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Toward Autonomous, Software-Defined Networks of Wireless Drones

Paul G. Allen School via YouTube

Overview

Explore cutting-edge research on autonomous, software-defined networks of wireless drones in this comprehensive seminar. Delve into applications for augmenting cellular connectivity, streaming live video, and providing disaster relief. Examine self-optimizing drone networks for tactical scenarios and discuss open research challenges in enabling seamless, programmable connectivity. Learn about innovative projects like SkyCell, HIRO-NET, and SwarmControl, which address issues such as interference management, video streaming optimization, and SDN control frameworks for UAV networks. Gain insights into data-driven optimization techniques and parallel deep reinforcement learning for multi-agent training in UAV networks. Discover the potential of aerial experimentation platforms for advanced wireless research, presented by Professor Tommaso Melodia, a leading expert in wireless networked systems and Internet of Things technologies.

Syllabus

Intro
Networked Drones: Multi-hop Extensions of Cellular Networks
Networked Drones: UAV Tactical Networks
Connected Drones: UAV Surveillance Networks
Connected Drones: Some Core Challenges
Connected Drones: Outline
SkyCell: Architecture
SkyCell: Hardware Overview
SkyCell: Software-Defined Base Station
Challenge: Flying Users Source of Interference!
Motivation: Experimental measurements
Intuition: Directional TX and Location control
Problem Statement: Video streaming from POI
Proposed Architecture: Open-RAN Control Loop
Results: Performance Evaluation
And People Need Connectivity
Three Main Questions
HIRO-NET: Two Tier Architecture
HIRO-NET: Lower Tier (BLE)
SwarmControl: SDN control framework for UAV Networks
SwarmContral: The Control Framework (1/2)
SwarmControl: Drone Programmable Protocol Stack (DPPS)
SwarmControl: Prototyping the UAV nodes
Experimental Evaluation: Indoor (3/3)
Overcoming model-and-optimize' approaches
Data-driven optimization for UAV networks
Parallel DRL multi-agent training
Optimization Result
AERPAW: Aerial Experimentation and Research Platform for Advanced Wireless

Taught by

Paul G. Allen School

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