A similarity-aware sampling method using Chebyshev distance and KDTree keeps 5G indoor localization accurate while adapting with as few as 50 exemplars, reaching 0.261 m MAE in one tested configuration.
A Comprehensive Framework for 5G Indoor Localization,
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5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization
A similarity-aware sampling method using Chebyshev distance and KDTree keeps 5G indoor localization accurate while adapting with as few as 50 exemplars, reaching 0.261 m MAE in one tested configuration.