Comparison of statistical and machine learning techniques for physical layer authentication

L Senigagliesi, M Baldi, E Gambi - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In this article we consider authentication at the physical layer, in which the authenticator
aims at distinguishing a legitimate supplicant from an attacker on the basis of the …

Impact of mobility on physical layer security over wireless fading channels

J Tang, M Dabaghchian, K Zeng… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Wireless physical layer security has attracted great attention in recent years. Although
mobility is an intrinsic property of wireless networks, most of the existing works only consider …

[图书][B] Wireless communications: algorithmic techniques

GA Vitetta, DP Taylor, G Colavolpe, F Pancaldi… - 2013 - books.google.com
This book introduces the theoretical elements at the basis of various classes of algorithms
commonly employed in the physical layer (and, in part, in MAC layer) of wireless …

SNR estimation for multilevel constellations using higher-order moments

M Alvarez-Diaz, R López-Valcarce… - IEEE Transactions on …, 2009 - ieeexplore.ieee.org
The performance of existing moments-based non-data-aided (NDA) estimators of signal-to-
noise ratio (SNR) in digital communication systems substantially degrades with multilevel …

Specguard: Spectrum misuse detection in dynamic spectrum access systems

X Jin, J Sun, R Zhang, Y Zhang… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Dynamic spectrum access (DSA) is the key to solving worldwide spectrum shortage. The
open wireless medium subjects DSA systems to unauthorized spectrum use by illegitimate …

Deep learning based prediction of signal-to-noise ratio (SNR) for LTE and 5G systems

T Ngo, B Kelley, P Rad - 2020 8th International Conference on …, 2020 - ieeexplore.ieee.org
Deep learning (DL) is applied to predict signal-to-noise ratio (SNR) in de facto LTE and 5G
systems in a non-data-aided (NDA) manner. Various channel conditions and impairments …

Machine learning model for adaptive modulation of multi-stream in MIMO-OFDM system

CB Ha, YH You, HK Song - IEEE Access, 2018 - ieeexplore.ieee.org
In this paper, we propose the adaptive modulation (AM) model based on machine learning
for a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing …

[PDF][PDF] Signal classification in fading channels using cyclic spectral analysis

E Like, VD Chakravarthy, P Ratazzi, Z Wu - EURASIP Journal on Wireless …, 2009 - Springer
Cognitive Radio (CR), a hierarchical Dynamic Spectrum Access (DSA) model, has been
considered as a strong candidate for future communication systems improving spectrum …

Positioning and location-based beamforming for high speed trains in 5G NR networks

J Talvitie, T Levanen, M Koivisto… - 2018 IEEE …, 2018 - ieeexplore.ieee.org
High-accuracy positioning enables emerging of new vertical markets for the forthcoming fifth
generation (5G) mobile networks. In this paper, we study network-side positioning and …

Block-sparsity regularized maximum correntropy criterion for structured-sparse system identification

T Tian, FY Wu, K Yang - Journal of the Franklin Institute, 2020 - Elsevier
This work deals with the block-sparse system identification problem on the basis of the
maximum correntropy criterion (MCC). The MCC is known for its robustness against non …