Title :
Semi-blind iterative joint channel estimation and K-Best Sphere Decoding for MIMO
Author :
Dey, I. ; Messier, G.G. ; Magierowski, S. ; Sheng Chen
Author_Institution :
Sch. of Electr. & Comput. Eng., Univ. of Calgary, Calgary, AB, Canada
Abstract :
An efficient and high-performance semi-blind scheme is proposed for Multiple-Input Multiple-Output (MIMO) systems by iteratively combining channel estimation with K-Best Sphere Decoding (SD). To avoid the exponentially increasing complexity of Maximum Likelihood Detection (MLD) while achieving a near optimal MLD performance, K-best SD is considered to accomplish data detection. Semi-blind iterative estimation is adopted for identifying the MIMO channel matrix. Specifically, a training-based least squares channel estimate is initially provided to the K-best SD data detector, and the channel estimator and the data detector then iteratively exchange information to perform the decision-directed channel update and consequently to enhance the detection performance. The proposed scheme is capable of approaching the ideal detection performance obtained with the perfect MIMO channel state information.
Keywords :
MIMO communication; channel estimation; iterative decoding; least squares approximations; maximum likelihood detection; K-best sphere decoding; MIMO; channel estimation; channel estimator; channel state information; data detection; data detector; decision-directed channel update; maximum likelihood detection; multiple-input multiple-output systems; semi-blind iterative estimation; training-based least squares channel estimate; Channel estimation; Complexity theory; Decoding; Iterative decoding; Joints; MIMO; Training; K-Best Sphere Decoding; Multiple-input multiple-output; joint channel estimation and data detection;
Conference_Titel :
Communications, Computers and Signal Processing (PACRIM), 2013 IEEE Pacific Rim Conference on
Conference_Location :
Victoria, BC
DOI :
10.1109/PACRIM.2013.6625445