Stars
A comprehensive scheduling optimization scheme for TT and AVB traffic in Time-Sensitive Networking
Open code for the paper 'Scalable Scheduling of Real-Time Task and Message for Multi-Rate Task Chain in Time Sensitive System'
Designed and implemented a Graph Neural Network (GNN) + Proximal Policy Optimization (PPO) reinforcement learning system to solve workforce scheduling and routing with strict time constraints.
Multi-source Strategy Adaptation Network for Time-triggered Flow Scheduling in Unknown Environments
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling (AAAI 2020)
This repository is the official implementation of the paper “Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning”. IEEE Transactions on Neural Networks and Learning…
Link Scheduling using Graph Neural Networks, IEEE TWC
Fast creation and configuration of topologies, traffic matrices and event schedules for network experiments
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 detect…
Transformer's Encoder-based 5G Transport Block Size (TBS) Prediction Model
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 detect…
A Framework for Safe and Accelerated Reinforcement Learning-based Radio Resource Management
A deep reinforcement learning framework for dynamic 5G resource allocation. This project uses Proximal Policy Optimization (PPO) with learnable priority weights to intelligently assign MAC layer re…
A offline scheduler based on time-triggered ethernet.
GitHub for the article Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic (Fabio Saggese, Luca Pasqualini, Marco Moretti, Andrea Abrardo)
Framework for learning handover algorithms using deep reinforcement learning.
This project optimizes handover management in 5G networks using Q-Learning to minimize handover failures and enhance QoS. It employs a dynamic grid-based system where an agent learns to ensure low …
Codes for paper "Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation"
Repo for my srsRAN Project demonstration
A machine-learning-based DDoS detection and mitigation system using SDN. Built with Ryu and Mininet, it simulates network traffic, collects flow data, detects attacks via a Random Forest model, and…
An Anomaly-Based Intrusion Detection System (AIDS) built with a Random Forest classifier on the CICIOT23 dataset. This project automates the full ML pipeline to detect anomalous IoT network traffic…
To reproduce the results of the paper: Probabilistic Delay Forecasting in 5G Using Recurrent and Attention-Based Architectures
To reproduce the results of the paper: Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities
A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networking
Applying Reinforecement Learning Algortihms to perform traffic engineering using SDN
Automated traffic engineering using evolutionary optimization algorithms