Joint modulation format/bit‐rate classification and signal‐to‐noise ratio estimation in multipath fading channels using deep machine learning | Zendy

Khan F.N. | Zendy; Lu C. | Zendy; Lau A.P.T. | Zendy

Open Access

Joint 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) - joint (building) , multipath propagation , fading , computer science , bit error rate , signal to noise ratio (imaging) , electronic engineering , modulation (music) , speech recognition , bit (key) , artificial intelligence , fading distribution , noise (video) , channel (broadcasting) , pattern recognition (psychology) , telecommunications , computer network , engineering , acoustics , rayleigh fading , physics , architectural engineering , image (mathematics)

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.

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