Cognitive radio is a promising technology to improve spectral efficiency. However, the secure performance of a secondary network achieved by using physical layer security techniques is limited by its transmit power and channel fading. In order to tackle this issue, a cognitive unmanned aerial vehicle (UAV) communication network is studied by exploiting the high flexibility of a UAV and the possibility of establishing line-of-sight links. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAV’s trajectory and transmit power. Our problem formulation takes into account two practical inaccurate location estimation cases, namely, the worst case and the outage-constrained case. In order to solve those challenging non-convex problems, an iterative algorithm based on S-Procedure is proposed for the worst case while an iterative algorithm based on Bernstein-type inequalities is proposed for the outage-constrained case. The proposed algorithms can obtain effective suboptimal solutions of the corresponding problems. Our simulation results demonstrate that the algorithm under the outage-constrained case can achieve a higher average secrecy rate with a low computational complexity compared to that of the algorithm under the worst case. Moreover, the proposed schemes can improve the secure communication performance significantly compared to other benchmark schemes.

In this article, to bridge the gap between the traditional model-based methods and data-driven deep learning schemes, we propose a physical model-inspired deep unrolling network for solving nonlinear inverse scattering problems, termed PM-Net. The proposed end-to-end network is formed by two consequent steps. First, an augmented Lagrangian method is introduced to transform a constrained objective function to be an unconstrained optimization. In addition, it is further decomposed into four quasi-linear subproblems. Second, we unfold the iterative scheme into a layer-wise deep neural network. Each subproblem is mapped into a module of the deep unrolling network. In PM-Net, these variables including the weight, the regularization of contrast, and other parameters are learned and updated alternately by corresponding network layers. PM-Net effectively combines neural networks with the knowledge of underlying physics as well as traditional techniques. Unlike existing networks, PM-Net explicitly exploits contrast source and contrast modules. Compared to traditional iterative methods, the performance of PM-Net is comparable or even better than subspace-based optimization method in the high noise-level circumstance. Compared to the state-of-the-art learning approaches, not only less network parameters need to be learned, but also better performance is achieved by PM-Net.

Given the obstacle in accentuating the reconstruction accuracy for diagnostically significant tissues, most existing MRI reconstruction methods perform targeted reconstruction of the entire MR image without considering fine details, especially when dealing with highly under-sampled images. Therefore, a considerable volume of efforts has been directed towards surmounting this challenge, as evidenced by the emergence of numerous methods dedicated to preserving high-frequency content as well as fine textural details in the reconstructed image. In this case, exploring the merits associated with each method of mining high-frequency information and formulating a reasonable principle to maximize the joint utilization of these approaches will be a more effective solution to achieve accurate reconstruction. Specifically, this work constructs an innovative principle named Correlated and Multi-frequency Diffusion Model (CM-DM) for highly under-sampled MRI reconstruction. In essence, the rationale underlying the establishment of such principle lies not in assembling arbitrary models, but in pursuing the effective combinations and replacement of components. It also means that the novel principle focuses on forming a correlated and multi-frequency prior through different high-frequency operators in the diffusion process. Moreover, multi-frequency prior further constraints the noise term closer to the target distribution in the frequency domain, thereby making the diffusion process converge faster. Experimental results verify that the proposed method achieved superior reconstruction accuracy, with a notable enhancement of approximately 2dB in PSNR compared to state-of-the-art methods.

Due to the flexible bistatic configuration and complicated moving trajectory of radar platform, time-domain algorithms have significant focusing performance advantages for bistatic forward-looking synthetic aperture radar (BFSAR) applications. In this paper, a new fast factorized back projection (FFBP) based on orthogonal elliptical polar (OEP) coordinate is proposed for BFSAR imaging. Owing to the orthogonality of OEP system, the spectrum of BFSAR subimages can be compacted into the narrowest range and very low Nyquist sampling rate can be utilized in FFBP recursion. Comparing with the conventional FFBP based on original elliptical polar coordinate, the proposed OEP-based algorithm has prominently reduced burden in computation, especially for the BFSAR cases with large baseline geometry. Moreover, a new wavenumber decomposition-based strategy is presented to reveal two-dimensional Nyquist sample requirement without complicated calculation and mathematical derivation, which makes the FFBP process much more easier to design. Promising results from simulations and raw data experiments are presented to validate the advantages of the proposed algorithm.

To alleviate the extremely intrinsical ill-posedness and nonlinearity of electromagnetic inverse scattering under high contrast and low signal to noise ratio (SNR), we propose a deep unfolding network based on generative adversarial network (GAN) under contrast source inversion (CSI) framework, termed UCSI-GAN. The method solves inverse scattering problems (ISPs) using end-to-end generating confrontation way by incorporating a physical model together with its iterative updating formulation into the internal architecture of GAN. First, the nonlinear iterative scheme is extended to a deep unfolding generator network, and the contrast source and contrast updates are mapped to each module of the generator network. Second, to stabilize the imaging process, we add a refinement network to each variable update. Finally, the discriminator network is employed to ensure the authenticity of reconstructed images. The generator network and discriminator network are alternately trained with a generative adversarial learning strategy to reconstruct the properties of the medium object. Numerical experiments demonstrated that the performance of UCSI-GAN is better than traditional CSI and state-of-the-art learning approaches under high contrast and low SNR condition.

Electromagnetic inverse scattering problems (ISPs) aim to retrieve permittivities of dielectric scatterers from the scattering measurement. It is often highly nonlinear, causing the problem to be very difficult to solve. To alleviate the issue, this letter exploits a linear model-based network (LMN) learning strategy, which benefits from both model complexity and data learning. By introducing a linear model for ISPs, a new model with network-driven regularizer is proposed. For attaining efficient end-to-end learning, the network architecture and hyper-parameter estimation are presented. Experimental results validate its superiority to some state-of-the-arts.