Fingerprinting Localization Method Based on Clustering and Gaussian Process Regression in Distributed Massive MIMO Systems

Abstract

Fingerprinting (FP) localization methods are used in massive multiple-input multiple-output (MIMO) systems due to their high reliability and accuracy. The Gaussian process regression (GPR) method could potentially be used, as an FP-based localization method, in a massive MIMO system to provide high accuracy. However, it is limited by high complexity, especially in a large-scale environment. In this paper, we propose an FP-based localization method, using affinity propagation (AP) clustering and Gaussian process regression (GPR) to estimate user’s location in a distributed massive MIMO system based on the uplink received signal strength (RSS) vectors. First, the training RSS vectors are clustered using the AP algorithm to reduce the computational complexity. Then, the data distribution within each cluster is accurately modeled using GPR to provide excellent support for further positioning. Simulation studies reveal that the proposed method improves root-mean-squared estimation error (RMSE) performance significantly by reducing the location estimation error compared to using only GPR for all training RSS data. Also, it reduces the computational complexity of using GPR.