Bias Optimization for List-Sequential Detection in MIMO Systems

Iterative reception involves the computation of high quality log-likelihood ratios (LLR) to run efficiently, but the computation complexity of optimal a posteriori probabilities (APP) is exponential in the multiple-input multiple-output (MIMO) system dimensions. The list-sequential (LISS) detector achieves near- optimal performance by searching for a list of best candidates inside a tree modelizing the system. As this decoder remains complex, we propose here to modify its cost metric by adding a positive bias term, which accelerates the decoding algorithm. A good optimization of the bias value results in an important drop-off of the complexity while preserving the performance of the classical LISS decoder.