A Non-Data-Aided OSNR Estimation Algorithm for Coherent Optical Fiber Communication Systems Employing Multilevel Constellations

The performance of existing moments-based non-data-aided (NDA) optical signal-to-noise ratio (OSNR) estimation approaches degrades greatly for coherent optical systems employing multilevel constellations. We propose a novel NDA OSNR estimation algorithm, which provides enhanced performance for such...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of lightwave technology Ročník 37; číslo 15; s. 3815 - 3825
Hlavní autori: Xiang Lin, Dobre, Octavia A., Ngatched, Telex M. N., Cheng Li
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
ISSN:0733-8724, 1558-2213
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The performance of existing moments-based non-data-aided (NDA) optical signal-to-noise ratio (OSNR) estimation approaches degrades greatly for coherent optical systems employing multilevel constellations. We propose a novel NDA OSNR estimation algorithm, which provides enhanced performance for such systems. The proposed algorithm utilizes the empirical cumulative distribution function of the signal's amplitude to extract the information on the noise variance. Analytical and extensive simulation results show the feasibility and advantages of the algorithm. For the studied systems employing multilevel constellations such as 8-quadrature amplitude modulation (QAM), 16-QAM, 32-QAM, and 64-QAM, the proposed algorithm attains the derived Cramér-Rao lower bound. Furthermore, it achieves a lower mean square error with significantly lower complexity when compared to the conventional moments-based NDA estimation approach. Moreover, the impact of fiber nonlinearity is investigated with a five-channel Nyquist wavelength division multiplexing system, and the proposed algorithm outperforms the moments-based counterpart.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0733-8724
1558-2213
DOI:10.1109/JLT.2019.2921305