Abstract
The principles and benefits of soft decisions are well known and widely applied. The advantage of knowing that a decision is reliable or not is obvious. Belief propagation within a factor graph enables a unified use of soft information for both channel estimation and data detection. However, if soft information does not reflect the true reliability of a decision, the achievable performance may degrade. In this paper, the calculation of reliability information is refined to consider the event of unreliable soft decisions. The proposed solution is based on the mean bit error probability calculated after each iteration and integrates nicely within the existing factor graph. Simulation results are provided to illustrate the performance gains.
Christopher Knievel
Professor for Autonomous Systems
My research interests include situation assessment, maneuver planning, and machine learning applied for (mobile) autonomous systems.