[Deep Dive] GLANCE: Graph-based Learnable Digital Twin for Wireless Networks

https://is1-ssl.mzstatic.com/image/thumb/Podcasts221/v4/9f/98/b8/9f98b8e1-d7dd-d166-6170-cb4e08f78c0b/mza_9663701580874883811.jpg/600x600bb.jpg

Deep Dive into Networked AI

NetAIStudio

10 episodes

2 days ago

Explore the cutting-edge of artificial intelligence (AI), machine learning, optimization, signal processing, wireless communications, networking, and broader networked systems. Join us as we dive deep into frontier research papers, uncover groundbreaking innovations, and discuss the possibilities shaping the future of technology and science.

Technology

RSS

All content for Deep Dive into Networked AI is the property of NetAIStudio and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.

Technology

https://d3t3ozftmdmh3i.cloudfront.net/staging/podcast_uploaded_nologo/42656925/42656925-1734080297033-cd05f7d9e5055.jpg

[Deep Dive] GLANCE: Graph-based Learnable Digital Twin for Wireless Networks

Deep Dive into Networked AI

17 minutes 17 seconds

9 months ago

[Deep Dive] GLANCE: Graph-based Learnable Digital Twin for Wireless Networks

In this episode, we dive into the applications of graph neural networks as a learnable digital twin of network simulators, which can accelerate network optimization by its fast and differentiable prediction of networking key performance indicators (KPIs). This episode is based on a preprient authored by Boning Li, et al.

Generated using NotebookLM from Google, this podcast highlights the key findings and implications of this research.

🎧 Read the paper here: [arXiv]

📷 Cover Image Source: imagine.art, Microsoft Designer

🎵 BGM: Artlist.io

🛠️ Credits: NotebookLM by Google