Work

Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network

Year: 2021

Type: conference-paper

Abstract: Multiple-input multiple-output (MIMO) is an enabling technology to meet the growing demand for faster and more reliable communications in wireless networks with a large number of terminals, but it can also be applied for position estimation of a terminal exploiting multipath propagation from multiple antennas. In this paper, we investigate new convolutional neural network (CNN) structures for exploiting MIMO-based channel state information (CSI) to improve indoor positioning. We evaluate and compare the performance of three variants of the proposed CNN structure to five NN structures proposed in the scientific literature using the same sets of training-evaluation data. The results demonstrate that the proposed residual convolutional NN structure improves the accuracy of position estimation and keeps the total number of weights lower than the published NN structures. The proposed CNN structure yields from 2 cm to 10 cm better position accuracy than known NN structures used as a reference.

Source: Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)

DOI: 10.1109/eucnc/6gsummit51104.2021.9482604

Authors: Gregor Cerar, Ales Svigelj, Mihael Mohorcic, Carolina Fortuna, Tomaz Javornik

Institutions: Jožef Stefan International Postgraduate School, Jožef Stefan Institute

Language: English

Topic: Indoor and Outdoor Localization Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Javna Agencija za Raziskovalno Dejavnost RS

Award: G3329947058