Outage Analysis for SWIPT-Enabled Two-Way Cognitive Cooperative Communications

In this paper, we study a cooperative cognitive radio network (CCRN) where the secondary user-transmitter (SU-Tx) assists bi-directional communication between a pair of primary users (PUs) following the principle of two-way relaying. In return, it gets access to the spectrum of the PUs to enable its own transmission to SU-receiver (SU-Rx). Further, in order to support sustainable operation of the network, SU-Tx is assumed to harvest energy from the RF signals received from the PUs, using the technique of simultaneous wireless information and power transfer (SWIPT). Assuming a decode-and-forward behaviour and power-splitting based relaying protocol at SU-Tx, closed form expressions for outage probability of PU and SU are obtained. Simulation results validate our analytical results and illustrate spectrum-efficiency and energy-efficiency advantages of the proposed system over one-way relaying.Comment: 15 pages, 5 figures, Submitted to IEEE Transactions on Vehicular Technolog

Secure Full-Duplex Device-to-Device Communication

This paper considers full-duplex (FD) device-to-device (D2D) communications in a downlink MISO cellular system in the presence of multiple eavesdroppers. The D2D pair communicate sharing the same frequency band allocated to the cellular users (CUs). Since the D2D users share the same frequency as the CUs, both the base station (BS) and D2D transmissions interfere each other. In addition, due to limited processing capability, D2D users are susceptible to external attacks. Our aim is to design optimal beamforming and power control mechanism to guarantee secure communication while delivering the required quality-of-service (QoS) for the D2D link. In order to improve security, artificial noise (AN) is transmitted by the BS. We design robust beamforming for secure message as well as the AN in the worst-case sense for minimizing total transmit power with imperfect channel state information (CSI) of all links available at the BS. The problem is strictly non-convex with infinitely many constraints. By discovering the hidden convexity of the problem, we derive a rank-one optimal solution for the power minimization problem.Comment: Accepted in IEEE GLOBECOM 2017, Singapore, 4-8 Dec. 201

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

Robust secrecy beamforming with energy-harvesting eavesdroppers

This letter considers simultaneous wireless information and power transfer (SWIPT) in multiple-input-single-output downlink systems in which a multiantenna transmitter sends a secret message to a single-antenna information receiver (IR) with multiple single-antenna energy receivers (ERs). We aim to maximize the harvested energy by the ERs while maintaining the signal-to-interference-and-noise ratio (SINR) threshold at the IR and keeping the message secure from possible eavesdropping by the ERs by suppressing their SINRs. Both scenarios of perfect and imperfect channel state information at the transmitter are studied. Using semidefinite relaxation techniques, we show that there always exists a rank-one optimal solution for the IR, i.e., transmit beamforming is optimal for the IR

UAV-Aided Jamming for Secure Ground Communication with Unknown Eavesdropper Location

This paper investigates unmanned aerial vehicle (UAV)-aided jamming technique for enabling physical layer keyless security in scenarios where the exact eavesdropper location is unknown. We assume that the unknown eavesdropper location is within an ellipse characterizing the coverage region of the transmitter. By sequentially optimizing the transmit power, the flight path of the UAV and its jamming power, we aim at maximizing the average secrecy rate with arbitrary eavesdropper location. Simulation results demonstrate that the optimal flight path obtains better secrecy rate performance compared to that using direct UAV flight path encasing the transmitter and the legitimate receiver. Most importantly, even with the unknown eavesdropper location, we obtained a secrecy rate that is comparable to a scenario when the eavesdropper's location is known. However, the average secrecy rate with the unknown eavesdropper location varies depending on the proximity of the eavesdropper to the known location of the transmitter. We also observe that due to the UAV-aided jamming, the average secrecy rate stabilizes at some point even though the average received envelope power of the eavesdropper increases. This essentially demonstrates the effectiveness of the proposed scheme.Comment: Submitted to IEEE Access. Contents may be subject to copyright to IEE

Constructive interference based secure precoding:A new dimension in physical layer security

Conventionally, interference and noise are treated as catastrophic elements in wireless communications. However, it has been shown recently that exploiting known interference constructively can even contribute to signal detection ability at the receiving end. This paper exploits this concept to design artificial noise (AN) beamformers constructive to the intended receiver (IR) yet keeping AN disruptive to possible eavesdroppers (Eves). The scenario considered here is a multiple-input single-output (MISO) wiretap channel with multiple eavesdroppers. Both perfect and imperfect channel information have been considered. The main objective is to improve the receive signal-to-interference and noise ratio (SINR) at IR through exploitation of AN power in an attempt to minimize the total transmit power, while confusing the Eves. Numerical simulations demonstrate that the proposed constructive AN precoding approach yields superior performance over conventional AN schemes in terms of transmit power as well as symbol error rate (SER)