This paper introduces a novel automatic modulation classification (AMC) method using the graph-based fractional lower-order cyclic-spectrum analysis in the α-stable noise environment. The noise in the practical communication scenario usually exhibits impulse characteristics in the statistical sense, which could be modeled as the α-stable distribution. This would make the second- or higher-order statistics of the received signal vanish, and thus the performances of the conventional AMC algorithms designed for Gaussian noise significantly deteriorate when directly employed in the α-stable noise. In our proposed framework, the fractional lower-order cyclic spectrum (FLOCS) analysis is first invoked to acquire the polyspectra of the signal corrupted by the α-stable noise. Then, the graph-based AMC mechanism is systematically established upon the graph representation of the FLOCS to identify the modulation type according to the discrepancies between the graph features derived from the training and test data. The performance of our proposed new algorithm is theoretically analyzed, and the correct classification probability ${P_{cc}}$ over the modulation candidate set is formulated analytically. The remarkable scalability and efficiency of our proposed approach for the modulation candidate set variation are also theoretically investigated in detail. Monte Carlo simulation results demonstrate the effectiveness and superiority of the proposed AMC scheme.

Unmanned aerial vehicle (UAV) sensing based on the emitted radio-frequency (RF) signals is investigated for the UAV surveillance and control. A novel robust RF-based UAV sensing approach is introduced in this paper. In our proposed new approach, once an RF signal, such as UAV telemetry, track, and command (TT&C) signal and digital data-transmission (DDT) signal, emitted by a UAV is received, the corresponding minimum variance distortionless response (MVDR) spectrum is obtained to extract the feature vector. Then, a novel hybrid lightweight deep-learning network, namely UAV-CTNet, is designed by fusing transformer and convolution to facilitate the feature-map recognition based on the aforementioned feature vector so that the UAV can be detected and identified. Furthermore, Monte Carlo simulations are carried out to evaluate the effectiveness of our proposed new UAV sensing scheme in terms of the detection accuracy and correct recognition rate using the UAV’s TT&C and DDT signals in the presence of interference signals.

Automatic composite-modulation classification (ACMC) is deemed to be an essential and important cognitive mechanism adopted for the next generation intelligent telemetry, tracking, and command (TT&C), cognitive aerospace communications, and space surveillance as it can automatically recognize the unknown composite-modulation (CM) scheme of the received signal. In this work, we introduce a novel ACMC method using the hybrid feature-vectors based on our proposed new cyclic-paw-print images extracted from the CM signals. In our proposed novel approach, according to the polyspectral analysis of the received CM signals, a received CM signal can be converted to a gray-scale image matrix, named cyclic-paw-print (CPP), which can be very robust against noise. Then, the discrete cosine transform (DCT) and discrete wavelet transform (DWT) are simultaneously applied to the obtained CPP matrix and the hybrid DCT/DWT feature-vector is further constructed thereby. Finally, the sequential minimal optimized support vector machine (SMO-SVM) is adopted as the classifier using such feature vectors. Our proposed new ACMC technique requires a lower computational complexity and leads to a higher recognition-accuracy than other existing ACMC methods according to Monte Carlo simulations and experiments using real CM signal data.

Automatic modulation classification (AMC) has been intriguing many researchers as it has many civil and military applications. Recently, cooperative AMC (CAMC) using a dynamic or ad hoc sensor network becomes appealing and challenging. As the unmanned aerial vehicles (UAVs) can facilitate three-dimensional communication/sensor network, we propose a novel CAMC approach based on a dynamic (ad hoc) UAV network. In our proposed new CAMC approach, the local classification decisions, which are made by spatially distributed nodes (UAVs) using our previously proposed graph-based modulation classifier, are gathered to reach an overall decision by a new weighted voting mechanism pertinent to individual received signal qualities. Note that the fusion center does not have to be a fixed UAV and it can be dynamically reassigned to any UAV within the same network in each sensing interval. The corresponding weights to individual UAVs are to be determined according to their cumulative states and the temporal discount factor. As a result, our proposed new CAMC approach can be fully distributed as no control center (or hub) is necessary. Besides, our new CAMC scheme can accommodate realistic ad hoc network variations to allow the existing UAVs to depart and/or the new UAVs to join in any sensing interval. Monte Carlo simulation results demonstrate that our proposed new CAMC scheme is quite robust and outperforms the existing CAMC method.

Automatic composite-modulation classification (ACMC) has been considered as an essential function in the next generation intelligent telemetry, tracking & command (TT&C), cognitive space communications, and space surveillance. This paper introduces a novel ACMC scheme using the cyclic-paw-print extracted from the composite-modulation (CM) signals. In this new framework, the cyclic-spectrum analysis is first invoked to acquire the polyspectra of the received CM signals corrupted by different fading channels. Then, a new feature, namely cyclic-paw-print (CPP), is established upon the image representation of the cyclic spectrum, which can be robust against channel noise. Then, a highly-efficient ultra lightweight deep-learning network (ULWNet), which takes the CPPs as the input features, is designed to identify the composite modulation type. Our proposed new scheme can greatly improve the computational efficiencies incurred by the existing deep-learning networks and capture more reliable features latent in CM signals to result in an excellent classification accuracy. Monte Carlo simulation results demonstrate the effectiveness and the superiority of our proposed new ACMC scheme to the existing deep-learning networks.

Unmanned aerial vehicles (UAVs) are considered to be excellent candidates of airborne relays or base stations for the 3-D ad hoc networks. They can be promptly deployed to serve large bursts of communication traffic in cellular networks or provide timely network support in wireless sensor networks (WSNs). It is desirable but challenging to dynamically find the optimal deployment strategy of UAVs in the air to provide a better Quality of Service (QoS) for a UAV-assisted wireless network. In this article, we propose a novel Gibbs-sampling distributed algorithm (GSDA) to dynamically optimize the UAVs’ locations when they serve as airborne base stations for ground users. In our proposed GSDA, channel capacity is adopted as the objective function and a distributed approach is employed such that each UAV is able to optimize its location independently and asynchronously. Furthermore, we propose a polynomial-regression-based predictor to make use of users’ moving trajectories and take advantage of the predicted users’ future locations to expedite the convergence of the GSDA. Meanwhile, we also compare our proposed GSDA with the existing distributed genetic algorithm. The asynchronization of UAV location updates and the location errors are also investigated to evaluate the robustness of the GSDA. Simulation results demonstrate that our proposed novel GSDA is quite robust and superior to the existing distributed genetic algorithm.