Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61897
Title: Modulation format identification in coherent receivers using deep machine learning
Authors: Khan, FN
Zhong, K
Al-Arashi, WH
Yu, C 
Lu, C 
Lau, APT
Keywords: Coherent detection
Deep machine learning
Modulation format identification
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE photonics technology letters, 2016, v. 28, no. 17, 7482803, p. 1886-1889 How to cite?
Journal: IEEE photonics technology letters 
Abstract: We propose a novel technique for modulation format identification (MFI) in digital coherent receivers by applying deep neural network (DNN) based pattern recognition on signals' amplitude histograms obtained after constant modulus algorithm (CMA) equalization. Experimental results for three commonly-used modulation formats demonstrate MFI with an accuracy of 100% over a wide optical signal-to-noise ratio (OSNR) range. The effects of fiber nonlinearity on the performance of MFI technique are also investigated. The proposed technique is non-data-aided (NDA) and avoids any additional hardware on top of standard digital coherent receiver. Therefore, it is ideal for simple and cost-effective MFI in future heterogeneous optical networks.
URI: http://hdl.handle.net/10397/61897
ISSN: 1041-1135
EISSN: 1941-0174
DOI: 10.1109/LPT.2016.2574800
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