Abstract—This paper presents a performance analysis of soft decision based Lattice Reduction (LR) aided Schnorr-Euchner (SE) multiple-input-multiple-output (MIMO) detection algorithm with that of a hard one. We develop an advanced soft decoding scheme, for 4×4 MIMO with different modulation schemes and it provides 1.3 to 1.5 dB improvement compared to the LR-aided hard decision based detection method. The optimum parameters for K and saturation limit are further derived using extensive simulation. In the conventional K-best algorithm limiting the LLR values is not beneficiary in reducing the optimum size of candidate list, whereas applying the saturation limit on the LLR values in LR-aided algorithm will result in more than 8x reduction in list size as well as in the complexity of detector and LLR calculation unit.

Index Terms—MIMO, soft decoding, lattice reduction, and k-best detector.

The authors are with the Department of Electrical and Computer Science, Texas A&M University, College Station, TX 77840, USA (e-mail: mehnaz@tamu.edu, ehsanrohani@tamu.edu, xujw@tamu.edu, gchoi@ece.tamu.edu).

Cite:Mehnaz Rahman, Ehsan Rohani, Jingwei Xu, and Gwan S. Choi, "An Improved Soft Decision Based MIMO Detection Using Lattice Reduction," International Journal of Computer and Communication Engineering vol. 3, no. 4, pp. 264-268, 2014.