Browsing by Author "Hsiao, Chang-Lung"

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MMSE Channel Factorization for Near Maximum-Likelihood Detection of MIMO Signals
(2008)
Kuo, Chih-Cheng
;
Sheen, Wern-Ho
;
Hsiao, Chang-Lung
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電信工程研究所
;
Institute of Communications Engineering
This paper investigates channel factorization-aided detection for MIMO (multiple-input, multiple-output) spatial multiplexing signals. In essence, this technique transforms, through channel factorization, the MIMO system into an equivalent one with a better-conditioned channel that is more suitable for low-complexity detection. The detected signal in the transform domain is then transformed back to the original domain and decision is made to the transmitted signal. Channel factorization-aided detector has recently become of great interest for MIMO systems because of its ability to cope with noise enhancement incurred by an ill-conditioned channel. Lattice-reduction-aided detector (LRAD) is such an example, where channel factorization is done with the Lenstra-Lenstra-Lovasz (LLL) or Seysen lattice-basis reduction algorithm. In this paper, a new minimum mean square error (MMSE)-based channel factorization is proposed. By minimizing mean square error in the transform domain, the proposed method can approach nearly to the performance of maximum likelihood detection and outperform the LLL factorization by up to 0.5dB, depending on the types of used low-complexity detectors and/or antennas numbers.
New Factorization Algorithms for Channel-Factorization Aided MMSE Receiver in MIMO Systems
(2011-01-01)
Kuo, Chih-Cheng
;
Sheen, Wern-Ho
;
Hsiao, Chang-Lung
;
電機工程學系
;
Department of Electrical and Computer Engineering
Channel-factorization aided detector (CFAD) is one of the important low-complexity detectors used in multiple input, multiple output (MIMO) receivers. Through channel factorization, this method transforms the original MIMO system into an equivalent system with a better-conditioned channel where detection is performed with a low-complexity detector; the estimate is then transferred back to the original system to obtain the final decision. Traditionally, the channel factorization is done with the lattice reduction algorithms such as the Lenstra-Lenstra-Lovasz (LLL) and Seysen's algorithms with no consideration of the low-complexity detector used. In this paper, we propose a different approach: the channel factorization is designed specifically for the minimum mean-square-error (MMSE) detector that is a popular low-complexity detector in CFADs. Two new types of factorization algorithms are proposed. Type-I is LLL based, where the well-known DLLL-extended algorithm. the LLL algorithm working on the dual matrix of the extended channel matrix, is a member of this type but with a higher complexity. DLLL-extended is the best-performed factorization algorithm found in the literature, Type-II is greedy-search based where its members are differentiated with different algorithm's parameters. Type-II algorithms can provide around 0.5-1.0 dB gain over Type-I algorithms and have a fixed computational complexity which is advantageous in hardware implementation.