Histogram Based Clustering for Nonlinear Compensation in Long Reach Coherent Passive Optical Networks

• Autor(a) principal: Aldaya, Ivan [UNESP]
• Data de Publicação: 2020
• Outros Autores: Giacoumidis, Elias, Oliveira, Geraldo de [UNESP], Wei, Jinlong, Pita, Julian Leonel, Marconi, Jorge Diego, Mello Fagotto, Eric Alberto, Barry, Liam, Francisco Abbade, Marcelo Luis [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/app10010152; http://hdl.handle.net/11449/195161
• topic: passive optical networks; nonlinear compensation; clustering

In order to meet the increasing capacity requirements, network operators are extending their optical infrastructure closer to the end-user while making more efficient use of the resources. In this context, long reach passive optical networks (LR-PONs) are attracting increasing attention.Coherent LR-PONs based on high speed digital signal processors represent a high potential alternative because, alongside with the inherent mixing gain and the possibility of amplitude and phase diversity formats, they pave the way to compensate linear impairments in a more efficient way than in traditional direct detection systems. The performance of coherent LR-PONs is then limited by the combined effect of noise and nonlinear distortion. The noise is particularly critical in single channel systems where, in addition to the the elevated fibre loss, the splitting losses should be considered. In such systems, Kerr induced self-phase modulation emerges as the main limitation to the maximum capacity. In this work, we propose a novel clustering algorithm, denominated histogram based clustering (HBC), that employs the spatial density of the points of a 2D histogram to identify the borders of high density areas to classify nonlinearly distorted noisy constellations. Simulation results reveal that for a 100 km long LR-PON with a 1:64 splitting ratio, at optimum power levels, HBC presents a Q-factor 0.57 dB higher than maximum likelihood and 0.21 dB higher than k-means. In terms of nonlinear tolerance, at a BER of 2x10(-3), our method achieves a gain of similar to 2.5 dB and similar to 1.25 dB over maximum likelihood and k-means, respectively. Numerical results also show that the proposed method can operate over blocks as small as 2500 symbols.