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Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization
Existing localization approaches utilizing environment-specific channel state information (CSI) excel under specific environment but struggle to generalize across varied environments.
MetaLoc: Learning to Learn Wireless Localization
The framework introduces two paradigms for the optimization of meta-parameters: a centralized paradigm that simplifies the process by sharing data from all historical environments, and a distributed paradigm that maintains data privacy by training meta-parameters for each specific environment separately.
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