Open AccessJoint modulation format/bit‐rate classification and signal‐to‐noise ratio estimation in multipath fading channels using deep machine learning
Author(s) -
Khan F.N.,
Lu C.,
Lau A.P.T.
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.0876
Subject(s) - fading , computer science , multipath propagation , bit error rate , asynchronous communication , signal to noise ratio (imaging) , modulation (music) , joint (building) , channel (broadcasting) , artificial intelligence , noise (video) , electronic engineering , signal (programming language) , speech recognition , algorithm , telecommunications , engineering , architectural engineering , programming language , philosophy , image (mathematics) , aesthetics
A novel algorithm for simultaneous modulation format/bit‐rate classification and non‐data‐aided (NDA) signal‐to‐noise ratio (SNR) estimation in multipath fading channels by applying deep machine learning‐based pattern recognition on signals’ asynchronous delay‐tap plots (ADTPs) is proposed. The results for three widely‐used modulation formats at two different bit‐rates demonstrate classification accuracy of 99.8%. In addition, NDA SNR estimation over a wide range of 0−30 dB is shown with mean error of 1 dB. The proposed method requires low‐speed, asynchronous sampling of signal and is thus ideal for low‐cost multiparameter estimation under real‐world channel conditions.