PMID- 34613126
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20211007
IS  - 1539-4522 (Electronic)
IS  - 1559-128X (Linking)
VI  - 60
IP  - 28
DP  - 2021 Oct 1
TI  - Training strategies to minimize interchannel interference effects using 
      supervised learning in gridless Nyquist-WDM systems.
PG  - 8939-8948
LID - 10.1364/AO.428856 [doi]
AB  - The dynamism envisioned in future high-capacity gridless optical networks 
      requires facing several challenges in distortion mitigation, such as the 
      mitigation of interchannel interference (ICI) effects in any optical channel 
      without information of their adjacent channels. Machine learning (ML)-based 
      techniques have been proposed in recent works to estimate and mitigate different 
      optical impairments with promising results. We propose and evaluate two training 
      strategies for supervised learning algorithms with the aim to minimize ICI 
      effects in a gridless 3x16-Gbaud 16-quadrature amplitude modulation (QAM) 
      Nyquist-wavelength-division multiplexing (WDM) system. One strategy, called 
      updating strategy, is based on symbol training sequence, and the other one, 
      called characterization strategy, is based on an offline training using a 
      previous system characterization. Artificial neural networks (ANN), support 
      vector machine (SVM), K-nearest neighbors (KNN), and extreme learning machine 
      (ELM) algorithms are explored for both training strategies. Experimental results 
      showed a bit error rate (BER) improvement at low training lengths for both 
      training strategies, for instance, gains up to  approximately 4dB in terms of optical 
      signal-to-noise ratio were achieved in a back-to-back scenario. Besides, the KNN 
      and ELM algorithms showed significant BER reduction in transmission over 250 km 
      optical fiber. Additionally, we carried out a brief computational complexity 
      analysis where ELM presented only 1.9% of ANN processing time. Hence, the use of 
      ML-based techniques could enhance the optical gridless networks performance and 
      consequently fulfill future traffic demands.
FAU - Escobar Perez, Alejandro
AU  - Escobar Perez A
FAU - Zabala-Blanco, David
AU  - Zabala-Blanco D
FAU - Azurdia Meza, Cesar A
AU  - Azurdia Meza CA
FAU - Guerrero Gonzalez, Neil
AU  - Guerrero Gonzalez N
FAU - Granada Torres, Jhon J
AU  - Granada Torres JJ
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Appl Opt
JT  - Applied optics
JID - 0247660
SB  - IM
EDAT- 2021/10/07 06:00
MHDA- 2021/10/07 06:01
CRDT- 2021/10/06 12:25
PHST- 2021/10/06 12:25 [entrez]
PHST- 2021/10/07 06:00 [pubmed]
PHST- 2021/10/07 06:01 [medline]
AID - 460048 [pii]
AID - 10.1364/AO.428856 [doi]
PST - ppublish
SO  - Appl Opt. 2021 Oct 1;60(28):8939-8948. doi: 10.1364/AO.428856.