}}_{\mathrm{RMS}}$ approaching ∼1%, highlighting the need for careful joint tuning of RF drive, optical launch power, and wavelength allocation across transceivers. Finally, the integrated access link employs diplexers for transmitter/receiver separation in a 2 × 2 configuration with 2.8 m antenna separation and low channel correlation, demonstrating a 10 m proof-of-concept range and enabling end-to-end spectrum/EVM/throughput observations across the full communication chain. Full article

With the rapid evolution of wireless networks, the need to explore novel technologies to meet the demands of future systems, particularly 6G, has become a significant challenge. One promising solution is integrating radio frequency (RF) and optical wireless communication (OWC) technologies to leverage their unique strengths. This paper introduces a novel model for integrating RF and OWC technologies within the framework of emerging 6G. The main objective of this approach is the dynamic technology selection (TS) and modulation scheme selection (MSS), which play a pivotal role in optimizing network efficiency and adapting to diverse 6G requirements. The proposed cross-layer architecture integrates the application layer, network layer based on a software-defined network (SDN), and physical layer consisting of a hybrid cell and software-defined radio with optical functionality (SDR-O). This approach facilitates real-time decision-making based on environmental factors and application requirements. Full article

The deployment of 5G (Fifth-Generation) networks in industrial Internet of Things (IoT), intelligent transportation, and emergency communications introduces heterogeneous and dynamic network states, leading to frequent and diverse faults. Traditional fault detection methods typically emphasize either local temporal anomalies or global distributional characteristics, but rarely achieve an effective balance between the two. In this paper, we propose a parallel multi-branch convolutional neural network (CNN)–Transformer framework (MBCT) to improve fault diagnosis accuracy in 5G networks. Specifically, MBCT takes time-series network key performance indicator (KPI) data as input for training and performs feature extraction through three parallel branches: a CNN branch for local patterns and short-term fluctuations, a Transformer encoder branch for cross-layer and long-term dependencies, and a statistical branch for global features describing quality-of-experience (QoE) metrics. A gating mechanism and feature-weighted fusion are applied outside the branches to adjust inter-branch weights and intra-branch feature sensitivity. The fused representation is then nonlinearly mapped and fed into a classifier to generate the fault category. This paper evaluates the performance of the proposed model on both the publicly available TelecomTS multi-modal 5G network observability dataset and a self-collected SDR5GFD dataset based on software-defined radio (SDR). Experimental results demonstrate that the proposed model achieves superior performance in fault classification, achieving 87.7% accuracy on the TelecomTS dataset and 86.3% on the SDR5GFD dataset, outperforming the baseline models CNN, Transformer, and Random Forest. Moreover, the model contains approximately 0.57M parameters and requires about 0.3 MFLOPs per sample for inference, making it suitable for large-scale online fault diagnosis. 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

The deployment of 5G networks has transformed the landscape of radiofrequency electromagnetic field (RF-EMF) exposure patterns, shifting from high-power macro base stations to dense networks of small, beamforming cells. This review critically assesses the validity, challenges, and research gaps of low-cost RF-EMF sensors used for 5G exposure monitoring. An analysis of over 60 studies covering Sub-6 GHz and emerging mmWave systems shows that well-calibrated sensors can achieve measurement deviations of ±3–6 dB compared to professional instruments like the Narda SRM-3006, with long-term calibration drift less than 0.5 dB per month and RMS reproducibility around 5%. Typical outdoor 5G FR1 exposure levels range from 0.01 to 0.5 W/m² near small cells, while personal device use can cause transient exposures 10–30 dB higher. Although mmWave (24–100 GHz) and Wi-Fi 7/8 (~60 GHz) are underrepresented due to antenna and component limitations, Sub-6 GHz sensing platforms, including software-defined radio (SDR)-based and triaxial isotropic designs, provide sufficient sensitivity for both citizen and institutional monitoring. Major challenges involve calibration drift, frequency band gaps, data interoperability, and ethical management of participatory networks. Addressing these issues through standardized calibration protocols, machine learning-assisted drift correction, and open data frameworks will allow affordable sensors to complement professional monitoring, improve spatial coverage, and enhance public transparency in 5G RF-EMF exposure governance. Full article

Maritime transportation accounts for approximately 80 percent of global trade volume, with modern vessels increasingly reliant on Software-Defined Radio (SDR) technologies for communication and navigation. However, the very flexibility and reconfigurability that make SDRs advantageous also introduce complex radio frequency vulnerabilities exposing ships to threats that jeopardize vessel security, and this disrupts global supply chains. This survey paper systematically examines the security landscape of maritime SDR systems through a threat modeling lens. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we analyzed 84 peer-reviewed publications (from 2002 to 2025) and applied the STRIDE framework to identify and categorize maritime SDR threats. We identified 44 distinct threat types, with tampering attacks being most prevalent (36 instances), followed by Denial of Service (33 instances), Repudiation (30 instances), Spoofing (23 instances), Information Disclosure (24 instances), and Elevation of Privilege (28 instances). These threats exploit vulnerabilities across device, software, network, message, and user layers, targeting critical systems including Global Navigation Satellite Systems, Automatic Identification Systems, Very High Frequency or Digital Selective Calling systems, Electronic Chart Display and Information Systems, and National Marine Electronics Association 2000 networks. Our analysis reveals that maritime SDR threats are multidimensional and interdependent, with compromises at any layer potentially cascading through entire maritime operations. Significant gaps remain in authentication mechanisms for core protocols, supply chain assurance, regulatory frameworks, multi-layer security implementations, awareness training, and standardized forensic procedures. Further analysis highlights that securing maritime SDRs requires a proactive security engineering that integrates secured hardware architectural designs, cryptographic authentications, adaptive spectrum management, strengthened international regulations, awareness education, and standardized forensic procedures to ensure resilience and trustworthiness. Full article

The emerging development of Global Navigation Satellite System (GNSS) software receivers has opened new opportunities in diverse operations. However, non-line-of-sight (NLOS) concatenated signal reception is one prevalent deterioration factor causing positioning errors in urban scenarios. To enhance integrity and reliability through receiver autonomous integrity monitoring (RAIM) techniques in urban environments, distinguishing between line-of-sight (LOS) and NLOS signals facilitates the exclusion of NLOS channels: this is challenging due to uncertain signal reflections/refractions from diverse obstruction conditions in the built environment. Moreover, NLOS features show similarity to multipath effects like scattering and diffraction which causes difficulty in identifying the NLOS type. Recent work exploited NLOS detections with multi-correlator outputs using neural networks that outperform using signal strength techniques for NLOS detection. This paper proposes a neural network approach designed to recognise and learn spatial features among early, late, and prompt correlator outputs, differentiating between correlations, and also by memorising temporal features to acquire propagation information. Specifically, the spatial features of correlator IQ streams are derived from convolutional layers incorporated with concatenations, to formulate associate models like early-minus-late discrimination. A Recurrent Neural Network (RNN), i.e., long short-term memory (LSTM), is integrated to obtain comprehensive temporal features; hereby, a softmax classifier is appended in the last layer to distinguish between NLOS and LOS signals. By simulating synthetic datasets generated by a Spirent simulator and captured by a software-defined radio (SDR), the correlator outputs are acquired during the scalar tracking stage. The product of the proposed network demonstrates high performance in terms of accuracy, time consumption and sensitivity, affirming the efficiency of utilising early-stage correlations for NLOS detection. Moreover, an impact analysis of varying the sliding window length on NLOS discrimination underscores the need to fine-tune the parameter, as well as balancing accuracy, operation complexity and sensitivity. 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