[BS-3] 新しいネットワークトポロジーを実現する次世代無線通信技術

[BS-3-6] An Improved Location Fingerprinting Method for 5G Ultra-Dense Networking Scenarios

^○YANG XIANG, Yue Su, Kien Nguyen, Hiroo Sekiya (Chiba Univ.)

この講演は本会「学術奨励賞受賞候補者」の資格対象です。
この講演はキャリアエクスプローラーの掲載を希望しています。

Keywords:5G UDN、Indoor Positioning、Location Fingerprinting

In this paper, the RPCA techniques is combined with the RSSI-based location fingerprinting algorithm to improve localization accuracy. We use the RPCA algorithm to deal with the noise values and outliers in the location fingerprint database established in the offline phase of the location fingerprinting process. The experimental simulation results demonstrate that the proposed approach can reduce the influence of noise and outliers in the environment on RSSI and reduce the localization error.