Search Results (648)

Background: To better understand the complexity of sexual dysfunction, we aim to construct multi-factorial explanatory models of sexual dysfunction using data from participants enrolled in the Danish cross-sectional Study of latE effects for those liviNg through cErvical CAncer (SENECA). Methods: In a nationwide survey, patient-reported outcomes (PRO) were collected from 2002 cervical cancer survivors treated with surgery and/or radio(chemo)therapy between 2005–2020 and a reference cohort consisting of 7853 women without a history of cancer. PROs in the EORTC C30/CX24 questionnaires were analysed. Bayesian Network models were developed for sexual activity and dyspareunia in reference and cancer survivor cohorts, resulting in four explanatory models. The reference cohort served as a structural prior for the cancer models. Sexual activity models included participants with complete data (6706 reference, 1694 cancer), while dyspareunia models included those reporting sexual activity in the past four weeks (4521 reference, 996 cancer). Results: Respondents were stratified by treatment received: (1) reference group (n = 7853), (2) surgery alone (n = 1285), (3) radio(chemo)therapy (RCT) with/without prior surgery (n = 951). Sexual inactivity was reported by 28%, 29% and 49% of reference, surgery and RCT groups respectively, and dyspareunia by 23%, 39% and 54%. Sexual activity was associated with age, treatment modality and social functioning. Social functioning was associated with cognitive and role functioning, global QoL, and financial difficulties, all of which were associated strongly with fatigue. Dyspareunia was associated with vaginal functioning and soreness. The strong associations observed in the cancer survivor cohort between fatigue and functional outcomes/QoL/sexual activity were absent in the reference cohort. Conclusions: Sexual inactivity and dyspareunia were more common in cervical cancer patients, particularly in those treated with RCT compared with the reference cohort. Differences in explanatory factors between cancer and reference cohorts highlight the need for cancer-specific approaches to sexual rehabilitation. Full article

Electromagnetic wave manipulation underpins radar detection, imaging, and electronic countermeasures. Conventional phased-array and radio-frequency-chain-based radar architectures provide mature and high-performance operation but can face practical constraints related to aperture profile, power and thermal management, calibration, bandwidth, and multifunctional integration. Electromagnetic metasurfaces provide a complementary approach by controlling the phase, amplitude, polarization, and frequency content of scattered or radiated fields through subwavelength surface elements. This article presents a radar-system-oriented review of metasurface-enabled wave manipulation. We first summarize the relevant physical mechanisms, including generalized scattering, digital and information metasurfaces, time-varying modulation, and polarization and geometric-phase control. We then review experimentally reported applications in radar-cross-section control, programmable beam steering, computational imaging, time-modulated radar, multiple-input multiple-output systems, and integrated sensing and communication. Particular attention is given to the level of experimental validation and to the distinction between measured device- or subsystem-level performance and anticipated system-level benefits. Potential applications in stealth, radar deception, low-probability-of-intercept-oriented operation, and cognitive sensing are discussed together with limitations in bandwidth, efficiency, power handling, biasing, calibration, thermal management, and scalability. Overall, the available literature indicates that metasurfaces can support selected aperture-level radar functions, whereas general system-level advantages in SWaP, cost, latency, and energy efficiency remain to be established through controlled comparative experiments. Full article

Advanced metering infrastructure (AMI) is the sensing backbone of the smart grid, and its reliability underpins urban energy services such as state estimation, demand response, and distributed-energy integration. When AMI uses cellular spectrum leased through a cognitive mobile virtual network operator (C-MVNO), allocating channels to data aggregation points (DAPs) each frame is difficult because three uncertainties interact: imperfect spectrum sensing, time-varying and cross-channel-correlated primary-user activity, and stochastic urban propagation. Classical Hungarian assignment is optimal per frame but blind to primary-user dynamics, while cognitive-radio heuristics ignore queue state and cross-channel structure. We propose a two-timescale hierarchy that couples these established tools in a new way: a Proximal Policy Optimization (PPO) agent decides, once per epoch, which opportunistic channels to expose, and an exact Hungarian solver performs the per-frame DAP-to-channel assignment. To our knowledge this is the first coupling of a learned cognitive layer with exact Hungarian assignment for cognitive-radio resource allocation. On a 3GPP TR 38.901-compliant simulator, PPO significantly outperforms a Bayesian-belief baseline and the Hungarian-only configuration in delivery ratio, latency, and a strict per-meter satisfaction metric, and is robust across independent seeds and sensitivity sweeps. An architectural ablation shows the DAP tier is a precondition for viability, not merely an optimization. Full article

Automatic Modulation Classification (AMC) aims to automatically identify modulation types based on the features of received signals, and acts as a vital technique for spectrum sensing, cognitive radio and electronic countermeasures. However, existing methods generally overlook the issue that modulation signals with similar characteristics are prone to confusion. In particular, under strong interference conditions, feature distributions become blurred and class boundaries tend to overlap, further exacerbating misclassification among modulation types with similar characteristics, thereby limiting improvements in classification accuracy and model robustness. To address this challenge, a modulation classification method based on fuzzy sample feature enhancement is proposed. Specifically, a modulation class entropy constraint and a fuzzy sample feature enhancement constraint are introduced to establish a Multi-scale Fuzzy Sample Feature Enhancement Framework (MTFSFEF). Through fuzzy sample selection and feature representation refinement, the proposed framework effectively mitigates feature overlap among fuzzy samples and enhances inter-class separability and discriminability. For $\mathrm{SNR}\ge 0\mathrm{dB}$, MTFSFEF delivers superior average classification accuracies across all three benchmark datasets: $92.82\%$ on RML2016.10a, over $93.43\%$ on RML2016.10b, and exceeding $92.78\%$ on RML2018.01a, outperforming existing methods by up to $2.68\%$, $2.89\%$, and $2.73\%$, respectively. Full article

Long-endurance unmanned aerial vehicle (UAV) missions beyond 72 consecutive flight hours face a reliability boundary at which thermal gyroscope drift drives the inertial navigation system (INS) position error into rapid nonlinear divergence at a predictable threshold t_c. This paper presents BAZ II, a simulation-validated multi-agent navigation system that extends the analytical BAZ (bifurcation-aware zonal navigation) framework. Its central idea is to treat communication quality as a planning resource and combine it with multi-agent collaboration, making the navigation cliff a manageable degradation event rather than a hard operating limit. Four contributions support this idea: a thermalhysteresis MEMS gyroscope drift model reproduces the analytical cliff in simulation and supplies its physical mechanism; a distributed collaborative simultaneous localization and mapping (SLAM) filter coupled to a stochastic continuous-time Markov chain (CTMC) interagent channel sustains GPS-denied localization within the operational accuracy budget; a 3D Gaussian process RF-aware model predictive controller (MPC) with cognitive radio frequency-hopping restores link availability under jamming, while an analytic hierarchy process (AHP)-weighted multi-objective communication cost improves latency and jitter at negligible signal-to-noise ratio cost; finally, the integrated controller executes within the onboard real-time budget of an NVIDIA Jetson Xavier NX. All results are obtained in simulation, with hardware-in-the-loop and field testing remaining as priority future work. Full article

Flying Ad-Hoc Networks (FANETs), Internet of Things (IoT), and Internet of Vehicles (IoV) are critical enablers of intelligent transportation and smart city ecosystems. Their reliance on shared wireless channels, however, exposes them to diverse jamming attacks that threaten communication reliability, mission effectiveness, and safety. This paper presents a comprehensive study of jamming threats in integrated FANET–IoT–IoV environments and analyzes conventional and advanced anti-jamming techniques across physical, link/MAC, spectral, spatial, temporal, and hybrid domains. To address the challenges posed by heterogeneous and dynamic network conditions, we propose a cross-layer anti-jamming framework that integrates Cognitive Radio (CR) for dynamic spectrum access and Multi-Agent Reinforcement Learning (MARL) for cooperative, adaptive decision-making. The framework employs a Perception Engine for local anomaly detection, a Cognitive Engine for constructing a collaborative jamming map, and a Decision and Action Engine for multi-agent DRL-based mitigation. Simulation results demonstrate that the proposed CR-MARL framework significantly improves packet delivery ratio, reduces latency, and adapts efficiently to varying jamming strategies, while maintaining low energy and computational overhead, making it suitable for resource-constrained UAVs, vehicles, and IoT sensors. Full article

The rapid development of the low-altitude economy demands comprehensive electromagnetic spectrum awareness. However, constructing a comprehensive radio environment map (REM) in this scenario is challenging, as spectrum sensing data collected by unmanned aerial vehicles (UAVs) in complex low-altitude environments is typically sparse, fragmented, and non-uniformly distributed across the high-dimensional space of time, frequency, and 3D space. To address these issues, this study proposes a rank-adaptive Bayesian tensor ring completion (Ra-BTRC) framework. The method models the low-altitude electromagnetic environment as a unified five-dimensional (5D) spectrum tensor. It then employs tensor ring (TR) decomposition to capture latent high-order correlations across all dimensions. To overcome the sensitivity of conventional TR methods to predefined ranks, Ra-BTRC introduces sparsity-inducing priors on the TR core factors, enabling variational Bayesian inference to learn observation uncertainty and infer effective TR ranks from sparse measurements without manually fixing the TR rank. Simulations demonstrate that Ra-BTRC significantly outperforms existing TR-based baselines, achieving more than 10 dB MMSE improvement at a 5% sampling rate while accurately recovering local spectrum structures and temporal dynamics. The proposed approach provides a robust and scalable solution for reliable global low-altitude spectrum cognition under stringent sensing budgets. Full article

Automatic modulation classification (AMC) is a critical enabler of cognitive radio (CR) systems, allowing secondary users to identify primary user modulation schemes and adapt transmission parameters in real time. Traditional AMC approaches, based on likelihood functions or hand-crafted features, suffer from degraded performance under low signal-to-noise ratio (SNR) conditions and realistic channel impairments. In this paper, we propose MS-SENet (Multi-Scale Squeeze–Excitation Network), a novel deep-learning architecture that integrates multi-scale convolutional feature extraction, squeeze-and-excitation channel attention, residual learning, bidirectional long short-term memory (BiLSTM) temporal modelling, and global attention pooling into a unified framework for robust AMC. The multi-scale convolution module employs parallel branches with kernel sizes of 3, 5, and 7 to capture both fine-grained phase transitions and coarse envelope patterns from raw in-phase/quadrature (I/Q) signal samples. Squeeze–excitation residual blocks perform channel-wise feature recalibration, enabling the network to emphasize informative feature maps while suppressing less relevant ones. A bidirectional LSTM layer models temporal dependencies across the signal sequence, and a global attention pooling mechanism performs weighted temporal aggregation prior to classification. We present a comprehensive taxonomy of deep-learning architectures for AMC organised along five axes—input representation, feature extraction, temporal modelling, regularization strategy, and architectural complexity—and conduct a rigorous comparative evaluation against ten baseline architectures on a RadioML-style synthetic dataset (110,000 samples, 11 modulation classes, and 20 SNR levels from −20 to +18 dB). The experimental results demonstrate that MS-SENet achieves a mean classification accuracy of 87.9% at SNR ≥ 0 dB (the average of the medium and high SNR regime averages: 86.06% for 0 ≤ SNR < 10 dB and 89.68% for SNR ≥ 10 dB) while maintaining a compact footprint of approximately 406 K parameters, making it suitable for deployment on resource-constrained edge devices. We further analyze the robustness of the proposed architecture to multipath fading, carrier frequency offset, and sample rate offset, confirming its resilience under practical operating conditions. MS-SENet is an architecture designed for automatic modulation classification of I/Q signals and is not related to the homonymous architecture for speech emotion recognition. Full article

Incremental automatic modulation recognition is essential for the awareness of complex electromagnetic environments but is prone to catastrophic forgetting. This is fundamentally precipitated by shortcut learning, a phenomenon where deep models prioritize stable but non-essential channel artifacts (e.g., noise, fading) over intrinsic modulation characteristics. Consequently, models rely on spurious correlations that collapse during incremental task updates or environmental shifts, leading to representation drift. To bridge this gap, we propose the ParalIMR framework, which integrates a parallel reconstruction architecture with the segment substitution (SS) strategy to decouple modulation signatures from environmental fingerprints. Specifically, the parallel branch utilizes a Denoising AutoEncoder (DAE) as a task-agnostic structural anchor, purifying feature representations and maintaining geometric consistency across varying signal-to-noise ratios without propagating noise-overfitting to the classifier. In the meantime, the SS strategy actively disrupts the temporal coupling between class labels and hardware fingerprints through random reorganization, forcing the model to extract modulation-invariant structural cues. Experimental results on the RML2016a datasets demonstrate that in a three-stage incremental setup, our method achieves an overall accuracy of 84.32% at 0 dB SNR, representing a 2.69% improvement over the iCaRL baseline. Notably, this advantage expanded to 4.46% on RML2018, demonstrating that ParalIMR effectively arrests catastrophic forgetting. Ultimately, this research provides a robust learning paradigm tailored for cognitive radio and electronic warfare in dynamic electromagnetic landscapes. Full article

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles. Full article

Automatic Modulation Classification (AMC) is essential for waveform-level signal characterization. It supports spectrum sensing, signal identification, and adaptive resource allocation in cognitive radio and next-generation wireless systems. However, channel impairments such as multipath propagation, frequency offset, fast fading, and noise degrade modulation signatures, making reliable AMC challenging. Existing deep learning-based approaches often rely on purely data-driven learning, leading to insufficient modeling of modulation-relevant features, loss of transient characteristics, and limited exploitation of hierarchical relationships among modulation types. To address these issues, this paper proposes PHM-Net, a physics-informed hierarchical multi-scale network for robust AMC. The model employs a hierarchical backbone with residual encoder blocks. A Transient Feature Gating (TFG) module enhances modulation-relevant representations, a Cross-Resolution Signal Aggregation (CRSA) module fuses multi-stage features, and a Physics-Informed Hierarchical Loss (PI-HL) enforces consistency between coarse- and fine-grained predictions. Experimental results on three benchmark datasets (RML2016.10a, RML2016.10b, and RML2018.01a) show that PHM-Net consistently achieves the highest average accuracy among all compared models. On RML2018.01a, which contains 1024-sample sequences and 24 classes, PHM-Net achieves an average accuracy of 64.59% and a best-case accuracy of 98.42%, surpassing AMC_Net by 11.14 and 17.09 percentage points and CNN-Transformer by 9.43 and 11.15 percentage points, respectively. PHM-Net provides a robust and interpretable solution for AMC under complex channel conditions. Full article

Unauthorised FM broadcasting poses significant challenges to spectrum regulators globally, contributing to interference, degraded service quality, and national security threats. While traditional spectrum monitoring relies primarily on carrier frequency and power measurements, this study demonstrates that FM baseband features—specifically the multiplex (MPX) signal structure, pilot tone, and Radio Data System (RDS) subcarrier—provide robust discriminative markers for detecting non-compliant transmissions. Using a real-world dataset of 3710 pre-processed records collected across Nigeria’s capital region between 2021 and 2024, we extracted and analysed six transmission parameters: assigned frequency, band occupancy (±100 kHz), MPX overshoot percentage, pilot tone presence, and RDS indicators. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel was trained to distinguish compliant licensed stations from regulatory non-compliant transmissions—encompassing both unlicensed transmitters and technically non-compliant licensed operators—achieving 99.96% accuracy, 99.38% precision, and 99.63% recall with a false alarm rate of 0.026%. A Comparative analysis against baseline feature sets confirmed that integrating MPX, pilot, and RDS significantly improved detection robustness compared with carrier-only approaches. Results demonstrate that feature-rich baseband analysis enables scalable, cost-effective regulatory enforcement, reducing manual monitoring burden while enhancing detection reliability. This framework offers practical applicability for spectrum management agencies in resource-constrained environments where unauthorised broadcasting remains prevalent. Full article

This paper investigates attack- and channel-aware decision fusion for Reconfigurable Intelligent Surface (RIS)-enhanced Cooperative Spectrum Sensing (CSS) in Cognitive Radio Networks (CRNs) to mitigate the challenge from Byzantine attacks. Specifically, we first propose the optimal hard decision fusion rule for the Fusion Center (FC) based on maximum-likelihood criterion, which simultaneously accounts for channel impairments and statistical characteristics of Byzantine attacks. Following from this result, we then derive three suboptimal and low-complexity decision fusion rules when the Channel State Information (CSI) cannot be perfectly achieved at the FC. The correspondingly results indicate that negative weighting coefficients can be adaptively assigned to malicious reports based on attack intensity, which can successfully transform adversarial interference into effective detection gains for the FC in some scenarios. This finding profoundly reveals the intrinsic mechanism of how Byzantine attacks impact the decision fusion, and thus provide a rigorous theoretical perspective for developing robust decision fusion rule capable of adaptively suppressing and conversely exploiting malicious reports. Furthermore, to make practical implementation of our decision fusion rules, we develop simple and unbiased attack parameter estimation algorithms based on the first-order statistics of received reports at the FC, which also exhibits good convergence. Our results indicate that we can insert a virtual source under control, and send false data to the Byzantine attackers. This deception strategy can help the FC successfully learn the attack parameter aided by its collected data. Finally, extensive simulations are conducted and the correspondingly results demonstrate that our proposed fusion rules can effectively mitigate Byzantine attacks across a wide range of attack scenarios, and they can outperform traditional malicious report filtering defense algorithm by successfully reversing and exploiting malicious reports. Full article

Automatic modulation classification (AMC) is a key component of spectrum awareness, cognitive radio, and signal intelligence, enabling receivers to identify modulation schemes from noisy in-phase and quadrature (IQ) observations. Traditional approaches rely on likelihood-based methods or handcrafted feature extraction, which often struggle under channel impairments and real-world variability. Recent advances in deep learning enable models to learn directly from multiple signal representations, including raw IQ samples, engineered features, and time–frequency or constellation-based encodings, improving adaptability across diverse signal conditions. This paper presents a structured review of deep learning approaches for AMC, including CNNs, RNN/LSTM models, and transformer-based architectures, with a focus on performance, robustness, and system-level trade-offs. We analyze how representation choices, dataset design, and evaluation protocols influence reported results, and highlight key challenges such as domain shift, low-SNR environments, and multi-signal interference. Finally, we outline future directions focused on improving generalization, integrating classical signal processing with learning-based methods, and enabling efficient deployment in real-world and resource-constrained systems. Full article

Efficient spectrum sensing remains a main challenge for Cognitive Radio Networks (CRNs), especially in a wireless environment where methods like energy detection have high uncertainty. This work proposes an entropy-based spectrum-sensing system enhanced with machine-learning algorithms and implemented on a Software-Defined Radio (SDR) platform for real scenario testing. Entropy measures, such as Shannon and Rényi entropies, are used as discriminative features to distinguish occupied and idle frequency bands and release the channel if needed. Machine learning classifiers have achieved good results. In this research, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and Random Forests (RFs) are used with data captured via a GNU Radio and the Universal Software Radio Peripheral (USRP)-based SDR testbed. The experimental results demonstrate a probability of detection (P_d) above 0.9 and a false alarm rate (P_fa) below 0.1, indicating a substantial improvement over the classical energy detector of more than 20% for some signal-to-noise ratio (SNR) values. The integration of entropy metrics with machine learning (ML) models enables a dynamic detection in variable spectral environments, providing a practical framework for CRNs. Full article