Pinned Repositories
996.ICU
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
astyx
An API to access the Astyx Hires dataset
AutoEncoder-Based-Communication-System
Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
autoencoder_for_physical_layer
This is my attempt to reproduce and extend the results in the paper "An Introduction to Deep Learning for the Physical Layer" by Tim O'Shea and Jakob Hoydis
Channel-Equalization-in-FBMC
Allows to reproduce all figures from "FBMC-OQAM in Doubly-Selective Channels: A New Perspective on MMSE Equalization", IEEE SPAWC, 2017.
Deep-Learning-for-the-Physical-Layer
PyTorch implementation for part of paper "An Introduction to Deep Learning for the Physical Layer" by Kenta Iwasaki on behalf of Gram.AI.
Deep-MIMO-Detection
Deep-Reinforcement-Learning-Hands-On-Second-Edition
Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt
DeepMIMODetection
DeepRL
Deep Reinforcement Learning Lab, a platform designed to make DRL technology and fun for everyone
fafa-ccnu's Repositories
fafa-ccnu/996.ICU
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
fafa-ccnu/astyx
An API to access the Astyx Hires dataset
fafa-ccnu/AutoEncoder-Based-Communication-System
Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
fafa-ccnu/autoencoder_for_physical_layer
This is my attempt to reproduce and extend the results in the paper "An Introduction to Deep Learning for the Physical Layer" by Tim O'Shea and Jakob Hoydis
fafa-ccnu/Channel-Equalization-in-FBMC
Allows to reproduce all figures from "FBMC-OQAM in Doubly-Selective Channels: A New Perspective on MMSE Equalization", IEEE SPAWC, 2017.
fafa-ccnu/Deep-Learning-for-the-Physical-Layer
PyTorch implementation for part of paper "An Introduction to Deep Learning for the Physical Layer" by Kenta Iwasaki on behalf of Gram.AI.
fafa-ccnu/Deep-MIMO-Detection
fafa-ccnu/Deep-Reinforcement-Learning-Hands-On-Second-Edition
Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt
fafa-ccnu/DeepMIMODetection
fafa-ccnu/DeepRL
Deep Reinforcement Learning Lab, a platform designed to make DRL technology and fun for everyone
fafa-ccnu/DL-ChannelDecoding
On Deep Learning-based Channel Decoding
fafa-ccnu/DRL
Deep Reinforcement Learning
fafa-ccnu/easy-rl
强化学习中文教程，在线阅读地址：https://datawhalechina.github.io/easy-rl/
fafa-ccnu/gr-tutorial
A tutorial OOT module for GNU Radio
fafa-ccnu/Hands-on-RL
https://hrl.boyuai.com/
fafa-ccnu/introRL
Intro to Reinforcement Learning (强化学习纲要）
fafa-ccnu/mec_drl
Deep reinforcement learning for mobile edge computing
fafa-ccnu/OFDM_DNN
fafa-ccnu/Paper-with-Code-of-Wireless-communication-Based-on-DL
无线与深度学习结合的论文代码整理/Paper-with-Code-of-Wireless-communication-Based-on-DL
fafa-ccnu/py-radio-autoencoder
Python implementation of autoencoder based radio system
fafa-ccnu/PytorchNetHub
项目注释+论文复现+算法竞赛+Pytorch指北
fafa-ccnu/radar_dataset_astyx
fafa-ccnu/radio-transformer-networks
A PyTorch implementation of Radio Transformer Networks from the paper "An Introduction to Deep Learning for the Physical Layer".
fafa-ccnu/RadioML_2018.01A_Architectures_Benchmark
📊 Benchmarking Machine Learning Architectures for RadioML (Radio Machine Learning) 📡
fafa-ccnu/SFND_Radar_Target_Detection
Project for Udacity's Sensor Fusion Engineer Nanodegree Program - Radar Target detection
fafa-ccnu/wireless-ml
Research on machine learning in wireless communications.
fafa-ccnu/wireless-suite