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In nowadays wireless communication systems, massive multiple-input multiple-output (MIMO) technique brings better energy efficiency and coverage but higher computational complexity than small-scale MIMO. For linear detection such as minimum mean square error (MMSE), prohibitive complexity lies in solving large-scale linear equations. For a better tradeoff between BER performance and computational...
In this paper, a novel, low-complexity, and hardware efficient signal detection algorithm and its corresponding VLSI architecture are proposed for massive multiple-input multiple-output (MIMO) systems. This method is based on the parallel Gauss-Seidel (PGS) iterative method, and achieves comparable detection performance as the linear minimum mean-square error (MMSE) detection. It successfully avoids...
In massive multiple-input multiple-output (MIMO) uplink, the minimum mean square error (MMSE) algorithm is near-optimal and linear, but still suffers from high-complexity of matrix inversion. Based on Gauss-Seidel (GS) method, an efficient architecture for massive MIMO soft-output detection is proposed in this paper. To further accelerate the convergence rate of the conventional GS method with acceptable...
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