Deep reinforcement learning online offloading for SWIPT multiple access edge computing network
More computation-intensive and low latency applications are emerging recently, and they are constrained by the computing power and battery life of internet of things (IoT). Simultaneous wireless information and power transfer (SWIPT) with mobile-edge computing (MEC) can improve the data processing c...
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| Main Authors: | , , , |
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| Format: | Proceedings |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers
2021
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/32718/1/Deep%20reinforcement%20learning%20online%20offloading%20for%20SWIPT%20multiple%20access%20edge%20computing%20network.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/32718/2/Deep%20Reinforcement%20Learning%20Online%20Offloading%20for%20SWIPT%20Multiple%20Access%20Edge%20Computing%20Network.pdf https://eprints.ums.edu.my/id/eprint/32718/ https://ieeexplore.ieee.org/document/9612551 |
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https://eprints.ums.edu.my/id/eprint/32718/1/Deep%20reinforcement%20learning%20online%20offloading%20for%20SWIPT%20multiple%20access%20edge%20computing%20network.ABSTRACT.pdfhttps://eprints.ums.edu.my/id/eprint/32718/2/Deep%20Reinforcement%20Learning%20Online%20Offloading%20for%20SWIPT%20Multiple%20Access%20Edge%20Computing%20Network.pdf
https://eprints.ums.edu.my/id/eprint/32718/
https://ieeexplore.ieee.org/document/9612551