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Found 9 papers, 1 papers with code
CNN-Based Equalization for Communications: Achieving Gigabit Throughput with a Flexible FPGA Hardware Architecture
Thus, in this work, we present a high-performance FPGA implementation of an ANN-based equalizer, which meets the throughput requirements of modern optical communication systems.
Real-Time FPGA Demonstrator of ANN-Based Equalization for Optical Communications
In this work, we present a high-throughput field programmable gate array (FPGA) demonstrator of an artificial neural network (ANN)-based equalizer.
Fully-blind Neural Network Based Equalization for Severe Nonlinear Distortions in 112 Gbit/s Passive Optical Networks
We demonstrate and evaluate a fully-blind digital signal processing (DSP) chain for 100G passive optical networks (PONs), and analyze different equalizer topologies based on neural networks with low hardware complexity.
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
We propose a novel frequency-domain blind equalization scheme for coherent optical communications.
Unsupervised ANN-Based Equalizer and Its Trainable FPGA Implementation
In recent years, communication engineers put strong emphasis on artificial neural network (ANN)-based algorithms with the aim of increasing the flexibility and autonomy of the system and its components.
Blind Channel Equalization Using Vector-Quantized Variational Autoencoders
Furthermore, we show that for the linear channel, the proposed scheme exhibits better convergence properties than the \ac{MMSE}-based, the \ac{CMA}-based, and the \ac{VAE}-based equalizers in terms of both convergence speed and robustness to variations in training batch size and learning rate.
Improving the Bootstrap of Blind Equalizers with Variational Autoencoders
We evaluate the start-up of blind equalizers at critical working points, analyze the advantages and obstacles of commonly-used algorithms, and demonstrate how the recently-proposed variational autoencoder (VAE) based equalizers can improve bootstrapping.
Blind and Channel-agnostic Equalization Using Adversarial Networks
The proposed approach is independent of the equalizer topology and enables the application of powerful neural network based equalizers.
Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders
We investigate the potential of adaptive blind equalizers based on variational inference for carrier recovery in optical communications.
