Summary of Automatic Ai Model Selection For Wireless Systems: Online Learning Via Digital Twinning, by Qiushuo Hou et al.
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning
by Qiushuo Hou, Matteo Zecchin, Sangwoo Park, Yunlong Cai, Guanding Yu, Kaushik Chowdhury, Osvaldo Simeone
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a general methodology for the online optimization of artificial intelligence (AI)-based applications in modern wireless networks, specifically focusing on automatic model selection (AMS) mappings. The authors propose a novel method that corrects for the bias of the simulator by means of limited real data collected from the physical system, allowing for more accurate optimization of AMS mappings. This approach has significant advantages, as demonstrated by experimental results using a graph neural network-based power control app. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI is being used in wireless networks to improve scheduling and power control. The problem is that optimizing these AI models requires lots of data from different situations, which would take a long time if done online. One solution is to use a digital twin, a virtual copy of the physical system, to generate fake data. But this simulator isn’t perfect, so using it alone wouldn’t work well in real life. This paper shows how to correct for these biases and optimize AI models more accurately. |
Keywords
* Artificial intelligence * Graph neural network * Optimization
