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• W2008297835 abstract "This paper addresses the problem of decoding in large-scale multiple-input–multiple-output (MIMO) systems. In this case, the optimal maximum-likelihood (ML) detector becomes impractical due to an exponential increase in the complexity with the signal and the constellation dimensions. This paper introduces an iterative decoding strategy with a tolerable complexity order. We consider a MIMO system with finite constellation and model it as a system with sparse signal sources. We propose an ML relaxed detector that minimizes the Euclidean distance with the received signal while preserving a constant <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=TeX>$ell_{1} $</tex-math></inline-formula> -norm of the decoded signal. We also show that the detection problem is equivalent to a convex optimization problem, which is solvable in polynomial time. Two applications are proposed, and simulation results illustrate the efficiency of the proposed detector." @default.
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• W2008297835 date "2015-08-01" @default.
• W2008297835 modified "2023-10-03" @default.
• W2008297835 title "New Iterative Detector of MIMO Transmission Using Sparse Decomposition" @default.
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• W2008297835 doi "https://doi.org/10.1109/tvt.2014.2360687" @default.
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