Volterra-Based Reconfigurable Nonlinear Equalizer for Coherent OFDM

Abstract

Elias Giacoumidis, et al, 'Volterra-Based Reconfigurable Nonlinear Equalizer for Coherent OFDM', IEEE Photonics Technology Letters, Vol 26 (14): 1383-1386, June 2014, doi: https://doi.org/10.1109/LPT.2014.2321434. Published by IEEE.A reconfigurable nonlinear equalizer (RNLE) based on inverse Volterra series transfer function is proposed for dual-polarization (DP) and multiband coherent optical orthogonal frequency-division multiplexing (OFDM) signals. It is shown that the RNLE outperforms by 2 dB the linear equalization in a 260-Gb/s DP-OFDM system at 1500 km. The RNLE improves the tolerance to inter/intraband nonlinearities, being independent on polarization tributaries, modulation format, signal bit rate, subcarrier number, and distance

Affinity propagation clustering for blind nonlinearity compensation in coherent optical OFDM

“…Endeavors to surpass the Kerr nonlinearity limit in long-haul coherent communications have been attempted in digital domain by Volterra-based nonlinear equalization (V-NLE) [1] and digital-back propagation (DBP) [2]. V-NLE and DBP however, can only tackle deterministic nonlinearities such as self-phase modulation, without considering the stochastic nonlinear interaction from polarization-mode dispersion and amplified spontaneous emission noise caused by cascaded optical amplifiers.…”

Blind Nonlinearity Equalization by Machine-Learning-Based Clustering for Single- and Multichannel Coherent Optical OFDM

“…The Kerr effect is a nonlinear phenomenon which causes distortion to the propagated optical signal and it is proportional to its power [1,2], resulting in the deceleration of the data transmission. Attempts to combat fiber-induced nonlinearities in SSMF and few-mode fibers have been performed by nonlinearity compensators [3][4][5][6][7] which tackle deterministic nonlinearities. These techniques however, result in modest improvements because the interaction between nonlinearity and random noises in a long-distance network such as from concatenated Erbium-doped fiber amplifiers (EDFAs) (i.e.…”

“…Especially at low transmitted powers, the received data reveal more entropy meaning they have higher randomness due to EDFAs non-deterministic noise. Moreover, all proposed nonlinearity compensators present high complexity [3][4][5][6][7] being impractical for real-time communications. The aforementioned random noises of the network can be partially tackled by low-complex digital machine learning algorithms that perform nonlinear equalization (NLE), such as unsupervised and supervised algorithms: machine learning clustering (MLC) with K-means and Gaussian mixture [8][9][10], and classification machines [11], e.g.…”

“…In [10], the advanced classification supervised Fast-Newton support vector machine (F-SVM) [17] and ANN-NLE [12][13][14], and the reduced complexity Volterra-based NLE using the 3 rd order Kernelbased inverse Volterra-series transfer function (IVSTF)-NLE [5,6]. It is shown that FLC reveals the highest performance at optimum launched optical power (LOP), outperforming both K-means and ANN based NLEs.…”

“…[12][13][14]17] with the exception of implementing the proposed MLC algorithms as new NLEs at the receiver side. Section IV presents the experimental results using MLC (including the benchmark K-means [10]), the traditional machine learning based ANN [12], and the deterministic IVSTF [5,6] in singlechannel QPSK CO-OFDM and WDM 16-QAM CO-OFDM at 3200 and 2000 km, respectively. In this Section, we also investigate the impact of alternative novel advanced clustering designs on 16-QAM CO-OFDM.…”