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01/28/2023
Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
Most of today's communication systems are designed to target reliable me...
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12/07/2022
FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training
We design and implement an adaptive machine learning equalizer that alte...
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11/26/2022
End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
Optical interconnects (OIs) based on vertical-cavity surface-emitting la...
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05/13/2022
Data-Driven Upper Bounds on Channel Capacity
We consider the problem of estimating an upper bound on the capacity of ...
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03/15/2022
Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication
End-to-end autoencoder (AE) learning has the potential of exceeding the ...
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04/16/2021
Autoencoder-Based Unequal Error Protection Codes
We present a novel autoencoder-based approach for designing codes that p...
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11/27/2020
Pruning and Quantizing Neural Belief Propagation Decoders
We consider near maximum-likelihood (ML) decoding of short linear block ...
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11/04/2020
Learned Decimation for Neural Belief Propagation Decoders
We introduce a two-stage decimation process to improve the performance o...
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10/27/2020
Physics-Based Deep Learning for Fiber-Optic Communication Systems
We propose a new machine-learning approach for fiber-optic communication...
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01/25/2020
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
We propose a model-based machine-learning approach for polarization-mult...
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01/21/2020
Pruning Neural Belief Propagation Decoders
We consider near maximum-likelihood (ML) decoding of short linear block ...
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12/11/2019
End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information
GMI-based end-to-end learning is shown to be highly nonconvex. We apply ...
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06/11/2019
Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding
In this paper, we use reinforcement learning to find effective decoding ...
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04/22/2019
Revisiting Multi-Step Nonlinearity Compensation with Machine Learning
For the efficient compensation of fiber nonlinearity, one of the guiding...
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01/24/2019
Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
We consider the weighted belief-propagation (WBP) decoder recently propo...
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01/22/2019
What Can Machine Learning Teach Us about Communications?
Rapid improvements in machine learning over the past decade are beginnin...
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07/04/2018
Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep Learning
We propose a low-complexity sub-banded DSP architecture for digital back...
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06/28/2018
On Low-Complexity Decoding of Product Codes for High-Throughput Fiber-Optic Systems
We study low-complexity iterative decoding algorithms for product codes....
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06/19/2018
ASIC Implementation of Time-Domain Digital Backpropagation with Deep-Learned Chromatic Dispersion Filters
We consider time-domain digital backpropagation with chromatic dispersio...
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04/27/2018
Decoding Reed-Muller Codes Using Minimum-Weight Parity Checks
Reed-Muller (RM) codes exhibit good performance under maximum-likelihood...
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04/20/2018
Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Machine learning is used to compute achievable information rates (AIRs) ...
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04/09/2018
Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communications
An important problem in fiber-optic communications is to invert the nonl...
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10/17/2017
