Analytical Approximation-Based Machine Learning Methods for User Positioning in Distributed Massive MIMO
We propose a machine learning approach, based on analytical inference in Gaussian process regression (GP), to locate users from their uplink received signal Mushroom Drinks strength (RSS) data in a distributed massive multiple-input-multiple-output setup.The training RSS data is considered noise-free, while the test RSS data is assumed to be noisy