Title :
A Near-ML MIMO Subspace Detection Algorithm
Author :
Mansour, Mohamed M.
Author_Institution :
Dept. of Electr. & Comput. Eng., American Univ. of Beirut, Beirut, Lebanon
Abstract :
A low-complexity MIMO detection scheme is presented that decomposes a MIMO channel into multiple decoupled subsets of streams that can be detected separately. The scheme employs QL decomposition followed by elementary matrix operations to transform the channel matrix into a generalized elementary structure matching the subsets of streams to be detected. The proposed scheme avoids matrix inversion operations, and allows subsets to overlap thus achieving better diversity gain. Simulations demonstrate that this approach performs to within a few tenths of a dB from the optimum detection algorithm.
Keywords :
MIMO communication; matrix decomposition; maximum likelihood detection; set theory; telecommunication channels; MIMO channel decomposition; QL decomposition; channel matrix transform; diversity gain; elementary matrix operation; generalized elementary structure; matrix inversion operation avoidance; maximum likelihood detection; multiple decoupled stream subset; multiple-input multiple-output; near-ML MIMO subspace detection algorithm; optimum detection algorithm; Detection algorithms; Detectors; Interference; MIMO; Matrix decomposition; Signal processing algorithms; Vectors; Log-likelihood ratios (LLRs); MIMO detection; maximum likelihood (ML); subspace detection;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2014.2357991
