Autonomous Power Allocation Based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Networkopen access
- Authors
- Kim, Jeehyeong; Park, Joohan; Noh, Jaewon; Cho, Sunghyun
- Issue Date
- Jun-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- IoT-device-to-device communication; autonomous power allocation; deep learning; interference management
- Citation
- IEEE ACCESS, v.8, pp 107853 - 107864
- Pages
- 12
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 8
- Start Page
- 107853
- End Page
- 107864
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/1880
- DOI
- 10.1109/ACCESS.2020.3000350
- ISSN
- 2169-3536
2169-3536
- 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.
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