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BeamMaP: Beamforming-based Machine Learning for Positioning in Massive MIMO Systems

Authors:
Chong Liu
Hermann J. Helgert

Keywords: outdoor localization; machine learning; data training; beamforming.

Abstract:
Abstract—Existing positioning techniques can mostly overcome problems caused by path loss, background noise and Doppler effects, but multiple paths in complex indoor or outdoor environments present additional challenges. In this paper, we propose BeamMaP that can instantaneously locate users after training input data and steer the beams efficiently in a distributed massive Multiple-Input Multiple-Output (MIMO) system. To simulate a realistic environment, we evaluate the positioning accuracy with channel fingerprints collected from uplink Received Signal Strength (RSS) data, including Line-of-Sight (LoS) and Non-Line- of-Sight (NLoS), in the training data sets. Based on the adaptive beamforming, we employ the Rice distribution to sample the current mobile users locations in the testing data sets. Our simulation results achieve Reduced Root-Mean-Squared Estimation Error (RMSE) performance with increasing volume of training data. We prove our proposed model is more efficiency and steady in the positioning system compared with kNN and SVM. The results also demonstrate the effectiveness of the adaptive beamforming model in the testing process.

Pages: 16 to 23

Copyright: Copyright (c) IARIA, 2019

Publication date: June 30, 2019

Published in: conference

ISSN: 2308-443X

ISBN: 978-1-61208-721-4

Location: Rome, Italy

Dates: from June 30, 2019 to July 4, 2019