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Content Provider | Directory of Open Access Journals (DOAJ) |
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Author | Jeehyeong Kim Joohan Park Jaewon Noh Sunghyun Cho |
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. |
e-ISSN | 21693536 |
DOI | 10.1109/ACCESS.2020.3000350 |
Journal | IEEE Access |
Volume Number | 8 |
Language | English |
Publisher | IEEE |
Publisher Date | 2020-01-01 |
Publisher Place | United States |
Access Restriction | Open |
Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Iot-device-to-device Communication Autonomous Power Allocation Deep Learning Interference Management |
Content Type | Text |
Resource Type | Article |
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