DeepMIMOv4: A Toolchain and Database for Ray-tracing Datasets.

This advanced and complex project implements an AI-powered optimization system for 5G Open RAN networks. Using machine learning and deep learning, the system optimizes network performance by detecting anomalies, predicting network traffic, and dynamically allocating resources.

A PyTorch-based toolkit for simulating communication systems

M. Polese, F. Restuccia, and T. Melodia, "DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks", Proc. of ACM Intl. Symp. on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc), July 2021.

Code containing RRM simulation using RL in a scenario with RAN slicing.

This package analyzes the age of information (AoI) in a wireless network, providing metrics for network performance evaluation. It can be easily integrated into simulation environments for research on AoI.

Camera based beam prediction

Comyx is an optimized and modular Python library for simulating wireless communication systems

Learning Environment-aware and hardware-compatible beam-forming codebooks

This repository contains the code, datasets, and simulation tools for the paper "Machine Learning-Based mmWave MIMO Beam Tracking in V2I Scenarios: Algorithms and Datasets", published at IEEE Latincom 2024.

Vision-Aided Beam Tracking

Fusion of federated leaerning algorithm and reconfigurable intelligent surface from 6G

RAN Simulator for OAICT

RAN Fusion a NextG RAN Simulator

A curated list of Integrated Sensing and Communications resources

Differentiable full-wave electromagnetic solver built with Slang on GPU, native to PyTorch.

In this repository, you will find the source code for analyzing tracks during data transmission using Software Defined Radios. Metrics about error positioning and error syndrome are attached.

This project implements a Deep Q-Network (DQN) for optimizing Reconfigurable Intelligent Surface (RIS) configurations in 6G wireless communication systems. The system uses reinforcement learning to select optimal RIS phase configurations to maximize signal quality and user fairness.

Intent-based radio resource scheduling in a scenario with multiple slices.

Investigating AI fairness vulnerabilities in RL-driven RIS for B5G/6G networks. Research toolkit for bias analysis, mitigation strategies, and robust wireless communication systems.