DocumentCode
137126
Title
Block-equal QRS decomposition of mimo channels with ML-based block successive cancellation detection
Author
Dan Fang ; Anzhong Wong ; Jian-Kang Zhang ; Kon Max Wong
Author_Institution
Dept. of Electr. & Comput. Eng., McMaster Univ., Hamilton, ON, Canada
fYear
2014
fDate
22-25 June 2014
Firstpage
35
Lastpage
39
Abstract
The multiple-input and multiple-output (MIMO) channel model is very useful for the presentation of a wide range of wireless communication systems. This paper addresses the joint design of a precoder and a receiver for a point-to-point MIMO channel model in a scenario in which perfect channel state information (CSI) is available at both ends. We develop a novel framework for the dual transmission-reception process. Under the proposed framework, the receiver decomposes the channel matrix by using a block QR decomposition, where Q is a unitary matrix and R is a block upper triangular matrix. The optimal maximum likelihood (ML) detec- tion process is employed within each diagonal block of R. Then, the detected block of symbols is substituted and subtracted sequentially according to the block QR decomposition based successive cancellation. On the transmitting end, the expression of probability of error based on ML detection is chosen as the design criterion to formulate the precoder design problem. This paper presents a design of MIMO transceivers in the particular case of having 4 transmitting and 4 receiving antennas with full CSI knowledge on both sides. In addition, a closed-form expression for the optimal precoder matrix is obtained for channels satisfying certain conditions.
Keywords
MIMO communication; matrix algebra; maximum likelihood detection; precoding; radio transceivers; receiving antennas; transmitting antennas; CSI knowledge; MIMO transceivers; ML-based block successive cancellation detection; block upper triangular matrix; block-equal QRS decomposition; channel matrix; closed-form expression; design criterion; dual transmission-reception process; multiple-input and multiple-output channel model; optimal maximum likelihood detection process; optimal precoder matrix; perfect channel state information; point-to-point channel model; precoder design problem; receiving antennas; transmitting antennas; unitary matrix; wireless communication systems; Decision feedback equalizers; Detectors; MIMO; Matrix decomposition; Receivers; Wireless communication;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Advances in Wireless Communications (SPAWC), 2014 IEEE 15th International Workshop on
Conference_Location
Toronto, ON
Type
conf
DOI
10.1109/SPAWC.2014.6941312
Filename
6941312
Link To Document