Blind nonlinearity equalization by machine-learning-based clustering for QAM-based quantum noise stream cipher transmission

In quantum noise stream cipher (QNSC) systems, it is difficult to compensate fiber nonlinearity by digital signal processing (DSP) due to interactions between chromatic dispersion (CD), amplified spontaneous emission (ASE) noise from erbium-doped fiber amplifier (EDFA) and Kerr nonlinearity. Nonlinearity equalizer (NLE) based on machine learning (ML) algorithms have been extensively studied. However, most NLE based on supervised ML algorithms have high training overhead and computation complexity. In addition, the performance of these algorithms have a lot of randomness. This paper proposes two clustering algorithms based on Fuzzy-logic C-Means Clustering (FLC) to compensate the fiber nonlinearity in quadrature amplitude modulation (QAM)-based QNSC system, including FLC based on subtractive clustering (SC) and annealing evolution (AE) algorithm. The performance of FLC-SC and FLC-AE are evaluated through simulation and experiment. The proposed algorithms can promptly obtain suitable initial centroids and choose optimal initial centroids of the clusters to achieve the global optimal initial centroids especially for high order modulation scheme. In the simulation, different parameter configurations are considered, including fiber length, optical signal-to-noise ratio (OSNR), clipping ratio and resolution of digital to analog converter (DAC). Furthermore, we measure the Q-factor of transmission signal with different launched powers, DAC resolution and laser linewidth in the optical back-to-back (BTB) experiment with 80-km single mode fiber. Both simulation and experimental results show that the proposed techniques can greatly mitigate the signal impairments.