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

TL;DR

This paper introduces a neural network method utilizing LTE channel impulse response data for accurate indoor localization, significantly improving range estimation accuracy over baseline models.

Contribution

It presents a novel LSTM-based neural network approach for LTE signal-based indoor ranging, demonstrating enhanced accuracy over traditional RNN methods.

Findings

Ranging RMSE reduced from 13.11 m to 9.02 m

Maximum ranging error decreased from 55.68 m to 27.40 m

Effective indoor localization over 100 m using LTE signals

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.