Artifacts in 5G NR sensing are modeled as multiplicative-noise-induced periodic range extensions from Zadoff-Chu pilots, and a physics-informed masked encoder-decoder suppresses them, boosting detection probability to 98.88% in field tests.
Hierarchical reinforcement learning-based beam selection for integrated sensing and communication systems,
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Characterization and Mitigation of Polyphase-Code Artifacts in 5G NR ISAC
Artifacts in 5G NR sensing are modeled as multiplicative-noise-induced periodic range extensions from Zadoff-Chu pilots, and a physics-informed masked encoder-decoder suppresses them, boosting detection probability to 98.88% in field tests.