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.