Learning Parameters of Stochastic Radio Channel Models From Summaries

Learning Parameters of Stochastic Radio Channel Models From Summaries

Estimating parameters of stochastic radio channel models based on new measurement data is an arduous task usually involving multiple steps such as multipath extraction and clustering. We propose two different machine learning methods, one based on approximate Bayesian computation (ABC) and the other...

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Bibliographic Details
Main Authors: Ayush Bharti, Ramoni Adeogun, Troels Pedersen
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Open Journal of Antennas and Propagation
Subjects:
Machine learning
Monte Carlo methods
deep learning
Bayesian inference
radio channel modeling
approximate Bayesian computation
Online Access:https://ieeexplore.ieee.org/document/9076672/

Internet

https://ieeexplore.ieee.org/document/9076672/

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