Gaussian Process Regression for CSI and feedback estimation in LTE

With the constant increase in wireless handheld devices and the prospect of billions of connected machines one of the problems for future mobile networks, usually overlooked by the research community, is that more connected devices require proportionally more signalling overhead. Particularly, acquiring users' channel state information is necessary in order for the base station to assign frequency resources. Estimating this channel information with full resolution in frequency and in time is generally impossible and, thus, methods have to be implemented in order to reduce the overhead. In this paper, we propose a channel quality estimation method based on the concept of Gaussian Process Regression to predict users' channel states for varying user mobility profiles. Furthermore, we present a dual-control technique to determine which is the most appropriate prediction time for each user in order to keep the packet loss rate below a pre-defined threshold. The proposed dual-control technique is then analysed in a multicell network with proportional fair and maximum throughput scheduling mechanisms. Remarkably, it is shown that the presented approach allows for a reduction of the overall channel quality signalling by over 90% while keeping the packet loss below 5% with maximum throughput schedulers, as well as signalling reduction of 60% with proportional fair scheduling.