AUTHOR=Zhang Hao , Wang Yue , Park Hyeong Kwang Benno , Sung Tae Hyun
TITLE=Design and investigation of small-scale long-distance RF energy harvesting system for wireless charging using CNN, LSTM, and reinforcement learning
JOURNAL=Frontiers in Physics
VOLUME=12
YEAR=2024
URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2024.1337421
DOI=10.3389/fphy.2024.1337421
ISSN=2296-424X
ABSTRACT=Introduction: The energy supply challenge in wireless charging applications is currently a significant research problem. To address this issue, this study introduces a novel small-scale long-distance radio frequency (RF) energy harvesting system that utilizes a hybrid model incorporating CNN, LSTM, and reinforcement learning. This research aims to improve RF energy harvesting and wireless charging efficiency.
Method: The methodology of this study involves data collection, data processing, model training and evaluation, and integration of reinforcement learning algorithms. Firstly, RF signal data at different distances are collected and rigorously processed to create training and testing datasets. Next, the CNN-LSTM model is trained using the prepared data, and model performance is enhanced by adjusting hyperparameters. During the evaluation phase, specialized test data is used to assess the accuracy of the model in predicting RF energy harvesting and wireless charging efficiency. Finally, reinforcement learning algorithms are integrated, and a reward function is defined to incentivize efficient wireless charging and maximize energy harvesting, allowing the system to dynamically adjust its strategy in real time.
Results: Experimental validation demonstrates that the optimized CNN-LSTM model exhibits high accuracy in predicting RF energy harvesting and wireless charging efficiency. Through the integration of reinforcement learning algorithms, the system can dynamically adjust its strategy in real time, maximizing energy harvesting efficiency and charging effectiveness. These results indicate significant progress in long-distance RF energy harvesting and wireless charging with this system.
Discussion: The results of this study validate the outstanding performance of the small-scale long-distance RF energy harvesting system. This system is not only applicable to current wireless charging applications but also demonstrates potential in other wireless charging domains. Particularly, it holds significant prospects in providing energy support for wearable devices, Internet of Things (IoT), and mobile devices.