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

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-01-29 DOI:10.1109/LCOMM.2025.3536182

Nadia Abdolkhani;Nada Abdel Khalek;Walaa Hamouda;Iyad Dayoub

引用次数: 0

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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基于深度强化学习的swift - eh物联网联合时间和电源管理

本文介绍了一种新颖的深度强化学习（DRL）方法，用于在具有同步无线信息和功率传输（SWIPT）功能的认知物联网（CIoT）系统中进行联合时间分配和功率控制。CIoT 发射器利用可学习的时间切换因子自主管理能量采集（EH）和传输，同时优化功率以提高吞吐量和寿命。在小尺度衰减、现实 EH 和干扰约束条件下，联合优化被建模为马尔可夫决策过程。我们开发了一个双深度 Q 网络（DDQN），并增强了置信度上限。仿真结果表明，我们的方法优于现有的 DRL 方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.