Neural Network-Based Ranging with LTE Channel Impulse Response for Localization in Indoor Environments

• Published in: International Conference on Control, Automation and Systems (Online) pp. 939 - 944
• Main Authors: Lee, Halim, Abdallah, Ali A., Park, Jongmin, Seo, Jiwon, Kassas, Zaher M.
• Format: Conference Proceeding
• Language: English
• Published: Institute of Control, Robotics, and Systems - ICROS 13.10.2020
• Subjects: Distance measurement; Global navigation satellite system; indoor localization; indoor navigation; long short-term memory (LSTM); Long Term Evolution; long-term evolution (LTE); OFDM; Receivers; recurrent neural network (RNN); Recurrent neural networks; Training
• ISSN: 2642-3901
• DOI: 10.23919/ICCAS50221.2020.9268386

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.