Modified failproof physics-informed neural network framework for fast and accurate optical fiber transmission link modeling.

Physics-informed neural networks (PINNs) have recently emerged as an important and ground-breaking technique in scientific machine learning for numerous applications including in optical fiber communications. However, the vanilla/baseline version of PINNs is prone to fail under certain conditions because of the nature of the physics-based regularization term in its loss function. The use of this unique regularization technique results in a highly complex non-convex loss landscape when visualized.

Achievable information rate optimization in C-band optical fiber communication system.

Optical fiber communication networks play an important role in the global telecommunication network. However, nonlinear effects in the optical fiber and transceiver noise greatly limit the performance of fiber communication systems. In this paper, the product of mutual information (MI) and communication bandwidth is used as the metric of the achievable information rate (AIR).