Orthogonal time-frequency-space (OTFS) modulation, which demonstrates the superior robustness against time-frequency-selective fading over the orthogonal frequency-division multiplexing (OFDM) modulation, has been deemed to be a promising two-dimensional transmission technique for high-mobility wireless communications. Unfortunately, OTFS systems would suffer frominter-frame interference(IFI),inter-Doppler interference(IDoI), andinter-delay interference(IDeI). In this paper, a novel generalized channel model involving IFI, IDoI, and IDeI for wireless OTFS systems has been derived by considering practical factors such as Doppler effect, carrier-frequency drift of local oscillators, multi-path fading, and cyclic prefix coding. Meanwhile, the symbol-error-rate (SER) performance of the PSK-OTFS system is theoretically investigated accordingly. Monte Carlo simulation results demonstrate that our proposed new theoretical SER performance analysis can lead to more accurate results than the existing theoretical analysis involving IDoI only. Moreover, the detailed quantitative analysis of the dominant interference (IFI/IDoI/IDeI) under different conditions is also conducted in this work. Our proposed new interference analysis can be adopted easily to evaluate the reception quality of the future wireless OTFS transceivers.

As unmanned aerial vehicles (UAVs) are employed for many applications, UAV identification becomes critical to air traffic control nowadays. Conventional radio-frequency (RF) based UAV identification schemes can correctly recognize a UAV according to the acquired sufficient training RF signal data from a pre-specified training candidate set. However, they may often fail when the model of a test UAV is not included in such a training candidate set and/or the training data are quite limited. To address the aforementioned practical challenges, a novel RF-based open-set few-shot UAV recognition technique is introduced in this work. In our proposed new approach, an RF signal of interest, such as a UAV control signal and a digital data-transmission (DDT) signal, is first sensed and segmented in the UAV frequency band using the corresponding short-time energy-to-spectral entropy ratio (ST-ESER). Then, the cyclic spectrum analysis is applied to the selected RF signal segments to construct the corresponding polyspectra, which are further utilized to establish the cyclic-paw-print (CPP) tensors. Moreover, we design a novel deep-learning (DL) network, namely transformer-attention squeeze-excitation network (TASE-Net), by fusing the transformer-enhanced squeeze-and-excitation (SE) model and the Gaussian mixture model (GMM) into the residual network. The TASE-Net can excel in global feature modeling and unknown class detection simultaneously especially for the open-set and few-shot scenarios. Finally, the constructed CPPs are adopted as the input features of our proposed new TASE-Net to recognize the RF signals (the UAV models). Monte Carlo simulation results demonstrate that our proposed new UAV recognition approach using the TASE-Net can greatly outperform other existing deep-learning methods for open-set few-shot UAV identification.

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.

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.

In this paper, we propose a novel greedy iteration algorithm, called block matching pursuit (BMP), for arbitrary block sparse signal recovery. BMP can recover the target signal without prior information of block structure or block length and can estimate the true sparsity level. In each iteration, BMP processes a correlation test to estimate nonzero entries of signal with a fixed step size, and then gathers all possible nonzero entries to form a candidate list, finally checks the list to choose correct entries according to current estimated sparsity level. The simulation results show that BMP can recover the signal of interest in noiseless or noisy case and achieves an outstanding recovery performance.

A new modulated hopping Discrete Fourier Transform (mHDFT) algorithm which is characterized by its merits of high accuracy and constant stability is presented. The proposed algorithm, which is based on the circular frequency shift property of DFT, directly moves the k-th DFT bin to the position of k = 0, and computes the DFT by incorporating the successive DFT outputs with arbitrary time hop L. Compared to previous works, since the pole of mHDFT precisely settles on the unit circle in the Z-plane, the accumulated errors and potential instabilities, which are caused by the quantization of the twiddle factor, are always eliminated without increasing much computational effort. The numerical simulation results verify the effectiveness and superiority of the proposed algorithm.