Ray-Tracing Driven ANN Propagation Models for Indoor Environments at 28 GHz

被引：2
|
作者：
Seretis, Aristeidis [1 ]
Hashimoto, Takahiro [1 ]
Zeng, Kun [2 ]
Sarris, Costas D. [1 ]
机构：
[1] Univ Toronto, Edward S Rogers Sr Dept Elect & Comp Engn, Toronto, ON, Canada
[2] Huawei Technol Co Ltd, Chengdu, Peoples R China
来源：
2020 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION AND NORTH AMERICAN RADIO SCIENCE MEETING | 2020年
关键词：
D O I：
10.1109/IEEECONF35879.2020.9330037
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：
Ray-tracing is widely used for radio propagation modeling of indoor environments, such as hallways and offices. Shooting and bouncing ray-tracing methods are faster than full-wave methods in such environments, especially as the frequency of operation increases following the new 5G specifications. Still, a machine learning approach can generalize a few ray-traced points into full signal strength maps of arbitrary resolution. In this paper, a feedforward standard artificial neural network is trained by ray-tracing data at 28 GHz to predict signal strength in Gamma-shaped corridor. The network's accuracy in reconstructing the actual signal levels is on par with that of the ray-tracer.
引用
收藏
页码：1029 / 1030
页数：2
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