Neural network (NN)-based nonlinear equalizers have been shown effective for various
types of short-reach direct detection systems. However, they work best for a certain channel …

We investigate the complexity and performance of recurrent neural network (RNN) models
as post-processing units for the compensation of fibre nonlinearities in digital coherent …

For the first time, recurrent and feedforward neural network-based equalizers for nonlinearity
compensation are implemented in an FPGA, with a level of complexity comparable to that of …

The demand for high speed data transmission has increased rapidly over the past few years,
leading to the development of the data center concept. Considering that vertical cavity …

In this paper, we propose a scheme that utilizes the optimization ability of artificial
intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical …

As a promising neuromorphic framework, the optical neural network (ONN) demonstrates
ultrahigh inference speed with low energy consumption. However, the previous ONN …

Neural networks (NNs) inspired by the forward-backward algorithm (FBA) are used as
equalizers for bandlimited channels with a memoryless nonlinearity. The NN-equalizers are …

The demand for high speed data transmission has increased rapidly, leading to advanced
optical communication techniques. In the past few years, multiple equalizers based on …

We use a neural network trained jointly by multi-task learning on datasets acquired at
multiple wavelengths to mitigate the impact of chromatic dispersion in 4× 200Gb/s CWDM4 …

Volterra nonlinear equalizer (VNLE) is widely investigated for linear and nonlinear
distortions compensation in optical communication systems. Despite the powerful …