Autonomous Power Allocation based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Network

TL;DR

This paper introduces a fully autonomous, distributed deep learning-based power allocation method for IoT device-to-device communication in 5G networks, reducing eNB burden and improving cell throughput.

Contribution

It proposes a novel distributed deep learning architecture enabling IoT-D2D devices to independently optimize their transmit power, enhancing throughput and interference management.

Findings

Near-optimal cell throughput achieved

Interference to eNB effectively suppressed

Devices operate independently with group-trained deep learning

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.