In this paper, we propose a deep learning based algorithm to estimate the position of an user by utilizing reference signal received power (RSRP) and the location of base stations. To obtain reliable results in a real communication environment, parameters were measured using commercially available base stations and mobile phones within a LTE network. Since the structure of the measured data changes in accordance with the number of connected base stations, it is necessary to work on data uniformity processing before running the deep learning network. Therefore, we extract only the case in which three base stations are connected, using it as a feature of deep learning network. The experimental results reveal that the performance of the proposed algorithm is much better than that of the conventional fingerprint method. The average distance error is reduced from 71.04 meters for the fingerprint-based method to 43.51 meters for the proposed deep learning-based method.
|Title of host publication||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2018 Sep 10|
|Event||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada|
Duration: 2018 Apr 15 → 2018 Apr 20
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018|
|Period||18/4/15 → 18/4/20|
Bibliographical noteFunding Information:
This work was supported by the ICT R&D program of MSIT/IITP, Republic of Korea. [2017-0-00377-001, AI based wireless network quality monitoring system]
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering