Calculation of mutual information for nonlinear communication channel at large signal-to-noise ratio

Abstract

Using the path-integral technique we examine the mutual information for the communication channel modelled by the nonlinear Schrödinger equation with additive Gaussian noise. The nonlinear Schrödinger equation is one of the fundamental models in nonlinear physics, and it has a broad range of applications, including fiber optical communications the backbone of the Internet. At large signal-to-noise ratio (SNR) we present the mutual information through the path-integral which is convenient for the perturbative expansion in nonlinearity. In the limit of small noise and small nonlinearity we derive analytically the first nonzero nonlinear correction to the mutual information for the channel.

Next-to-leading-order corrections to capacity for a nondispersive nonlinear optical fiber channel in the intermediate power region

“…The influence of nonlinearity on capacity is investigated both for dispersive and nondispersive optical channels. The channels with dispersion were studied in numerous papers, see, e.g., [2][3][4][5][6][7][8][9][10][11][12] and references therein. Despite the fact that the capacity for the nonlinear channel with dispersion was considered in many papers the exact in nonlinearity result is still not found due to difficulty of the problem.…”

Path Integral Approach to Nondispersive Optical Fiber Communication Channel

“…The calculations performed in Refs. [ 29 , 30 , 31 , 32 , 33 ] is indicative of the possibility to use the presented methods for the capacity investigation of the nonlinear fiber-optical channels with non-zero dispersion.…”

Complex-Valued Neural Network Design for Mitigation of Signal Distortions in Optical Links

“…For the Manakov equation (Eq. (1)), perturbative methods describing the field evolution in the highly nonlinear regime [16][17][18][19] have been developed under some limiting assumptions. Conversely, we suggest adding trainable general nonlinearity functions to the memory-less solution provided by Eq.…”