Fingerprint Based mmWave Positioning System Aided by Reconfigurable Intelligent Surface

TL;DR

This paper introduces a novel mmWave positioning system using reconfigurable intelligent surfaces and a new fingerprint derived from space-time channel response vectors, employing a residual convolution network for 3D position estimation.

Contribution

It proposes a new fingerprint based on space-time channel response vectors and a residual convolution network for improved 3D positioning in RIS-assisted mmWave systems.

Findings

RCNR outperforms traditional CNN in accuracy

The proposed fingerprint effectively captures multipath characteristics

Simulation results validate the system's high precision

Abstract

Reconfigurable intelligent surface (RIS) is a promising technique for millimeter wave (mmWave) positioning systems. In this paper, we consider multiple mobile users (MUs) positioning problem in the multiple-input multiple-output (MIMO) time-division duplex (TDD) mmWave systems aided by the RIS. We derive the expression for the space-time channel response vector (STCRV) as a novel type of fingerprint. The STCRV consists of the multipath channel characteristics, e.g., time delay and angle of arrival (AOA), which is related to the position of the MU. By using the STCRV as input, we propose a novel residual convolution network regression (RCNR) learning algorithm to output the estimated three-dimensional (3D) position of the MU. Specifically, the RCNR learninng algorithm includes a data processing block to process the input STCRV, a normal convolution block to extract the features of STCRV, four residual convolution blocks to further extract the features and protect the integrity of the features, and a regression block to estimate the 3D position. Extensive simulation results are also presented to demonstrate that the proposed RCNR learning algorithm outperforms the traditional convolution neural network (CNN).