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.
|Title of host publication||2020 20th International Conference on Control, Automation and Systems, ICCAS 2020|
|Publisher||IEEE Computer Society|
|Number of pages||6|
|Publication status||Published - 2020 Oct 13|
|Event||20th International Conference on Control, Automation and Systems, ICCAS 2020 - Busan, Korea, Republic of|
Duration: 2020 Oct 13 → 2020 Oct 16
|Name||International Conference on Control, Automation and Systems|
|Conference||20th International Conference on Control, Automation and Systems, ICCAS 2020|
|Country||Korea, Republic of|
|Period||20/10/13 → 20/10/16|
Bibliographical notePublisher Copyright:
© 2020 Institute of Control, Robotics, and Systems - ICROS.
Copyright 2020 Elsevier B.V., All rights reserved.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering