Gradient-free training of autoencoders for non-differentiable communication channels

Abstract

Training of autoencoders using the back-propagation algorithm is challenging for non-differential channel models or in an experimental environment where gradients cannot be computed. In this paper, we study a gradient-free training method based on the cubature Kalman filter. To numerically validate the method, the autoencoder is employed to perform geometric constellation shaping on differentiable communication channels, showing the same performance as the back-propagation algorithm. Further investigation is done on a non-differentiable communication channel that includes: laser phase noise, additive white Gaussian noise and blind phase search-based phase noise compensation. Our results indicate that the autoencoder can be successfully optimized using the proposed training method to achieve better robustness to residual phase noise with respect to standard constellation schemes such as Quadrature Amplitude Modulation and Iterative Polar Modulation for the considered conditions.

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• Constellation Shaping for WDM systems using 256QAM/1024QAM with Probabilistic Optimization
• End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks
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Cited by in corpus (6)

• Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation
• End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
• Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning
• End-to-end Learning of a Constellation Shape Robust to Channel Condition Uncertainties
• End-to-end Optimization of Constellation Shaping for Wiener Phase Noise Channels with a Differentiable Blind Phase Search
• Geometric Constellation Shaping for Phase-noise Channels Using a Differentiable Blind Phase Search