2014
DOI: 10.1049/el.2014.0713
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Low‐complexity near‐optimal signal detection for uplink large‐scale MIMO systems

Abstract: Minimum mean square error (MMSE) signal detection algorithm is nearoptimal for uplink multi-user large-scale multiple input multiple output (MIMO) systems, but involves matrix inversion with high complexity. In this letter, we firstly prove that the MMSE filtering matrix for largescale MIMO is symmetric positive definite, based on which we propose a low-complexity near-optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion. The complexity can be reduced from O(K 3 … Show more

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Cited by 125 publications
(108 citation statements)
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“…Using only the first two terms of the Neumann series, the computational complexity is reduced from O(U 3 ) to O(U 2 ) [10]. However, the BER performance of the Neumann series approximation with two terms is considerably less than that of the exact matrix inversion [11], [13], [14]. For low-complexity and near-optimal signal detection of massive MIMO systems, stationary iterative methods have been proposed [11]- [17].…”
Section: Approximate Matrix Inversion and Iterative Signal Detection mentioning
confidence: 99%
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“…Using only the first two terms of the Neumann series, the computational complexity is reduced from O(U 3 ) to O(U 2 ) [10]. However, the BER performance of the Neumann series approximation with two terms is considerably less than that of the exact matrix inversion [11], [13], [14]. For low-complexity and near-optimal signal detection of massive MIMO systems, stationary iterative methods have been proposed [11]- [17].…”
Section: Approximate Matrix Inversion and Iterative Signal Detection mentioning
confidence: 99%
“…In stationary iterative methods, the U-dimensional transmitted symbol vector is approximated to a solution of the ith iteration. The iterative solution of the Richardson [11], [12], successive over-relation (SOR) [13], [14], and Jacobi-based [15] iterative methods are presented in the following:…”
Section: Approximate Matrix Inversion and Iterative Signal Detection mentioning
confidence: 99%
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