Accuracy Assessment of Nondispersive Optical Perturbative Models through Capacity Analysis
Journal article, 2019

A number of simplified models, based on perturbation theory, have been proposed for the fiber-optical channel and have been extensively used in the literature. Although these models are mainly developed for the low-power regime, they are used at moderate or high powers as well. It remains unclear to what extent the capacity of these models is affected by the simplifying assumptions under which they are derived. In this paper, we consider single-channel data transmission based on three continuous-time optical models: (i) a regular perturbative channel, (ii) a logarithmic perturbative channel, and (iii) the stochastic nonlinear Schrödinger (NLS) channel. To obtain analytically tractable discrete-time models, we consider zero-dispersion fibers and a sampling receiver. We investigate the per-sample capacity of these models. Specifically, (i) we establish tight bounds on the capacity of the regular perturbative channel; (ii) we obtain the capacity of the logarithmic perturbative channel; and (iii) we present a novel upper bound on the capacity of the zero-dispersion NLS channel. Our results illustrate that the capacity of these models departs from each other at high powers because these models yield different capacity pre-logs. Since all three models are based on the same physical channel, our results highlight that care must be exercised in using simplified channel models in the high-power regime.

optical fiber

information theory

nonlinear channel

achievable rate

channel capacity

Author

Kamran Keykhosravi

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Giuseppe Durisi

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Erik Agrell

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Entropy

1099-4300 (ISSN)

Vol. 21 8 760

Optical Fiber Interference is Not Noise

Swedish Research Council (VR), 2014-01-01 -- 2017-12-31.

Subject Categories

Communication Systems

Probability Theory and Statistics

Computer Science

DOI

10.3390/e21080760

More information

Latest update

11/7/2019