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

F. N. Khan, Chao Lu, Pak Tao Lau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)

Abstract

A novel algorithm for simultaneous modulation format/bit-rate classi-fication 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 delaytap 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.
Original languageEnglish
Pages (from-to)1272-1274
Number of pages3
JournalElectronics Letters
Volume52
Issue number14
DOIs
Publication statusPublished - 7 Jul 2016

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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