Daniele Tarchi

Reinforcing Edge-DASH: Deep Learning for Multi-Objective Streaming Optimization

Arash Bozorgchenani, David Naseh, Daniele Tarchi, Sergio A. Salinas Monroy, Farshad Mashhadi, Qiang Ni

TL;DR: This work considers an Edge-DASH scenario and forms a joint optimization problem that involves four critical aspects: bitrate allocation, user-to-server assignment, caching, and bandwidth allocation, and employs deep reinforcement learning for three of them and develops a heuristic solution for the fourth.

With the growing demand for multimedia services, Dynamic Adaptive Streaming over HTTP (DASH) has become a key solution for delivering high-quality video content. In this work, we consider an Edge-DASH scenario and formulate a joint optimization problem that involves four critical…Read more

Unified Distributed Machine Learning for 6G Intelligent Transportation Systems: A Hierarchical Approach for Terrestrial and Non-Terrestrial Networks

David Naseh, Arash Bozorgchenani, Swapnil Sadashiv Shinde, Daniele Tarchi

The successful integration of Terrestrial and Non-Terrestrial Networks (T/NTNs) in 6G is poised to revolutionize demanding domains like Earth Observation (EO) and Intelligent Transportation Systems (ITSs). Still, it requires Distributed Machine Learning (DML) frameworks that are…Read more

Network Sliced Distributed Learning-as-a-Service for Internet of Vehicles Applications in 6G Non-Terrestrial Network Scenarios

David Naseh, Swapnil Sadashiv Shinde, Daniele Tarchi

In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communicatio…Read more