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[Deep Dive] Learning Decentralized Wireless Resource Allocations with Graph Neural Networks
In this episode, we dive into the combination of graph neural networks and unsupervised primal-dual learning, a model-free approach to scalable, intelligent wireless resource allocations. This episode is based on two papers published in IEEE Transactions on Signal Processing by Zhiyang Wang, Mark Eisen, and Alejandro Ribeiro.
Generated using NotebookLM from Google, this podcast highlights the key findings and implications of this research.
🎧 Read the papers here: [Eisen 2019], [Wang 2022]
📷 Cover Image Source: imagine.art, Microsoft Designer
🎵 BGM: Artlist.io
🛠️ Credits: NotebookLM by Google
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By NetAIStudioIn this episode, we dive into the combination of graph neural networks and unsupervised primal-dual learning, a model-free approach to scalable, intelligent wireless resource allocations. This episode is based on two papers published in IEEE Transactions on Signal Processing by Zhiyang Wang, Mark Eisen, and Alejandro Ribeiro.
Generated using NotebookLM from Google, this podcast highlights the key findings and implications of this research.
🎧 Read the papers here: [Eisen 2019], [Wang 2022]
📷 Cover Image Source: imagine.art, Microsoft Designer
🎵 BGM: Artlist.io
🛠️ Credits: NotebookLM by Google