Radio Environment Map of an LTE Deployment Based on Machine Learning Estimation of Signal Levels

被引：1

作者：

Santana, Yosvany Hervis ^{[1
,2
]}

Plets, David ^{[1
]}

Martinez Alonso, Rodney ^{[3
]}

Nieto, Glauco Guillen ^{[2
]}

Martens, Luc ^{[1
]}

Joseph, Wout ^{[1
]}

机构：

[1] Univ Ghent, IMEC, Ghent, Belgium

[2] LACETEL, Havana, Cuba

[3] Univ Ghent, IMEC, Havana, Cuba

来源：

2022 IEEE INTERNATIONAL SYMPOSIUM ON BROADBAND MULTIMEDIA SYSTEMS AND BROADCASTING (BMSB) | 2022年

关键词：

Coverage; Estimation; Machine Learning; Received Signal; REM;

D O I：

10.1109/BMSB55706.2022.9828582

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Accurate estimation of Propagation Path Loss is important for reliable and optimized coverage of a service. In literature, a diversity of theoretically or experimentally based propagation models have been documented to estimate the received signal level. The goal of this work is to estimate the effective coverage area of service, predict the Path Loss, and build a Radio Environment Map (REM) using a sensor network. To this end, a sensor's correlation area is defined. By using Machine Learning (ML), the received signal level variation in this area can be estimated correctly 92.3% of the time, with a Mean Absolute Error (MAE) of 1.57 dB. Finally, a proper distribution of sensors based on the correlation area, and ML tools leads to building a REM for the effective coverage area. This approach is applied to a Long-Term Evolution network.

引用

页数：6

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