Correlation Discovery and Channel Prediction in Mobile Networks: A Revisiting to Gaussian Process

With accurate knowledge of future Channel State Information (CSI), it becomes possible to better comprehend the radio propagating environment and manipulate the wireless resources in a proactive manner, so as to provide solid support to smart and high quality wireless transmission. However, in mobile environment, the evolving correlation patterns in CSI series challenge the existing data-driven algorithms to adaptively learn and predict its behavior. In this article, an adaptive learning algorithm is proposed based on Gaussian Process (GP), to discover and utilize the spatial correlation within a channel and across channels, and produce accurate CSI prediction. Specifically, 1). To track the evolving correlation of a channel, we tailor Spectrum Mixture (SM) kernel to not only approximate the optimal kernel adapting to the current CSI, but also capture the combined effect of path loss and User Equipment (UE) motion. 2). The correlation across channels is encoded into the GP-based learning framework through Linear Model of Co-regionalization (LMC). Finally, we verify the performance improvements through simulation.