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The rapid emergence of 5G communications technology and standardization has seen an accelerated transfer of theoretical concepts to advanced development and implementation. Not only are 5G baseband signal processing algorithms becoming more important, but also the co-design and implementation of corresponding circuits, architectures, and platforms are becoming necessary due to rapid standardization...
In this paper, belief propagation (BP) detection based on max-sum (MS) algorithm for massive multiple-input multiple-output (MIMO) systems is therefore proposed to reduce computational complexity of general belief propagation. Owing to employing the approximation strategy, complexity reduction of MS is at the expense of detection performance loss. Based on MS algorithm, two effective approaches are...
Low-density parity-check (LDPC) coded massive multiple-input and multiple-output (MIMO) scheme is getting increasingly popular and sophisticated in today's wireless communication systems, since it can highly improve the spectral efficiency, data rates, and error performance. In this paper, a novel iterative detection and decoding (IDD) method for LDPC-coded massive MIMO systems is proposed. Based...
In massive multiple-input multiple-output (MIMO) mobile system, the computational complexity of signal detection increases exponentially along with the growing number of antennas. For example, the sub-optimal linear detection schemes, such as zero forcing (ZF) detector and minimum mean square error (MMSE) detector, always have to balance the performance and complexity resulted from the large-scale...
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...
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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