Abstract
The accurate estimation of mutual information (MI) plays a vital role in understanding channel capacity and optimizing the performance of optical communications. While MI computations for the additive white Gaussian noise (AWGN) channel are well-established, they fall short when dealing with the challenges posed by nonlinear optical fiber channels due to an unknown channel model. For the first time, to our knowledge, this Letter introduces a mutual information neural estimator (MINE) for MI estimation in optical fiber communications. We propose an enhanced MINE (E-MINE), achieved by enlarging the training batch size to improve estimation accuracy and stability. Our findings reveal that the E-MINE achieves highly accurate estimations in the AWGN channel and maintains strong consistency with symbol-by-symbol MI estimations, comparable to Monte Carlo (MC) methods based on a Gaussian distribution in long-haul optical fiber channels. Furthermore, with multi-symbol estimation, the E-MINE yields a 0.16 bits/4D-symbol improvement in our experiments. We anticipate that our findings will drive further research in the field, opening new possibilities for enhancing communication systems design and performance using deep learning techniques.
Published Version
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