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We propose a model-driven Bayesian deep learning framework for multiple access uplink systems in Multiuser MIMO systems. Utilizing tools from Streaming Variational Inference, we combine graphical models with neural networks… Click to show full abstract
We propose a model-driven Bayesian deep learning framework for multiple access uplink systems in Multiuser MIMO systems. Utilizing tools from Streaming Variational Inference, we combine graphical models with neural networks to enable fast online machine learning. The proposed distributed inference framework is shown to be robust and suitable for the online scenario. Our simulations demonstrate the robustness of the proposed solution in online propagation environments and its ability to capture uncertainty.
Journal Title: IEEE Internet of Things Journal
Year Published: 2022
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