Search Results (114)

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

As wireless networks evolve from 5G toward 6G, the complexity of the electromagnetic environment increases sharply. Spectrum usage expands significantly into millimetre-wave (mmWave) and terahertz (THz) high-frequency bands. Network node density and mobility increase markedly. Moreover, communication-sensing-computation functions are deeply integrated. Accurate, real-time, full-band Electromagnetic Spectrum Maps (ESMs) have become a core infrastructure for 6G spectrum situational awareness, Dynamic Spectrum Access (DSA), interference coordination, and Integrated Sensing and Communication (ISAC). However, while a growing body of recent work extends radio mapping to multi-band and temporal domains, the predominant focus of existing Radio Map research remains the two-dimensional spatial power distribution at a single fixed frequency—essentially a degenerate special case of ESM after the frequency and time dimensions are collapsed—and no existing survey unifies 3D spatial construction, time-varying prediction, and full 6G system integration under a shared 4D formalism. This paper focuses on the three core research dimensions of ESMs, i.e., 3D spatial ESM construction, dynamic time-varying ESM modelling and prediction, and ESM integration with 6G systems. Under a unified four-dimensional ESM framework (space × frequency × time × power), we clarify the hierarchical relationships among ESM/SEM/REM/Radio Map/Channel Knowledge Maps (CKMs). Then, we systematically review 3D ESM construction, dynamic ESM modelling and prediction, and the integration of ESM with CKM/Digital Twin Networks (DTNs)/ISAC. Finally, we identify five, core open problems that constrain the development of the field to provide a systematic reference for 6G intelligent spectrum management research. 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

Multi-unmanned aerial vehicle (UAV) platforms integrate radio-frequency (RF) sensing, datalinks, and onboard embedded compute; adversarial electronic warfare (EW) degrades these subsystems through jamming and forces decentralized control policies to act on fragmented observations—a setting aligned with intelligent electronic systems and autonomous robotics in contested spectrum. Cooperative swarms then face two compounding failure modes: loss of coherent situational awareness, and reward-driven passive survival that suppresses mission completion. Memory-based multi-agent reinforcement learning (MARL) partially addresses the first but tends to reinforce the second; dense intent shaping addresses the second but becomes unreliable when observations are incomplete. We propose CIDA (Cognitive–Intent Dual-Stream Architecture), a reinforcement learning framework that decouples belief reconstruction from tactical intent at the representation level while coupling them through a unified actor–critic update. The cognitive stream encodes a 64-step observation history with a pre-normalized Transformer to reconstruct threat belief; the intent stream supplies a hierarchical potential field (reconnaissance, threat-weighted engagement, and approach incentives). A steady-state training mechanism (dynamic reward scaling and adaptive gradient clipping) stabilizes Transformer-based on-policy learning under non-stationary multi-agent dynamics. In a complex terrain scenario with SAM, AAA, and jammer assets, CIDA reaches 96.15% task success versus 12.21% (memoryless PPO) and 25.28% (MAPPO+RNN), with ablations showing nonlinear coupling and emergent tactics such as jammer bypass and weak-sector traversal. Results are robust to a four-fold sweep of the intent-shaping weight (above 90% success). Full article

Automatic modulation recognition (AMR) is increasingly relevant to communication-sensing front ends in robotic and human–robot collaborative systems, where reliable spectrum awareness and adaptive wireless reception are desired. However, existing methods often degrade sharply at low signal-to-noise ratios (SNRs), and large language models (LLMs) are not natively compatible with continuous I/Q signals due to the inherent modality gap. We propose BioLAMR, a GPT-2 adaptation framework for AMR inspired by the auditory system’s parallel time–frequency processing and cortical hierarchy. The framework combines bio-inspired dual-domain feature extraction with parameter-efficient LLM adaptation. BioLAMR includes three components. First, a lightweight dual-domain fusion (LDDF) module extracts complementary time- and frequency-domain features and fuses them through channel and spatial attention. Second, a convolutional embedding module converts continuous I/Q signals into GPT-2-compatible sequences without discrete tokenization. Third, a hierarchical fine-tuning strategy updates only 8.9% of parameters to preserve pretrained knowledge while adapting to modulation recognition. Experiments on the RadioML2016.10a and RadioML2016.10b benchmarks show that BioLAMR achieves overall accuracies of 64.99% and 67.43%, outperforming the strongest competing method by 2.60 and 2.47 percentage points, respectively. Under low-SNR conditions, it reaches 36.78% and 38.14%, the best results among the compared methods. Ablation studies verify the contribution of each component. These results demonstrate that combining dual-domain signal modeling with parameter-efficient GPT-2 adaptation is an effective route to robust AMR in challenging wireless environments. Full article

Unauthorized unmanned aerial vehicles (UAVs) threaten Vehicle-to-Everything (V2X) spectrum security. Real-time edge detection faces strict hardware constraints, severe multipath fading, and Doppler distortions. This article proposes HA-EffNet, a physics-informed multi-task learning framework engineered for radio frequency (RF) sensing on roadside units (RSUs). The network restricts its temporal receptive field to align mathematically with the channel coherence time, thereby preventing deep noise overfitting. A hierarchical mechanism integrates Efficient Channel Attention (ECA) for shallow noise suppression and Receptive Field Attention (RFA) for deep signature extraction. Furthermore, the shared multi-task architecture simultaneously executes discrete classification and continuous spectral parameter regression, effectively halving computational overhead compared to redundant single-task deployments. Evaluations on the Microphase and DroneRFa datasets yield classification accuracies of 97.88% and 94.67%. Compound tests integrating Tapped Delay Line C (TDL-C) models and dynamic signal-to-noise ratio (SNR) variations validate algorithmic resilience against severe physical degradation. Utilizing a 0.12-million-parameter footprint, the network delivers a 0.84 ms inference latency and 1204.9 frames per second (FPS) throughput on the NVIDIA Jetson Orin Nano Super, providing a highly efficient edge-sensing solution. Full article

The inversion of wind and wave parameters in high-frequency ocean remote sensing radar relies heavily on the sea echo Doppler power spectrum. However, the accuracy of parameter inversion is often compromised by radio frequency interference (RFI), which distorts the Doppler spectral power distribution. Existing RFI suppression algorithms primarily focus on enhancing the signal-to-interference-plus-noise ratio post-mitigation, while insufficient attention has been paid to the spectral power fluctuations induced by these suppression processes. To address this issue, this study proposes a narrowband RFI suppression scheme that combines inter-pulse phase agility (IPA) with orthogonal projection (OP). An optimized aperiodic sequence is used to modulate the inter-pulse phases of the transmitted waveform, thus uniformly dispersing the sea echo power across the entire Doppler spectrum. Spatial OP is then applied to suppress RFI stripes on the range-Doppler spectrum, a process in which only the sea echo samples masked by the RFI stripes are affected. Finally, phase compensation restores the sea echo coherence and disperses residual RFI power uniformly into the Doppler domain, minimizing its localized impact. Simulations and semi-synthetic tests involving real-world interference verify that the proposed scheme effectively suppresses RFI while alleviating spectral distortion in the sea-echo Doppler spectrum. Full article

This paper presents a unified perspective on Orthogonal Frequency-Division Multiplexing (OFDM)-based joint radar–communication (JRC) sensing, focusing on the efficient reuse of time and frequency resources in range–Doppler estimation and imaging scenarios. By leveraging OFDM’s inherent subcarrier orthogonality, noise-like temporal properties, and minor carrier frequency offsets, these systems can support concurrent transmissions over the same spectral and temporal resources while maintaining interference resilience. Experimental and simulation-based insights demonstrate the feasibility of simultaneous sensing across users and antennas, even in dense Radio Frequency (RF) environments. We analyze trade-offs, implementation considerations, and system-level implications to provide a consolidated foundation for designing future OFDM-based JRC systems. The feasibility of an Orthogonal Time Frequency Space (OTFS) waveform for the proposed method is also investigated. The review highlights the potential of such architectures in spectrum and time-congested applications such as Vehicle-to-Everything (V2X), indoor localization, Internet of Things (IoT), and beyond fifth-generation (5G) networks. Full article

With the continuous proliferation of radio frequency devices, electromagnetic environments in various regions are becoming increasingly complex. Effective monitoring of the electromagnetic environment and identification of interference sources have thus become critical tasks for maintaining order in the electromagnetic spectrum. In recent years, rapid advances in UAV technology have spurred exploration of UAV-based electromagnetic spectrum monitoring as a novel approach. However, the limited payload capacity and endurance of UAVs constrain their monitoring capabilities. To address these challenges, we propose HMUDRL, a distributed heterogeneous multi-agent deep reinforcement learning algorithm. By leveraging cooperative operation between cluster-head UAVs (CH) and cluster-monitoring UAVs (CM) within a heterogeneous UAV swarm, HMUDRL enables high-precision detection and wide-area localization of UHF radiation source. Furthermore, we integrate a minimum-gap localization algorithm that exploits the spatial distribution of multiple CM to accurately pinpoint anomalous radiation sources. Simulation results validate the effectiveness of HMUDRL: in the later stages of training, the success rate of localizing target radiation sources converges to 96.1%, representing an average improvement of 1.8% over baseline algorithms; localization accuracy, measured by root mean square error (RMSE), is enhanced by approximately 87.3% compared to baselines; and communication overhead is reduced by more than 80% relative to homogeneous architectures. These results demonstrate that HMUDRL effectively addresses the challenges of data transmission control and sensing-localization performance faced by UAVs in UHF spectrum monitoring. 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

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

Unmanned aerial vehicles (UAVs) are emerging as mobile aerial platforms for radio frequency (RF) spectrum sensing, enabling dynamic monitoring of the spectrum occupancy of cellular systems at different altitudes. The impact of UAV receiver antenna configurations, particularly with respect to altitude, is critical in determining occupancy performance. In this paper, we present a height-dependent analytical framework for UAV-based spectrum occupancy, focusing on how different receiver antenna configurations affect the sensed signal power. We consider two types of 3D antenna patterns: a typical dipole antenna and a downward directional antenna. Using a stochastic geometry-based approach, we derive closed-form expressions for the altitude-dependent sensed power under both antenna configurations. Moreover, we execute ray tracing-based analysis with a real-world 3-D map and realistic antenna patterns. Monte Carlo simulations are conducted to validate the analytical results, revealing that both altitude and antenna directivity critically affect occupancy accuracy and coverage. Full article

Wideband spectrum sensing is a crucial technology for the efficient utilization of limited frequency resources in cognitive radio. While deep learning models have yielded promising results, they typically rely on either time-domain (I/Q) or frequency-domain (FFT) data alone, which can limit their performance. This study proposes IPFSCNN (IQ-Parallel FFT-Serial CNN), a novel asymmetric hybrid architecture that synergistically fuses both data representations. The key idea of its design is an asymmetric architecture that employs two specialized streams: a parallelized branch to efficiently capture temporal features from I/Q data, and a deep serial branch to extract spectral patterns from FFT data. These complementary features are fused to perform a multi-label classification task. Experiments on an LTE-M dataset demonstrate that the proposed IPFSCNN achieves a higher detection performance than state-of-the-art models, including DeepSense and ParallelCNN, particularly in low signal-to-noise ratio conditions. Furthermore, IPFSCNN achieves this superior accuracy while maintaining high computational efficiency, requiring 15% fewer parameters and only one-third of the multiply-accumulate (MAC) operations compared to the DeepSense model. Crucially, a comprehensive ablation study validates this asymmetric design, proving that the proposed ‘IQ-Parallel FFT-Serial’ combination is demonstrably superior to other hybrid configurations. Full article

Mobile Cognitive Radio Networks (MCRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic access to underutilized frequency bands assigned to Primary or Licensed Users (PUs). These networks rely on Cooperative Spectrum Sensing (CSS) to identify available spectrum, but this collaborative approach also introduces vulnerabilities to security threats—most notably, Spectrum Sensing Data Falsification (SSDF) attacks. In such attacks, malicious nodes deliberately report false sensing information, undermining the reliability and performance of the network. This paper investigates the application of machine learning techniques to detect and mitigate SSDF attacks in MCRNs, particularly considering the additional challenges introduced by node mobility. We propose a hybrid detection framework that integrates a reputation-based weighting mechanism with Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers to improve detection accuracy and reduce the influence of falsified data. Experimental results on software defined radio (SDR) demonstrate that the proposed method significantly enhances the system’s ability to identify malicious behavior, achieving high detection accuracy, reduces the rate of data falsification by approximately 5–20%, increases the probability of attack detection, and supports the dynamic creation of a blacklist to isolate malicious nodes. These results underscore the potential of combining machine learning with trust-based mechanisms to strengthen the security and reliability of mobile cognitive radio networks. Full article

Radio Frequency Interference has emerged as a growing challenge for aviation safety and system integrity due to the increasing spectral overlap between communication technologies and aviation systems. This paper investigates the sources, types, and consequences of RFI in Global Navigation Satellite Systems, Instrument Landing Systems, and altimeters used in civil aviation. A detailed examination of both intentional and unintentional interference is presented, highlighting real-world incidents and simulated impact models. The study analyzes technical mechanisms such as receiver desensitization, intermodulation, and cross-modulation, and further explores UAV-based interference detection frameworks. Mitigation strategies are reviewed, including regulatory practices, spectrum filters, shielding architectures, and dynamic UAV sensing systems. Comparative insights into simulation results, shielding techniques, and regulatory gaps are discussed. The paper concludes with recommendations for enhancing current aviation standards and suggests a hybrid validation model combining in-flight measurements with simulation-based assessments. This research contributes to the understanding of electromagnetic vulnerabilities in aviation and provides a basis for future mitigation protocols. Full article