Hybrid Fingerprint-based Positioning in Cell-Free Massive MIMO Systems

TL;DR

This paper proposes a hybrid fingerprinting approach combining angle-of-arrival and received signal strength data in cell-free massive MIMO systems, using Gaussian process regression to improve user positioning accuracy.

Contribution

It introduces a novel hybrid fingerprinting method leveraging AOA and RSS data with GPR for enhanced positioning in 6G cell-free MIMO networks.

Findings

Hybrid fingerprinting outperforms RSS-only and AOA-only methods.

GPR with hybrid input achieves higher positioning accuracy.

Simulation results validate the effectiveness of the proposed approach.

Abstract

Recently, there has been an increasing interest in 6G technology for integrated sensing and communications, where positioning stands out as a key application. In the realm of 6G, cell-free massive multiple-input multiple-output (MIMO) systems, featuring distributed base stations equipped with a large number of antennas, present an abundant source of angle-of-arrival (AOA) information that could be exploited for positioning applications. In this paper we leverage this AOA information at the base stations using the multiple signal classification (MUSIC) algorithm, in conjunction with received signal strength (RSS) for positioning through Gaussian process regression (GPR). An AOA fingerprint database is constructed by capturing the angle data from multiple locations across the network area and is combined with RSS data from the same locations to form a hybrid fingerprint which is then used to train a GPR model employing a squared exponential kernel. The trained regression model is subsequently utilized to estimate the location of a user equipment. Simulations demonstrate that the GPR model with hybrid input achieves better positioning accuracy than traditional GPR models utilizing RSS-only and AOA-only inputs.