Deep Reinforcement Learning for Joint Time and Power Management in SWIPT-EH CIoT
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908717920813056 |
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| author | Abdolkhani, Nadia Khalek, Nada Abdel Hamouda, Walaa Dayoub, Iyad |
| author_facet | Abdolkhani, Nadia Khalek, Nada Abdel Hamouda, Walaa Dayoub, Iyad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15062 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for Joint Time and Power Management in SWIPT-EH CIoT Abdolkhani, Nadia Khalek, Nada Abdel Hamouda, Walaa Dayoub, Iyad Signal Processing Networking and Internet Architecture 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. |
| title | Deep Reinforcement Learning for Joint Time and Power Management in SWIPT-EH CIoT |
| topic | Signal Processing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2512.15062 |