Abstract
Nowadays, message combining is an essential component in most digital communication systems. Correlation between random measures has a significant impact on the combining process. In order to provide the best estimate after combining, correlation must be considered. In many applications correlation is obvious, e.g. correlation in the time, frequency, and/or spatial domain of a radio channel. In other cases, correlation is more concealed. In this paper, two methods to combine correlated random values are presented and applied to a graph-based iterative receiver. It is explained, why correlation in the message exchange arises and how it can be taken into account in the message combining step. Simulation results are provided showing the performance gains when correlation is considered.
Christopher Knievel
Professor for Autonomous Systems
My research interests include situation assessment, maneuver planning, and machine learning applied for (mobile) autonomous systems.