Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61777
Title: Joint modulation format/bit-rate classification and signal-to-noise ratio estimation in multipath fading channels using deep machine learning
Authors: Khan, FN
Lu, C 
Lau, APT 
Issue Date: 2016
Publisher: Institution of Engineering and Technology
Source: Electronics letters, 2016, v. 52, no. 14, p. 1272-1274 How to cite?
Journal: Electronics letters 
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.
URI: http://hdl.handle.net/10397/61777
ISSN: 0013-5194
EISSN: 1350-911X
DOI: 10.1049/el.2016.0876
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