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The hybrid terrestrial-satellite system is a promising and attractive infrastructure for future communication networks, conceived to provide navigation assistance, wide coverage, and services in tunnel areas. In this paper, we analyze the performance of two-way hybrid terrestrial-satellite relaying systems over generalized fading channels. More specifically, the satellite-relay link is modeled as the $\kappa$ - $\mu$ shadowed fading channel, whereas the user equipment (UE)-relay link is assumed to follow the Nakagami- $m$ distribution. Despite mathematical intractability, we derive novel and exact outage probability, symbol error probability, and achievable rate expressions in order to tap into hitherto unexplored performance analysis. To obtain better insight into the implications of the channel parameters on the system performance, we also provide asymptotic outage probability expressions at high signal-to-noise ratios. To further improve the system performance, we extend our analysis to a multiple-relay network and propose two relay selection schemes. It is interesting to find that the relay selection scheme of maximizing the received signal power of the satellite can significantly achieve better outage probability performance than that of the $N$ th worst relay selection scheme for the sake of conserving the satellite's power. Finally, we provide simulation results to verify the accuracy of the derived analytical results. [ABSTRACT FROM AUTHOR]

Ensuring the physical layer security (PHY-security) of millimeter-wave (mmWave) communications is one of the key factors for the success of 5G. Recent field measurements show that conventional fading models cannot accurately model the random fluctuations of mmWave signals. To tackle this challenge, the fluctuating two-ray (FTR) fading model has been proposed. In this correspondence, we comprehensively analyze the PHY-security in mmWave communications over FTR fading channels. More specifically, we derive analytical expressions for significant PHY-security metrics, such as the average secrecy capacity, the secrecy outage probability, and the probability of strictly positive secrecy capacity, with simple functions. The effect of channel parameters on the PHY-security has been validated by numerical results. [ABSTRACT FROM AUTHOR]

The fluctuating two-ray (FTR) fading model provides a much better fit than other fading models for small-scale fading measurements in millimeter wave communications. In this paper, using a mixture of gamma distributions, new exact analytical expressions for the probability density and cumulative distribution functions of the FTR distribution with arbitrary fading parameters are presented. Moreover, the performance of digital communication systems over the FTR fading channel is evaluated in terms of the channel capacity and the bit error rate. The interaction between channel fading parameters and system performance is further investigated. Our newly derived results extend and complement previous knowledge of the FTR fading model. [ABSTRACT FROM AUTHOR]

Impulsive processes frequently occur in many fields, such as radar, sonar, communications, audio and speech processing, and biomedical engineering. In this paper, we propose a nonconvex linear regression (NLR) based minimum dispersion beamforming technique for impulsive signals to achieve significant performance improvement over the conventional minimum variance beamformer. The proposed beamformer minimizes the \ellp-norm of the output with p<1 subject to a linear distortionless response constraint, resulting in a difficult nonconvex and nonsmooth optimization problem. The constrained optimization problem is first reduced to a multivariate linear regression via constraint elimination. As a major contribution of this paper, a coordinate descent algorithm (CDA) is devised for solving the resultant NLR problem of \ellp-minimization with p<1 at a computational complexity of \cal O(MN^2), where M is the number of sensors and N is the sample size. At each inner iteration of the CDA, an efficient algorithm is designed to find the global minimum of each subproblem of univariate linear regression. The convergence of the CDA is analyzed. The NLR beamformer with a single constraint is further generalized to the case of multiple linear constraints, which is robust against model mismatch. Simulation results demonstrate the superior performance of nonconvex optimization based beamformer. [ABSTRACT FROM AUTHOR]

Greedy pursuit, which includes matching pursuit (MP) and orthogonal matching pursuit (OMP), is an efficient approach for sparse approximation. However, conventional greedy pursuit algorithms designed for inner product space are not robust against outliers. In this paper, we devise a new definition of correlation in \ell p-space with p>0, called \ell p-correlation, and introduce the concept of orthogonality in \ell p-space. Based on the \ell p-correlation and \ell p-orthogonality, which are generalizations of the absolute inner product and orthogonality of inner product space, respectively, we develop three greedy pursuit algorithms, namely, \ell p-MP, \ell p-OMP, and weak \ell p-MP, for robust sparse approximation in the presence of outliers. The convergence of the three algorithms is proved. In particular, the \ell p-norm of the residual of each algorithm decays exponentially. It is revealed that the exponential decay factor in the worst case is related to the minimal \ell p-correlation of the residual and the dictionary. Numerical examples on sparse signal recovery and harmonic retrieval in impulsive noise demonstrate that the \ell p-greedy pursuit algorithms with 0< p< 2 are more outlier-resistant than their counterparts in inner product space. [ABSTRACT FROM PUBLISHER]

A common approach to time-delay estimation (TDE) and joint delay-Doppler estimation (JDDE) for target localization is matched filtering, which is equivalent to the cross correlation of the received and transmitted signals. The conventional correlation in Hilbert space is statistically optimal in white Gaussian noise. However, its performance significantly degrades in the presence of non-Gaussian noise or clutter. In this paper, two new concepts called \ellp-correlation and \ellp-ambiguity functions, which generalize the conventional matched filtering from Hilbert space to \ellp-space, are proposed. In addition, several important mathematical properties of the \ellp-correlation and \ellp-ambiguity functions are given and proved. Compared with conventional ambiguity function, the time-frequency concentration of the \ellp-ambiguity function is significantly enhanced, i.e., the delay-Doppler resolution is improved and the sidelobes are suppressed. Based on these two newly defined functions, a family of TDE and JDDE algorithms that are robust against impulsive noise or clutter are developed. Furthermore, the Cramér–Rao bounds of TDE and JDDE for non-Gaussian noise with arbitrary probability distribution are derived. Simulation results under several impulsive noise models demonstrate that the performance in terms of robustness, resolution, and estimation accuracy is substantially improved when compared with the conventional matched filtering and fractional lower-order moment based methods. [ABSTRACT FROM AUTHOR]

A quadratically constrained minimum dispersion (QCMD) beamformer that is robust against model uncertainties is devised for non-Gaussian signals. Different from the minimum variance based beamformers, the QCMD beamformer minimizes the \ellp-norm (p\geq 1) of the output while constraining the magnitude response of any steering vector within a spherical uncertainty set to exceed unity. A gradient projection algorithmic framework is proposed to efficiently solve the resulting convex optimization problem instead of directly applying the standard optimization algorithm which has a high computational complexity. In each iteration, the gradient projection updates the solution along the gradient direction and projects it back to the constraint set. Importantly, a closed-form expression of the projection onto the constraint set is derived, which only needs a low complexity of \cal O(M) with M being the sensor number. Therefore, the proposed algorithm is much faster and simpler to implement compared with the standard method. In addition, the robust constant modulus beamformer (RCMB) is also discussed as a special case of the QCMD beamformer. Simulation results demonstrate the efficiency of the gradient projection algorithm and superiority of the QCMD beamformer over several representative robust beamformers, indicating that it can approach the optimal performance bound. [ABSTRACT FROM PUBLISHER]

A family of algorithms, named \ell p-MUSIC, for direction-of-arrival (DOA) estimation in impulsive noise is proposed. The \ell p-MUSIC estimator adopts the \ell p-norm (1\leq p< 2) of the residual fitting error matrix as the objective function for subspace decomposition, rather than the Frobenius norm that is used in the conventional MUSIC method. Although the matrix \ell p-norm minimization based subspace decomposition will lead to a nonconvex optimization problem, two iterative algorithms are designed for achieving efficient solutions. The first algorithm is the iteratively reweighted singular value decomposition (IR-SVD), where the SVD of a reweighted data matrix is performed in each iteration. The second algorithm solves the nonconvex matrix \ell p-norm minimization by alternating convex optimization. Two complex-valued Newton's methods with optimal step size in each iteration are devised to solve the resulting convex problem. The convergence of the iterative procedure is also proved. Numerical results verify that the \ell p-MUSIC methodology outperforms the standard MUSIC scheme and several existing outlier-resistant DOA estimation approaches in terms of resolution capability and estimation accuracy. [ABSTRACT FROM PUBLISHER]

This paper addresses robust beamforming in the presence of model uncertainty for constant modulus (CM) signals that are frequently encountered in radar and communication systems. Instead of employing the commonly used minimum variance approach, we utilize a CM criterion that is different from the conventional techniques for blind equalization. The proposed robust beamformer minimizes the CM objective function while constraining the magnitude response of any steering vector within a spherical uncertainty region to exceed unity. We develop two algorithms to solve the resultant nonconvex and nonsmooth optimization problem. The first is subgradient projection method, which is simple but converges slowly and is sensitive to initialization. By introducing auxiliary phase variables, our second scheme reformulates the original nonsmooth problem into a differentiable one, that can be solved by an iterative constrained least squares (ICLS) procedure. At each iteration of the ICLS, a least squares (LS) problem with a second-order cone constraint is solved. More important, a closed-form solution for this constrained LS problem, which has a low computational complexity, is derived. The ICLS has fast convergence and is not sensitive to the initial value. Simulation results demonstrate that the proposed CM beamformer achieves a substantial performance gain compared with several representative robust beamformers. [ABSTRACT FROM AUTHOR]

Spatiotemporal data fusion is a cost-effective way to produce remote sensing images with high spatial and temporal resolutions using multisource images. Using spectral unmixing analysis and spatial interpolation, the flexible spatiotemporal data fusion (FSDAF) algorithm is suitable for heterogeneous landscapes and capable of capturing abrupt land-cover changes. However, the extensive computational complexity of FSDAF prevents its use in large-scale applications and mass production. Besides, the domain decomposition strategy of FSDAF causes accuracy loss at the edges of subdomains due to the insufficient consideration of edge effects. In this study, an enhanced FSDAF (cuFSDAF) is proposed to address these problems, and includes three main improvements. First, the TPS interpolator is replaced by an accelerated inverse distance weighted (IDW) interpolator to reduce computational complexity. Second, the algorithm is parallelized based on the compute unified device architecture (CUDA), a widely used parallel computing framework for graphics processing units (GPUs). Third, an adaptive domain decomposition (ADD) method is proposed to improve the fusion accuracy at the edges of subdomains and to enable GPUs with varying computing capacities to deal with datasets of any size. Experiments showed while obtaining similar accuracies to FSDAF and an up-to-date deep-learning-based method, cuFSDAF reduced the computing time significantly and achieved speed-ups of 140.3–182.2 over the original FSDAF program. cuFSDAF is capable of efficiently producing fused images with both high spatial and temporal resolutions to support applications for large-scale and long-term land surface dynamics. Source code and test data available at https://github.com/HPSCIL/cuFSDAF. [ABSTRACT FROM AUTHOR]

Deep Convolutional Neural Networks (CNN) have drawn great attention in image super-resolution (SR). Recently, visual attention mechanism, which exploits both of the feature importance and contextual cues, has been introduced to image SR and proves to be effective to improve CNN-based SR performance. In this paper, we make a thorough investigation on the attention mechanisms in a SR model and shed light on how simple and effective improvements on these ideas improve the state-of-the-arts. We further propose a unified approach called “multi-grained attention networks (MGAN)” which fully exploits the advantages of multi-scale and attention mechanisms in SR tasks. In our method, the importance of each neuron is computed according to its surrounding regions in a multi-grained fashion and then is used to adaptively re-scale the feature responses. More importantly, the “channel attention” and “spatial attention” strategies in previous methods can be essentially considered as two special cases of our method. We also introduce multi-scale dense connections to extract the image features at multiple scales and capture the features of different layers through dense skip connections. Ablation studies on benchmark datasets demonstrate the effectiveness of our method. In comparison with other state-of-the-art SR methods, our method shows the superiority in terms of both accuracy and model size. [ABSTRACT FROM AUTHOR]