Perspectives Using a Reinforcement Learning Approach and Ray-Tracing SW for 5G+Indoor Coverage Optimization

被引：1

作者：

Hong, Ju Yeon ^{[1
]}

Kim, Chung-Sup ^{[1
]}

Lim, Jong-Su ^{[1
]}

Chong, Young-Jun ^{[1
]}

Kim, Junseok ^{[1
]}

机构：

[1] Elect & Telecommun Res Inst ETRI, Radio Resorce Res Grp, Daejeon, South Korea

来源：

12TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC 2021): BEYOND THE PANDEMIC ERA WITH ICT CONVERGENCE INNOVATION | 2021年

关键词：

Dyna-Q; ray tracing; wireless propagation;

D O I：

10.1109/ICTC52510.2021.9620977

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In wireless communications, measurement-based stochastic GSCM model, ray-tracing based deterministic model, and hybrid model are used. The prediction method based on ray-tracing provides propagation models for environments. Also, material properties were modelled and applied to a ray-tracing analysis. This paper presents an extended reinforcement learning approach for the deterministic ray-based propagation method for indoor environments. To cope with the explosive use of wireless communication, we are studying a method of applying RL to the propagation model of a deterministic prediction method to optimize antenna location combinations and coverage extensions in indoor scenarios such as small cell and DAS systems.

引用

页码：1777 / 1779

页数：3

共 24 条

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