Year: 2025
Type: article
Abstract: ABSTRACT In recent years, indoor localisation based on channel state information (CSI) fingerprint has been actively researched because of the rapid growth of the Internet of Things (IoT). In addition, various deep learning (DL) methods such as deep neural networks (DNN) and convolutional neural networks (CNN) have been widely discussed for the indoor localisation. The CSI‐fingerprint can be produced by continuous and quantised values. For the CSI‐fingerprint using quantised values, good performance is achieved. However, since quantised data for the optimal level has not been sufficiently discussed, the best performance of quantisation is not indicated. Therefore, in this paper, we propose an effective quantised CSI‐fingerprint for DL‐based indoor localisation.
Source: IET Wireless Sensor Systems
DOI: 10.1049/wss2.70003
Institutions: Yamaguchi University, Kagoshima University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,310
Awards: Evaluation and Implementation of Parallel Optical Wireless Communication System Resistant to Beam Spreading, Signal Construction by Time-Frequency Channel Learning for Multi-Relay Communications, Highly efficient channel estimation by multi-dimensional signal processing for massive connectivity, Terminal collaboration technology that both improves energy efficiency and communication performance
OpenAlex ID: W4409309581