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Iterative signal reconstruction of deliberately clipped SMT signals

Science China(Information Sciences) 2014年第2期57卷 5-17页

作者： KOLLR Zsolt gazda juraj HORVTH Pter VARGA Lajos KOCUR Duan Budapest University of Technology and Economics Technical University of Koice

Staggered MultiTone(SMT)is a modulation technique showing significantly reduced Adjacent Channel Leakage Ratio(ACLR)resulting in a more compact Power Spectrum Density(PSD)for the transmitted signal,than the well-known and already widely adopted Orthogonal Frequency Division Multiple Access(OFDMA)***,the unique spectral properties of an SMT signal could be degraded by a non-linear element(e.g.a Power Amplifier(PA))in the *** baseband clipping can be applied to the transmitted signal,reducing the notable high Peak-to-Average Power Ratio(PAPR).The objective of this paper is to give a brief introduction to the SMT scheme,with a special emphasis on deliberate clipping efects and their *** paper introduces two receiver-oriented iterative methods aiming at the restoration of the baseband Bit Error Rate(BER)performance of a non-clipped *** methods are evaluated and compared based on numerical *** paper concludes with the selection of a possible candidate for use in systems applying deliberately clipped SMT signals.

关键词： clipping SMT iterative decoding nonlinear distortion PAPR

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Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

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Computers, Materials & Continua 2022年第1期70卷 413-431页

作者： Nazarii Lutsiv Taras Maksymyuk Mykola Beshley Orest Lavriv Volodymyr Andrushchak Anatoliy Sachenko Liberios Vokorokos juraj gazda Lviv Polytechnic National University Lviv79013Ukraine West Ukrainian National University Ternopil46009Ukraine Technical University of Kosice Kosice04200Slovakia

The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new *** the massive number of connected devices,opportunities for potential network attacks are nearly *** additional problem is that many low-cost devices are not equippedwith effective security protection so that they are easily hacked and applied within a network of bots(botnet)to perform distributed denial of service(DDoS)*** this paper,we propose a novel intrusion detection system(IDS)based on deep learning that aims to identify suspicious behavior in modern heterogeneous information *** proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network *** additional feature of the proposed IDS is that it is trained with an optimized dataset,where the number of features is reduced by 94%without classification accuracy ***,the proposed IDS remains stable in response to slight system perturbations,which do not represent network *** proposed approach is evaluated under different simulation scenarios and provides a 99%detection accuracy over known datasets while reducing the training time by an order of magnitude.

关键词： DDoS deep semisupervised learning cybersecurity anomaly detection

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