Implementation for classical/quantum (variational) autoencoders

A latex document style website

Implementation of research paper: End-to-End Learning of Communications Systems Without a Channel Model

This is the code package related to the follow scientific article: Luca Sanguinetti, Alessio Zappone, Merouane Debbah 'Deep-Learning-Power-Allocation-in-Massive-MIMO' presented at the Asilomar Con…

Model summary in PyTorch similar to `model.summary()` in Keras

Sionna: An Open-Source Library for Research on Communication Systems

[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

Jupyter Notebooks and other material from tutorial sessions on Machine Learning, Data Science, and related

Latex code for making neural networks diagrams

PIMRC paper Reproducible codes

Công cụ quét và phân tích từ khoá các trang báo mạng Việt Nam

Simulation code for “Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning,” by Özlem Tugfe Demir, Emil Björnson, IEEE Open Journal of the Communi…

Using multi-agent Deep Q Learning with LSTM cells (DRQN) to train multiple users in cognitive radio to learn to share scarce resource (channels) equally without communication

This is a part of MATLAB implementation of the paper "Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks" in which Gaussian Mixture Model clustering is employed.

A simple MATLAB project to analyze CMDA communications based on PN codes when different signal types are applied.

Python implementation of autoencoder based radio system

Personalized Federated Learning with Moreau Envelopes (pFedMe) using Pytorch (NeurIPS 2020)

This repository includes the same codes for my paper "Whale Optimization Algorithm with Applications to Resource Allocation in Wireless Networks"

There are many optimization algorithms described in the book "Optimization of Engineering Design: Algorithms and Examples" by Prof. Kalyanmoy Deb. I will try to write each of those algorithms in pr…

Computation of MIMO capacity with per-antenna power constraint using MATLAB

Simulation code for "Weighted sum rate optimization for multicell MIMO systems with hardware-impaired transceivers" (ICASSP'14).