
arXiv: 1606.04073
Different aspects of probabilistic shaping for a multi-span optical communication system are studied. First, a numerical analysis of the additive white Gaussian noise (AWGN) channel investigates the effect of using a small number of input probability mass functions (PMFs) for a range of signal-to-noise ratios (SNRs), instead of optimizing the constellation shaping for each SNR. It is shown that if a small penalty of at most 0.1 dB SNR to the full shaping gain is acceptable, just two shaped PMFs are required per quadrature amplitude modulation (QAM) over a large SNR range. For a multi-span wavelength division multiplexing (WDM) optical fiber system with 64QAM input, it is shown that just one PMF is required to achieve large gains over uniform input for distances from 1,400 km to 3,000 km. Using recently developed theoretical models that extend the Gaussian noise (GN) model and full-field split-step simulations, we illustrate the ramifications of probabilistic shaping on the effective SNR after fiber propagation. Our results show that, for a fixed average optical launch power, a shaping gain is obtained for the noise contributions from fiber amplifiers and modulation-independent nonlinear interference (NLI), whereas shaping simultaneously causes a penalty as it leads to an increased NLI. However, this nonlinear shaping loss is found to have a relatively minor impact, and optimizing the shaped PMF with a modulation-dependent GN model confirms that the PMF found for AWGN is also a good choice for a multi-span fiber system.
11 pages, 9 figures, 5 tables. Accepted for publication in IEEE/OSA Journal of Lightwave Technology
FOS: Computer and information sciences, bit-wise decoders, nonlinear fiber channel, Computer Science - Information Theory, Information Theory (cs.IT), probabilistic shaping, Gaussian noise models, Achievable information rates, mutual information, wavelength division multiplexing
FOS: Computer and information sciences, bit-wise decoders, nonlinear fiber channel, Computer Science - Information Theory, Information Theory (cs.IT), probabilistic shaping, Gaussian noise models, Achievable information rates, mutual information, wavelength division multiplexing
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