An iterative LR-aided MMSE extended soft MIMO decoding algorithm

This paper presents an iterative soft decision based lattice reduction (LR) aided Schnorr-Euchner (SE) multiple-input-multiple-output (MIMO) decoding algorithm, which reduces the gap in performance between suboptimal K-best and maximum likelihood (ML) detectors. Following IEEE 802.16e standard, we develop an iterative soft decoding algorithm for 4×4 MIMO with different modulation schemes. Using this method, we obtain 1.1 to 2.7 dB improvement over iterative soft decision based least sphere decoding (LSD) for different iterations. Then, using extensive simulation, we determine the optimum values for list size and saturation limit, which are the two governing parameters of our algorithm. Finally, we demonstrate that limiting the log likelihood ratio (LLR) values in LR-aided and LSD algorithm results in more than 8x reduction in list size as well as in the complexity of detectors and LLR calculation units.