A Signal-Space Distance Measure for Nondispersive Optical Fiber

The nondispersive per-sample channel model for the optical fiber channel is considered. Under certain smoothness assumptions, the problem of finding the minimum amount of noise energy that can render two different input points indistinguishable is formulated. This minimum noise energy is then taken as a measure of distance between the points in the input alphabet. Using the machinery of optimal control theory, necessary conditions that describe the minimum-energy noise trajectories are stated as a system of nonlinear differential equations. It is shown how to find the distance between two input points by solving this system of differential equations. The problem of designing signal constellations with the largest minimum distance subject to a peak power constraint is formulated as a clique-finding problem. As an example, a 16-point constellation is designed and compared with conventional quadrature amplitude modulation. A computationally efficient approximation for the proposed distance measure is provided. It is shown how to use this approximation to design large constellations with large minimum distances. Based on the control-theoretic viewpoint of this paper, a new decoding scheme for such nonlinear channels is proposed.

Automated summary

The paper considers the nondispersive per-sample channel model for the optical fiber channel and formulates the problem of finding the minimum noise energy that can render two different input points indistinguishable, using it as a measure of distance. Necessary conditions describing the minimum-energy noise trajectories as a system of nonlinear differential equations are stated and the distance between two input points is found by solving them. The problem of designing signal constellations with the largest minimum distance is formulated as a clique-finding problem, showcasing an example of a 16-point constellation designed and compared with conventional quadrature amplitude modulation.