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IEEE/ICACT20260196 Slide.18        [Big Slide]	Oral Presentation
	Recent efforts have leveraged machine learning (ML) and reinforcement learning (RL) for predictive retransmission conˇ©trol [7], [15]. These models learn to estimate channel delay and optimize retransmission intervals adaptively. While promising in terms of performance, such solutions demand extensive training data and computational resources that exceed the capabilities of resource-limited loT endpoints, making largeˇ© scale deployment impractical.

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