Matrix Factorization-Based RSS Interpolation for Radio Environment Prediction

Norisato Suga, Kazuto Yano, Julian Webber, Yafei Hou, Eiji Nii, Toshihide Higashimori, Yoshinori Suzuki

研究成果: Article査読

抄録

This letter proposes matrix factorization (MF) based interpolation of received signal strength (RSS) from a transmitter mounted on moving robot in factory environment. For realizing the reliable wireless communication, machine learning based channel prediction methods have been intensively studied in the past decade. However, some traffic models will make the observation of RSS sequence be intermittent, and the missing values must be interpolated before input to the predictor. Classical interpolation such as linear interpolation cannot appropriately estimate the missing values because the result of the interpolation depends on the observation time. In this letter, we propose to apply an MF-based interpolation technique to RSS interpolation in order to restore the true RSS variation pattern. Moreover, the basic MF-based interpolation is improved by introducing a smoothing term in an objective function to represent the smooth variation of the RSS sequence. The simulation results show that the MF-based interpolation can improve the prediction accuracy of the machine learning based channel prediction method.

本文言語English
論文番号9392000
ページ(範囲)1464-1468
ページ数5
ジャーナルIEEE Wireless Communications Letters
10
7
DOI
出版ステータスPublished - 2021 7月
外部発表はい

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 電子工学および電気工学

フィンガープリント

「Matrix Factorization-Based RSS Interpolation for Radio Environment Prediction」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル