We investigate the performance of a machine learning classification technique, called the Parzen window, to mitigate the fiber nonlinearity in the context of dispersion managed and dispersion unmanaged systems. The technique is applied for detection at the receiver side, and deals with the non-Gaussian nonlinear effects by designing improved decision boundaries. We also propose a two-stage mitigation technique using digital back propagation and Parzen window for dispersion unmanaged systems. In this case, digital back propagation compensates for the deterministic nonlinearity and the Parzen window deals with the stochastic nonlinear signal-noise interactions, which are not taken into account by digital back propagation. A performance improvement up to 0.4 dB in terms of Q factor is observed.Index Terms-digital back propagation, fiber nonlinearity mitigation, machine learning, optical communication systems, Parzen window.

This study comprehensively investigated the impact of indoor carbon dioxide (CO2) concentration on sleep quality. Three experimental conditions (800, 1900, 3000 ppm) were created in chambers decorated as bedroom and other environmental parameters that may influence the sleep quality were under strict control. Sleep quality of 12 subjects (6 men and 6 women) was monitored for 54 consecutive days through sleep quality questionnaire and physiological measures. Both subjective and physiological results showed that sleep quality decreased significantly with the increase of CO2 concentration, and the comprehensive questionnaire score at 3000 ppm was only 80.8% of that at 800 ppm. A linear positive correlation was found between sleep onset latency (SOL) and CO2 concentration, while a linear negative correlation occurred between slow‐wave sleep (SWS) and CO2 concentration. In addition, in the same sleep environment, men had higher subjective questionnaire scores after wake‐up, longer SWS and shorter SOL, which lead to a better sleep quality compared with women, and there was a significant gender difference in sleep quality at 800 ppm (P < .05).

Abstract-In this letter, we propose a modulation classification algorithm which is based on the received signal's amplitude for coherent optical receivers. The proposed algorithm classifies the modulation format from several possible candidates by differentiating the cumulative distribution function (CDF) curves of their normalized amplitudes. The candidate with the most similar CDF to the received signal is selected. The measure of similarity is the minimum average distance between these CDFs. Five commonly used quadrature amplitude modulation formats in digital coherent optical systems are employed. Optical backto-back experiments and extended simulations are carried out to investigate the performance of the proposed algorithm. Results show that the proposed algorithm achieves accurate classification at optical signal-to-noise ratios of interest. Furthermore, it does not require carrier recovery.

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