Biblio

This paper investigates the physical layer (PHY-layer) authentication that exploits channel state information (CSI) to enhance multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system security by detecting spoofing attacks in wireless networks. A multi-user authentication system is proposed using convolutional neural networks (CNNs) which also can distinguish spoofers effectively. In addition, the mini batch scheme is used to train the neural networks and accelerate the training speed. Meanwhile, L1 regularization is adopted to prevent over-fitting and improve the authentication accuracy. The convolutional-neural-network-based (CNN-based) approach can authenticate legitimate users and detect attackers by CSIs with higher performances comparing to traditional hypothesis test based methods.

The passive radar also known as Green Radar exploits the available commercial communication signals and is useful for target tracking and detection in general. Recent communications standards frequently employ Orthogonal Frequency Division Multiplexing (OFDM) waveforms and wideband for broadcasting. This paper focuses on the recent developments of the target detection algorithms in the OFDM passive radar framework where its channel estimates have been derived using the matched filter concept using the knowledge of the transmitted signals. The MUSIC algorithm, which has been modified to solve this two dimensional delay-Doppler detection problem, is first reviewed. As the target detection problem can be represented as sparse signals, this paper employs compressive sensing to compare with the detection capability of the 2-D MUSIC algorithm. It is found that the previously proposed single time sample compressive sensing cannot significantly reduce the leakage from the direct signal component. Furthermore, this paper proposes the compressive sensing method utilizing multiple time samples, namely l1-SVD, for the detection of multiple targets. In comparison between the MUSIC and compressive sensing, the results show that l1-SVD can decrease the direct signal leakage but its prerequisite of computational resources remains a major issue. This paper also presents the detection performance of these two algorithms for closely spaced targets.