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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...
Massive Multiple-Input Multiple-Output (MIMO) is one of the key technologies in the fifth generation (5G) wireless communication for much higher throughput. However, current detection algorithms for massive MIMO suffer from large computational complexity. The Neumann series based approximated matrix inverse is a good tradeoff between detection performance and computational complexity. In this paper,...
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...
Affected by relaxation factor ω, for large-scale MIMO uplink, successive over relaxation (SOR) detection involves low-complexity matrix inversion but unstable performance as ω changes. In this paper, a more stable and efficient SOR-based detection, which is nearly unaffected by ω, is proposed. First, the convergence of the proposed method is proved. Both analytic and numerical results have shown that,...
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...
In massive multiple-input multiple-output (MIMO) systems, linear minimum mean square error (MMSE) detection is near-optimal but involves large dimensional matrix inversion, which results in high complexity. To this end, Neumann series expansion (NSE) approximation, which avoids the direct computation of the matrix inversion, is recently investigated due to its low implementation complexity. Unfortunately,...
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