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H. Alrikabi

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Open access Jul 2026

AI-Optimized Wake-Up Radio Systems for 6G IoT

This paper proposes an end-to-end AI-optimized WuR stack that combines a TinyML classifier embedded in the WuR path to suppress false triggers and adapt detection thresholds, and a reinforcement-learning scheduler at the base station or gateway that co-optimizes wake-up signaling with 3GPP NR DRX/C-DRX timers.

H. Alrikabi, I. R. Niama ALRubeei, A. Alaidi et al. · 0 citations

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