6 papers
Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
We present the first neural probabilistic amplitude shaping that outperforms existing methods while accounting for all implementation losses, using a block-less, easily implementab…
Neural Probabilistic Amplitude Shaping for Nonlinear Fiber Channels
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
We introduce neural probabilistic amplitude shaping, a joint-distribution learning framework for coherent fiber systems. The proposed scheme provides a 0.5 dB signal-to-noise ratio…
Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
We present an autoregressive end-to-end learning approach for probabilistic shaping on nonlinear fiber channels. Our proposed scheme learns the joint symbol distribution and provid…
Probabilistic Shaping for Nonlinearity Tolerance
Mohammad Taha Askari, Lutz Lampe
Optimizing the input probability distribution of a discrete-time channel is a standard step in the information-theoretic analysis of digital communication systems. Nevertheless, ma…
Perturbation-based Sequence Selection for Probabilistic Amplitude Shaping
Mohammad Taha Askari, Lutz Lampe
We introduce a practical sign-dependent sequence selection metric for probabilistic amplitude shaping and propose a simple method to predict the gains in signal-to-noise ratio (SNR…
Bayesian Phase Search for Probabilistic Amplitude Shaping
Mohammad Taha Askari, Lutz Lampe
We introduce a Bayesian carrier phase recovery (CPR) algorithm which is robust against low signal-to-noise ratio scenarios. It is therefore effective for phase recovery for probabi…