Autonomous Power Allocation Based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Network
- Title
- Autonomous Power Allocation Based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Network
- Author
- 조성현
- Keywords
- IoT-device-to-device communication; autonomous power allocation; deep learning; interference management
- Issue Date
- 2020-06
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Citation
- IEEE ACCESS, v. 8.0, Page. 107853-107864
- Abstract
- For Device-to-device (D2D) communication of Internet-of-Things (IoT) enabled 5G system, there is a limit to allocating resources considering a complicated interference between different links in a centralized manner. If D2D link is controlled by an enhanced node base station (eNB), and thus, remains a burden on the eNB and it causes delayed latency. This paper proposes a fully autonomous power allocation method for IoT-D2D communication underlaying cellular networks using deep learning. In the proposed scheme, an IoT-D2D transmitter decides the transmit power independently from an eNB and other IoT-D2D devices. In addition, the power set can be nearly optimized by deep learning with distributed manner to achieve higher cell throughput. We present a distributed deep learning architecture in which the devices are trained as a group but operate independently. The deep learning can attain near optimal cell throughput while suppressing interference to eNB.
- URI
- https://ieeexplore.ieee.org/document/9109349https://repository.hanyang.ac.kr/handle/20.500.11754/179241
- ISSN
- 2169-3536
- DOI
- 10.1109/ACCESS.2020.3000350
- Appears in Collections:
- ETC[S] > ETC
- Files in This Item:
- 3771_조성현.pdfDownload
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