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We examine the dimensionality of noise-free uplink received signal strength(RSS) data in a distributed multiuser massive multiple-input multiple-outputsystem. Specifically, we apply principal component analysis to the noise-freeuplink RSS and observe that it has a low-dimensional principal subspace. Wemake use of this unique property to propose RecGP - a reconstruction-basedGaussian process regression (GP) method which predicts user locations fromuplink RSS data. Considering noise-free RSS for training and noisy test RSS forlocation prediction, RecGP reconstructs the noisy test RSS from a low-dimensional principal subspace of the noise-free training RSS. Thereconstructed RSS is input to a trained GP model for location prediction. Noisereduction facilitated by the reconstruction step allows RecGP to achieve lowerprediction error than standard GP methods which directly use the test RSS forlocation prediction.

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