Adaptive Power Control using Reinforcement Learning in 5G Mobile Networks

被引：0

作者：

Park, Hyebin ^{[1
]}

Lim, Yujin ^{[1
]}

机构：

[1] Sookmyung Womens Univ, Dept IT Engn, Seoul, South Korea

来源：

2020 34TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2020) | 2020年

基金：

新加坡国家研究基金会;

关键词：

power control; RRH switching; Device-to-device (D2D) communication; reinforcement learning; fifth generation (5G);

D O I：

10.1109/icoin48656.2020.9016566

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With increasing data transmission requirements, radio remote heads (RRH) are densely deployed and device-to-device (D2D) communication is developed. D2D communication can decrease device energy consumption by reducing transmission power. In addition, D2D communication reuses cellular resources, and it can maximize system spectral efficiency. However, inter-cell interference from densely deployed RRHs can be critical to D2D communication. To mitigate the inter-cell interference problem, RRH switching method has been studied with D2D communication. In this paper, an adaptive power control algorithm using reinforcement learning in 5G mobile networks is proposed. To maximize system energy efficiency, we utilize Q-learning in the baseband unit (BBU) pool to decide the optimal number of active RRH. To maximize device energy efficiency, we utilize Q-learning in each device to decide transmission power. Further, to take account of changing channel quality, we adjust target SINR according to outage probability. Simulations and performance evaluations are presented to compare the system energy efficiency and device energy efficiency as well as average SINR.

引用

页码：409 / 414

页数：6

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