In this letter, we consider hierarchical cooperative spectrum sensing based on two-threshold energy detection in cognitive radio networks. In order to solve the sensing failure problem in conventional two-threshold energy detection, soft combination of the observed energy value from different cognitive radio users is investigated. Encouraged by the performance gain of soft combination of energy falling between the two thresholds, we further propose a new softened hard combination scheme to reduce the communication overhead. Results show that the proposed method achieves a good tradeoff between sensing performance and communication overhead.

Resorting to power accurate delivery for wireless power transfer (WPT) based on microwaves, a theory and method based on time reversal (TR) for WPT in 3D indoor environments are presented in this paper. Conventional WPT methods have the limitations of requiring that the elements on power source array (PSA) must have the same structure and regular arrangement. Unlike those conventional methods, the PSA used in this paper can be constructed by any omnidirectional antenna elements with any structure and any arrangement, whose principles are based on the auto-match of antenna radiation and environment adaptability of TR. Moreover, compared with those conventional methods, which perform complex multi-objective optimizations of beam pattern cost-functions at each frequency for getting excitation of PSA, the proposed algorithm can obtain PSA's excitation over a wide frequency range only by a single run of TR and Fourier transform operations. In addition, by weighting the time reversed charging request signal from powered devices, the interference caused by different observing elevation angles and amplitude difference among each channel can be suppressed effectively. At last, multi-powered devices placed arbitrarily can be charged simultaneously, and get similar power from PSA without power unfairness by our proposed algorithm.

One-bit Synthetic Aperture Radar (SAR) imaging, which collects echoes into one-bit quantized samples, is a highly promising technique for the faster SAR images acquisition and simplified implementations in numerous applications. Harmonics, accompanied with one-bit sampling operation, are one of the fundamental factors that trigger severe aliasing artifacts problem in frequency domain. For one-bit SAR imaging, rather than sampling the echoes with extremely high rate to alleviate the effects of harmonics, a Convolutional De-Quantization Network, termed as CDQOB-net, is proposed to establish de-aliasing in a transformed imagery domain. The proposed method starts with a fresh interpretation on the regular range-Doppler imaging, which is also referred to as the 2-dimensional (2D) transformed domain of SAR echo. Then, in the light of deep learning framework for image restoration, an alternative method designed in the perspective of harmonics is specifically analyzed, wherein the degradation caused by harmonics in transformed domain are sufficiently estimated. Consequently, as a data-driven method, our two-stage CDQOB-net is proposed and trained to handle aliasing harmonics and multiple distortions, which can significantly improve the quality of one-bit SAR imaging. The proposed method is easy to implement and computationally efficient. Extensive experiments on simulated and real SAR data also demonstrate its validation and effectiveness.

In this letter, we consider cooperative spectrum sensing with the presence of malicious users. The detailed detection performance and sensing efficiency are formulated to analyze the impact of the incorrect information on the sensing system. To cope with the misleading effect of the malicious users, we further propose to implement the authentication mechanism to filter out the incorrect information. We also optimize the authentication code length to reduce the system overhead. Simulation results demonstrate that we can reap comparable performance with the authentication mechanism and achieve a good tradeoff between sensing efficiency and communication overhead.

The high fidelity and fraudulence of deceptive jamming can severely mislead synthetic aperture radar (SAR), which poses a big challenge in SAR imaging. In this letter, an imagery domain target reconstruction approach is proposed to suppress deceptive jamming for single-channel SAR. Specifically, the distinction between the echo signals of true targets and deceptive jamming in the azimuth phase is ascertained first. Then, the imaging process encountering deceptive jamming is formulated as a linear model, and the target reconstruction problem is converted to a linear inverse problem with the dictionary containing the explored phase characteristics. Finally, considering the sparsity of man-made targets, the imageries of the true and false targets can be reconstructed simultaneously through solving this sparse signal recovery problem. Theoretical analysis and experimental results showcase the superiority of the proposed method.