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arXiv · 2512.15062

Deep Reinforcement Learning for Joint Time and Power Management in SWIPT-EH CIoT

Nadia Abdolkhani·Nada Abdel Khalek·Walaa Hamouda·Iyad Dayoub
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Abstract

This letter presents a novel deep reinforcement learning (DRL) approach for joint time allocation and power control in a cognitive Internet of Things (CIoT) system with simultaneous wireless information and power transfer (SWIPT). The CIoT transmitter autonomously manages energy harvesting (EH) and transmissions using a learnable time switching factor while optimizing power to enhance throughput and lifetime. The joint optimization is modeled as a Markov decision process under small-scale fading, realistic EH, and interference constraints. We develop a double deep Q-network (DDQN) enhanced with an upper confidence bound. Simulations benchmark our approach, showing superior performance over existing DRL methods.

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Nadia Abdolkhani, Nada Abdel Khalek, Walaa Hamouda, Iyad Dayoub. 2025-12-17. Deep Reinforcement Learning for Joint Time and Power Management in SWIPT-EH CIoT. https://doi.org/10.1109/lcomm.2025.3536182.

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