Abstract
Iterative receiver structures that jointly perform channel estimation and decoding promise substantial performance gains. However, these gains only materialize with sufficiently accurate initial channel estimates. In this paper, initialization by multi-objective particle swarm optimization (MOPSO) is investigated. MOPSO supports low-complexity initial channel estimation with superimposed training symbols. Furthermore, it is shown that MOPSO works well in rank-deficient scenarios with arbitrary training sequences. Numerical results validate the performance enhancement of MOPSO initialization integrated within a graph-based iterative receiver.
Christopher Knievel
Professor for Autonomous Systems
My research interests include situation assessment, maneuver planning, and machine learning applied for (mobile) autonomous systems.