Transformer-Based Online Bayesian Neural Networks for Grant-Free Uplink Access in CRAN With Streaming Variational Inference
| Autor: | Vincent K. N. Lau, Nilesh Kumar Jha |
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| Rok vydání: | 2022 |
| Předmět: |
Artificial neural network
Computer Networks and Communications business.industry Computer science Deep learning Bayesian probability Inference Online machine learning Computer Science Applications Hardware and Architecture Robustness (computer science) Signal Processing Artificial intelligence Graphical model business Information Systems Transformer (machine learning model) |
| Zdroj: | IEEE Internet of Things Journal. 9:7051-7064 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2021.3113679 |
| Popis: | We propose a model driven Bayesian deep learning framework for multiple access uplink systems in Multi-user MIMO systems. Utilising 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 online scenario. Our simulations demonstrate the robustness of the proposed solution in online propagation environments and its ability to capture uncertainty. |
| Databáze: | OpenAIRE |
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