Title
Accuracy Assessment of Nondispersive Optical Perturbative Models through Capacity Analysis.
Abstract
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 Schrodinger (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.
Year
DOI
Venue
2019
10.3390/e21080760
ENTROPY
Keywords
Field
DocType
achievable rate,channel capacity,information theory,nonlinear channel,optical fiber
Information theory,Statistical physics,Mathematical optimization,Nonlinear system,Perturbation theory,Upper and lower bounds,Communication channel,Logarithm,Channel capacity,Mathematics,Perturbation theory (quantum mechanics)
Journal
Volume
Issue
ISSN
21
8
Entropy, vol. 21, no. 8, pp. 5532-5543, Aug. 2019
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Keykhosravi, K.132.77
Giuseppe Durisi271454.82
E. Agrell395991.69