Channel Estimation via Loss Field: Accurate Site-Trained Modeling for Shadowing Prediction

Future networks which share spectrum dynamically among groups of mobile users will require fast and accurate channel estimation in order to guarantee signal-to-interference-plus-noise ratio (SINR) requirements for co-channel links. There is a need for channel models with low computational complexity and high accuracy that adapt to the particular area of deployment while preserving explainability. We propose the Channel Estimation via Loss Field (CELF) model, which uses channel loss measurements from a deployed network and a Bayesian linear regression method to estimate a site-specific loss field for the area. The loss field is explainable as a site map of additional radio ‘shadowing’, compared to a base channel model, but it requires no site-specific terrain or building information. For an arbitrary pair of transmitter and receiver positions, CELF sums the loss field near the link line to estimate its shadowing loss. We use extensive measurements to show that CELF lowers the variance of channel estimates by up to 56% compared to the path loss exponent model, and outperforms 3 popular machine learning methods in variance reduction and training efficiency. CELF offers a new type of explainable learning model for accurate and fast site-specific radio channel loss estimation.