Home // International Journal On Advances in Internet Technology, volume 13, numbers 1 and 2, 2020 // View article
Authors:
Chong Liu
Hermann J. Helgert
Keywords: outdoor localization; machine learning; adaptive; beamforming.
Abstract:
Outdoor localization will become very essential in the development of 5G applications. Current localization techniques mainly relying on GPS and sensors 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 an improved adaptive BeamMaP that can instantaneously locate users in dynamic environment urban after training input data and steer the beams efficiently in a distributed massive Multiple-Input Multiple-Output (MIMO) system. We also design an adaptive algorithm to improve the performance of the model under the dynamic weather. To simulate a realistic environment, we evaluate the positioning accuracy with multiple 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) performances with increasing volume of training data, and the performances of RMSE are very close to the Bayesian Cramer-Rao bounds. We prove that our proposed positioning model is more efficiency and steadier compared with kNN and SVM in the dynamic weather conditions, and also demonstrate the effectiveness of the adaptive beamforming model in the online testing process.
Pages: 21 to 34
Copyright: Copyright (c) to authors, 2020. Used with permission.
Publication date: June 30, 2020
Published in: journal
ISSN: 1942-2652