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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders
A new approach for blind channel equalization and decoding, variational inference, and variational autoencoders (VAEs) in particular, is introduced. We first consider the reconstruction of uncoded data symbols transmitte…
Variational InferenceBlind Channel Equalization Using Vector-Quantized Variational Autoencoders
State-of-the-art high-spectral-efficiency communication systems employ high-order modulation formats coupled with high symbol rates to accommodate the ever-growing demand for data rate-hungry applications. However, such …
Blind and Channel-agnostic Equalization Using Adversarial Networks
Due to the rapid development of autonomous driving, the Internet of Things and streaming services, modern communication systems have to cope with varying channel conditions and a steadily rising number of users and devic…
Autonomous DrivingModel Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers
Multi-input multi-output orthogonal frequency division multiplexing (MIMO OFDM) is a key technology for mobile communication systems. However, due to the issue of high peak-to-average power ratio (PAPR), the OFDM symbols…
Achieving High Throughput with a Trainable Neural-Network-Based Equalizer for Communications on FPGA
The ever-increasing data rates of modern communication systems lead to severe distortions of the communication signal, imposing great challenges to state-of-the-art signal processing algorithms. In this context, neural n…
GPU