Most of today's communication systems are designed to target reliable me...
We design and implement an adaptive machine learning equalizer that
alte...
Optical interconnects (OIs) based on vertical-cavity surface-emitting la...
We consider the problem of estimating an upper bound on the capacity of ...
End-to-end autoencoder (AE) learning has the potential of exceeding the
...
We present a novel autoencoder-based approach for designing codes that
p...
We consider near maximum-likelihood (ML) decoding of short linear block
...
We introduce a two-stage decimation process to improve the performance o...
We propose a new machine-learning approach for fiber-optic communication...
We propose a model-based machine-learning approach for
polarization-mult...
We consider near maximum-likelihood (ML) decoding of short linear block ...
GMI-based end-to-end learning is shown to be highly nonconvex. We apply
...
In this paper, we use reinforcement learning to find effective decoding
...
For the efficient compensation of fiber nonlinearity, one of the guiding...
We consider the weighted belief-propagation (WBP) decoder recently propo...
Rapid improvements in machine learning over the past decade are beginnin...
We propose a low-complexity sub-banded DSP architecture for digital
back...
We study low-complexity iterative decoding algorithms for product codes....
We consider time-domain digital backpropagation with chromatic dispersio...
Reed-Muller (RM) codes exhibit good performance under maximum-likelihood...
Machine learning is used to compute achievable information rates (AIRs) ...
An important problem in fiber-optic communications is to invert the nonl...
A neural-network-based approach is presented to efficiently implement di...