A customized CNN trained on CIR spectrogram images classifies LOS and NLOS conditions for RIS-based indoor sensing, with reported accuracies of 86 to 99.9 percent across three measured environments.
Title resolution pending
1 Pith paper cite this work, alongside 15 external citations. Polarity classification is still indexing.
1
Pith paper citing it
15
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
fields
eess.SP 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
AI-Assisted NLOS Sensing for RIS-Based Indoor Localization in Smart Factories
A customized CNN trained on CIR spectrogram images classifies LOS and NLOS conditions for RIS-based indoor sensing, with reported accuracies of 86 to 99.9 percent across three measured environments.