llagona's starred repositories
Open-Science-Training-Handbook_EN
Main handbook content divided into chapters.
py-channelmodel
A wireless communication channel model module for simulations.
SatCommSystem-QPSK-OFDM-LSEstimation-TransionosphericChannel
MATLAB Imitation Modeling for the BER of the Satellite Communication System using QPSK and OFDM Modulation with LS Channel Estimation based on Pilot Signals in the Transionospheric Communication Channel with Rician Fading, Multipath, Frequency Selectivity and Limited Coherence Bandwidth
DNN_detection_via_keras
This is the simplest implementation of Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems using keras.
CyberEther
Multi-platform GPU-accelerated interface for compute-intensive pipelines. Radio, the final frontier.
awesome-chatgpt-prompts
This repo includes ChatGPT prompt curation to use ChatGPT better.
Reinforcement-Learning-for-Wireless
Some simulations for wireless RL
jcm-awgn-imp
Results submitted to ICASSP 2020 for the paper titled "Joint Coding and Modulation in the Ultra-Short Blocklength Regime for Bernoulli-Gaussian Impulsive Noise Channels Using Autoencoders"
Security-and-Robustness-of-Deep-Learning-in-Wireless-Communication-Systems
A research oriented repository on the Security and Robustness of Deep Learning for Wireless Communication Systems
capacity-approaching-autoencoders
Repository with the code on autoencoders and mutual information
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/
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
cheatsheets
Official Matplotlib cheat sheets
AE-Com-Roadmap
Collections of Papers and Codes about Communication Systems Built by Autoencoder
py-radio-autoencoder
Python implementation of autoencoder based radio system
deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
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