This is the COST2100 channel model, a MATLAB implementation of a spatially consistent radio channel model for MIMO and Massive MIMO communication. Originally developed within COST 2100 (http://www.…

A1: a) Representation of signal in time and frequency domain, observing the effect of sampling frequency. b) Transmission and reconstruction of an image using BPSK, QPSK and 16-QAM. Plotted the co…

Python coding examples for wireless communication systems. This includes currently the topics of modulation such as 4-QAM, BPSK (2-PSK) with an AWGN channel and also Rayleigh fading.

Generate Rayleigh fading channel with Doppler shift effect.

Bit Error and Block Error Rate Training for ML-Assisted Communication: simulation scripts and source code

DeepMIMO (v2 & v3) Python Dataset Framework for mmWave and massive MIMO Research

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to …

Matlab Code for Restricted/Deep Boltzmann Machines and Autoencoders

Using e-Greedy Q Learning, e-Greedy Q-Learning with e-decay, Deep Q-Network (DQN) to learn to navigate a Helicopter course

This is a very basic DQN implementation, which uses OpenAI's gym environment and Keras/Theano neural networks.

GNU Radio

digital signal processing library for software-defined radios

KEras Reinforcement Learning gYM agents

A couple of simple GANs in Keras

Unofficial Pytorch implementation of Deep Learning-Based MIMO Communications (Timothy J. O’Shea)

backup for RML2016.04C dataset

Dataset for 《Dual Residual Denoising Autoencoder for Modulation Signals with Channel Attention Mechanism》

This script shows the implementation of Autoencoders for QPSK Channel Constellation

This is an implementation of my research idea: A Channel-Specific Autoencoder inspired from the principle of "Co-operation" and "division of labour".

Non-Profiled Deep Learning-based Side-Channel Preprocessing with Autoencoders

Multi-Channel AutoEncoder for MNIST.

PyTorch implementation of a variational autoencoder (VAE) for use on multi-channel 2D data such as images

paper code of A-Novel-OFDM-Autoencoder-Featuring-CNN-Based-Channel-Estimation-for-Internet-of-Vessels

Code for "Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels" NeurIPS 2019