Feature Extraction in Reference Signal Received Power Prediction Based on Convolution Neural Networks

被引：12

作者：

Yi, Zheng ^{[1
]}

Liu, Zhiwen ^{[2
]}

Rong, Huang ^{[1
]}

Ji, Wang ^{[1
]}

Xie, Wenwu ^{[3
]}

Liu, Shouyin ^{[1
]}

机构：

[1] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Hubei, Peoples R China

[2] Cent China Normal Univ, Wollongong Joint Inst, Wuhan 430079, Hubei, Peoples R China

[3] Hunan Inst Sci & Technol, Yueyang 414006, Peoples R China

来源：

IEEE COMMUNICATIONS LETTERS | 2021年 / 25卷 / 06期

关键词：

Feature extraction; reference signal received power; convolution neural networks;

D O I：

10.1109/LCOMM.2021.3054862

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

In this letter, an environmental features (EFs) extraction model is proposed for estimating reference signal received power (RSRP) accurately. Firstly, 18-D measured data is transformed into 15-D physical features (PFs). Then 15-D PFs is reduced to 14-D by performing correlation analysis. Secondly, EFs are extracted from the environmental maps (EMs) by applying Convolution Neural Networks (CNNs). Finally, several Machine Learning Regressors (MLRs) are trained to predict RSRP combining PFs and EFs as inputs. The results, in test dataset, show that prediction performance of MLRs is improved through 14-D PFs, and is further improved in nonlinear MLRs combining PFs and EFs.

引用

页码：1751 / 1755

页数：5

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