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

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

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

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 OSA member, or as an authorized user of your institution.

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