Online prediction of spatial fields for radio-frequency communication

摘要

In this paper we predict spatial wireless channel characteristics using a stochastic model that takes into account both distance dependent pathloss and random spatial variation due to fading. This information is valuable for resource allocation, interference management, design in wireless communication systems. The spatial field model is trained using a convex covariance-based learning method which can be implemented online. The resulting joint learning and prediction method is suitable for large-scale or streaming data. The online method is first demonstrated on a synthetic dataset which models pathloss and medium-scale fading. We compare the method with a state-of-the-art scalable batch method. It is subsequently tested in a real dataset to capture small-scale variations.