Low-Dimensionality of Noise-Free RSS and Its Application in Distributed Massive MIMO

Abstract

We examine the dimensionality of noise-free uplink received signal strength (RSS) in a distributed massive multiple-input multiple-output system. Upon applying principal component analysis to the noise-free uplink RSS data, we observe that it spans over a low-dimensional principal subspace. We make use of this unique property to propose a reconstruction-based Gaussian process regression (RecGP) method which predicts user locations from their uplink RSS. Considering noise-free RSS for training and noisy RSS for testing purposes, RecGP reconstructs the noisy test RSS from a low-dimensional principal subspace of the noise-free training RSS. This reconstruction allows RecGP to achieve lower prediction error than the standard Gaussian process regression method which directly uses the original test RSS for location prediction.