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

TL;DR

This paper introduces a new residual convolutional neural network architecture that enhances indoor positioning accuracy using MIMO-based CSI data, outperforming existing neural network models by 2 to 10 centimeters.

Contribution

The paper proposes a novel residual CNN structure for MIMO CSI-based indoor positioning, achieving higher accuracy with fewer weights compared to prior neural network models.

Findings

Residual CNN improves position accuracy by 2-10cm.

Proposed CNN reduces total number of weights.

Outperforms five existing neural network structures.

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 2cm to 10cm better position accuracy than known NN structures used as a reference.