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Found 7 papers, 0 papers with code
Efficient Training of Volterra Series-Based Pre-distortion Filter Using Neural Networks
We present a simple, efficient "direct learning" approach to train Volterra series-based digital pre-distortion filters using neural networks.
End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping
We present a novel autoencoder-based learning of joint geometric and probabilistic constellation shaping for coded-modulation systems.
On the Comparison of Single-Carrier vs. Digital Multi-Carrier Signaling for Long-Haul Transmission of Probabilistically Shaped Constellation Formats
We report on theoretical and experimental investigations of the nonlinear tolerance of single carrier and digital multicarrier approaches with probabilistically shaped constellations.
Experimental Investigation of Deep Learning for Digital Signal Processing in Short Reach Optical Fiber Communications
The investigation of digital signal processing (DSP) optimized on experimental data is extended to pulse amplitude modulation with receivers performing sliding window sequence estimation using a feed-forward or a recurrent neural network as well as classical nonlinear Volterra equalization.
Optical Fiber Communication Systems Based on End-to-End Deep Learning
We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning.
Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model
We perform an experimental end-to-end transceiver optimization via deep learning using a generative adversarial network to approximate the test-bed channel.
End-to-end Deep Learning of Optical Fiber Communications
In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver.
