We discuss various applications of machine learning techniques in different aspects of optical communications and networking including optical performance monitoring, fiber nonlinearity compensation, cognitive network failure prediction, dynamic planning and cross-layer optimization of software-defined networks, quality of transmission estimation, and physical layer design of optical communication systems. Recent works employing deep learning technologies are also discussed.
KEYWORDS: Orthogonal frequency division multiplexing, Polarization, Signal detection, Digital signal processing, Photodetectors, Modulation, Single sideband modulation, Modulators, Receivers, Optical engineering
Polarization-interleave-multiplexed (PIM) with single-sideband orthogonal frequency-division multiplexing (SSB-OFDM) based on direct detection is proposed for short-reach applications transmitted up to 80 km in which the guard band can be shared for the two SSB signals with interleave electrical center frequencies. Based on two dual-drive Mach–Zehnder modulators with one single-end photodetector (PD), 100-Gb/s PIM-SSB-OFDM transmission over a 80-km standard single-mode fiber is successfully demonstrated. After 80-km transmission, the optical signal-to-noise ratio requirement is 29.1 dB with respect to the bit error rate threshold of 7% hard decision-forward error correction overhead.
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