Abstract
We consider user positioning in distributed massive multiple-input multiple-output (MIMO) systems based on uplink received signal strength (RSS) recorded at the base station. We take a machine learning approach, wherein, we train a Gaussian process regression (GP) model with uplink RSS vectors for several known training user locations and use the trained GP model for predicting locations of test users from their uplink RSS vectors. When the training RSS is noise-free and the test RSS is noisy due to shadowing from the wireless channel, we observe that the conventional GP method provides unrealistically small 2σ error-bars on the predicted locations. To address this shortcoming, we propose NAGP — a numerical approximation based GP method which employs averaging over Monte-Carlo samples to improve the 2σ error-bar estimates. Simulation studies confirm the superior performance offered by the NAGP method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.