  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.
