کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4956990 | 1444925 | 2017 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Mitigation of time-varying distortions in Nyquist-WDM systems using machine learning
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
We propose a machine learning-based nonsymmetrical demodulation technique relying on clustering to mitigate time-varying distortions derived from several impairments such as IQ imbalance, bias drift, phase noise and interchannel interference. Experimental results show that those impairments cause centroid movements in the received constellations seen in time-windows of 10k symbols in controlled scenarios. In our demodulation technique, the k-means algorithm iteratively identifies the cluster centroids in the constellation of the received symbols in short time windows by means of the optimization of decision thresholds for a minimum BER. We experimentally verified the effectiveness of this computationally efficient technique in multicarrier 16QAM Nyquist-WDM systems over 270Â km links. Our nonsymmetrical demodulation technique outperforms the conventional QAM demodulation technique, reducing the OSNR requirement up to â¼0.8Â dB at a BER of 1Â ÃÂ 10â2 for signals affected by interchannel interference.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Optical Fiber Technology - Volume 38, November 2017, Pages 130-135
Journal: Optical Fiber Technology - Volume 38, November 2017, Pages 130-135
نویسندگان
Jhon J. Granada Torres, Siddharth Varughese, Varghese A. Thomas, Andrea Chiuchiarelli, Stephen E. Ralph, Ana M. Cárdenas Soto, Neil Guerrero González,