AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is demonstrated that multimodal sensing with explainable AI improves operational visibility and educational analytics and the smart lab framework is proposed, supporting safer, more efficient, and data-driven learning across engineering disciplines.
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.
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