Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Journal of Lightwave Technology
  • Vol. 36,
  • Issue 17,
  • pp. 3564-3572
  • (2018)

Non-Data-Aided k-Nearest Neighbors Technique for Optical Fiber Nonlinearity Mitigation

Not Accessible

Your library or personal account may give you access

Abstract

We experimentally demonstrate a low-complexity and zero-redundancy fiber nonlinearity mitigation technique in the 16-QAM and 64-QAM coherent optical communication systems based on the non-data-aided (ND) k-nearest neighbors (KNN) algorithm. We measured the bit error rate (BER) performances of the 16-QAM and 64-QAM signals in the back-to-back case and in the single-mode-fiber transmission case and achieved notable BER improvement by the ND-KNN technique. The ND-KNN technique enables us to compensate any nondeterministic transmission impairments and does not require any extra training data, which is powerful to mitigate the fiber nonlinearity impairments in the 16-QAM and 64-QAM coherent optical communication systems. By utilizing the proposed ND-KNN method, we achieved 0.5-dB BER improvement in the 800-km single-mode fiber (SMF) 16-QAM transmission system and approximately 2-dB BER improvement in the 80-km SMF 64-QAM transmission system. The algorithm first utilizes the density parameter of the testing data to extract rapidly the center noiseless data and label them as the classification references and then applies the KNN method to classify the remaining testing data. Therefore, the algorithm is robust to the system noise and can achieve fast convergence. The proposed ND-KNN equalization technique can provide efficient compensation at a low computation cost and zero data redundancy and is promising for real-world application.

© 2018 IEEE

PDF Article
More Like This
K-means-clustering-based fiber nonlinearity equalization techniques for 64-QAM coherent optical communication system

Junfeng Zhang, Wei Chen, Mingyi Gao, and Gangxiang Shen
Opt. Express 25(22) 27570-27580 (2017)

Training strategies to minimize interchannel interference effects using supervised learning in gridless Nyquist-WDM systems

Alejandro Escobar Pérez, David Zabala-Blanco, Cesar A. Azurdia Meza, Neil Guerrero González, and Jhon J. Granada Torres
Appl. Opt. 60(28) 8939-8948 (2021)

Bit-based support vector machine nonlinear detector for millimeter-wave radio-over-fiber mobile fronthaul systems

Yue Cui, Min Zhang, Danshi Wang, Siming Liu, Ze Li, and Gee-Kung Chang
Opt. Express 25(21) 26186-26197 (2017)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.