Paper Info
Title
MMSE-based lattice-reduction-aided fixed-complexity sphere decoder for low-complexity near-ML MIMO detection
Abstract
In this paper, we propose a minimum-mean-squared-error (MMSE)-based lattice-reduction (LR)-aided fixed-complexity sphere decoder (FSD) for low-complexity near-maximum-likelihood (near-ML) multiple-input multiple-output (MIMO) detection. In order for the FSD to achieve optimal performance, the number of full expansion (FE) stages should be sufficient, which is the major cause of the increase in the computational complexity when either a large signal constellation or a large number of antennas are adopted. However, the proposed algorithm maintains the near-ML performance with the aid of the MMSE-based LR algorithm while reducing the number of FE stages. Although there exists the increase in the computational complexity for the application of the additional processing elements, the decrease in the number of FE stages results in the lower computational complexity of the overall algorithm. The numerical analysis demonstrates that there is a considerable decrease in the computational complexity while the performance degradation is negligible, compared to the optimal FSD.
Year
DOI
Venue
2015
10.1109/LANMAN.2015.7114716
LANMAN
Keywords
Field
DocType
lattice reduction (LR), fixed-complexity sphere decoder (FSD), multiple-input multiple-output (MIMO)
Computer science,Algorithm,MIMO,Constellation diagram,Decoding methods,Numerical analysis,Lattice reduction,Computational complexity theory,Distributed computing,Bit error rate
Conference
ISSN
Citations 
PageRank 
1944-0375
0
0.34
References 
Authors
12
3
Name
Order
Citations
PageRank
hyunsub kim142.43
Hyukyeon Lee222.06
Jaeseok Kim340558.33