Abstract
In most modern wireless systems, adaptive modulation and channel coding is applied to achieve a high spectral efficiency. Hence, suitable receivers need to support a large variety of modulation and channel coding schemes (MCS) while maintaining a low complexity. The performance and complexity of a multi-dimensional factor-graph based (MD-GSIR) framework for iterative joint channel estimation and data detection is investigated for the application of adaptive modulation and coding in an LTE environment. Three detection algorithms within the MD-GSIR framework are investigated and a novel tree-based detection is proposed. The versatile structure of the MD-GSIR concept offers a flexible trade-off between computational complexity and performance. Furthermore, the message exchange is adapted to support the irregular training structure which may occur in adaptive environments.
Christopher Knievel
Professor for Autonomous Systems
My research interests include situation assessment, maneuver planning, and machine learning applied for (mobile) autonomous systems.