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RF energy modeling using machine learning for energy harvesting communications systems
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Ye, Youjie, Azmat, Freeha, Adenopo, Idris, Chen, Yunfei and Shi, Yunfei (2021) RF energy modeling using machine learning for energy harvesting communications systems. International Journal of Communication Systems, 34 (3). e4688. doi:10.1002/dac.4688 ISSN 1074-5351.
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Official URL: https://doi.org/10.1002/dac.4688
Abstract
Machine learning (ML) theories and methods are mainly based on probability theory and statistics. It is a very powerful tool for data modelling. On the other hand, energy harvesting has been regarded as a viable solution to extending battery lifetime of wireless sensor network. Motivated by these, modelling of the radio frequency (RF) energy available to the wireless nodes is required for efficient operation of wireless networks. In this work, we will use different ML algorithms to model the RF energy data for efficient operation of energy harvesting communication systems. Four ML algorithms are studied and compared in terms of the accuracy for RF energy modelling using the energy data in the band between 1805 and 1880 MHz. The results show that linear regression (LR) has the highest accuracy and the most stable performance, while decision tree is the worst model. Also, in terms of the operation efficiency of the system, LR has the best performance, followed by support vector machine and random forest algorithm.
| Item Type: | Journal Article | ||||||||
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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| Library of Congress Subject Headings (LCSH): | Energy harvesting, Wireless sensor networks, Wireless communication systems -- Power supply, Machine learning, Radio frequency | ||||||||
| Journal or Publication Title: | International Journal of Communication Systems | ||||||||
| Publisher: | Wiley-Blackwell | ||||||||
| ISSN: | 1074-5351 | ||||||||
| Official Date: | February 2021 | ||||||||
| Dates: |
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| Volume: | 34 | ||||||||
| Number: | 3 | ||||||||
| Article Number: | e4688 | ||||||||
| DOI: | 10.1002/dac.4688 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 12 November 2020 | ||||||||
| Date of first compliant Open Access: | 18 December 2020 |
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