Neural Network-Based Ranging with LTE Channel Impulse Response for Localization in Indoor Environments
Abstract
A neural network (NN)-based approach for indoor localization via cellular long-term evolution (LTE) signals is proposed. The approach estimates, from the channel impulse response (CIR), the range between an LTE eNodeB and a receiver. A software-defined radio (SDR) extracts the CIR, which is fed to a long short-term memory model (LSTM) recurrent neural network (RNN) to estimate the range. Experimental results are presented comparing the proposed approach against a baseline RNN without LSTM. The results show a receiver navigating for 100 m in an indoor environment, while receiving signals from one LTE eNodeB. The ranging root-mean squared error (RMSE) and ranging maximum error along the receiver's trajectory were reduced from 13.11 m and 55.68 m, respectively, in the baseline RNN to 9.02 m and 27.40 m, respectively, with the proposed RNN-LSTM.
Keywords
Cite
@article{arxiv.2009.11907,
title = {Neural Network-Based Ranging with LTE Channel Impulse Response for Localization in Indoor Environments},
author = {Halim Lee and Ali A. Abdallah and Jongmin Park and Jiwon Seo and Zaher M. Kassas},
journal= {arXiv preprint arXiv:2009.11907},
year = {2020}
}
Comments
Submitted to ICCAS 2020