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ROD-based hybrid TH precoding and combining for mmWave large-scale MIMO systems

机译:基于棒的混合动力研讨会,用于MMWAVE大型MIMO系统的组合

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Hybrid precoding is one of key techniques for millimeter wave (mmWave) large-scale multiple-input multiple-output (MIMO) systems. This paper considers a nonlinear hybrid precoding architecture which consists of a nonlinear unit, a reductive digital precoder and a constant modulus radio frequency (RF) precoder, and presents a novel hybrid Tomlinson-Harashima (TH) precoding and combining algorithm. Firstly, due to the intractability of the sum rates maximization problem for such a nonlinear hybrid precoding architecture, a tractable three-stage optimization problem is constructed through the lower bound of the sum rates, which allows the digital precoding matrix, the RF precoding matrix and the RF combining matrix to be optimized sequentially and independently. Then, in order to solve the three-stage optimization problem effectively, a novel row orthogonal decomposition (ROD) is defined. Based on the ROD, it is interesting that the necessary and sufficient condition of the optimal digital precoding matrix can be obtained, and a near-optimal RF precoding matrix can be derived. Finally, the optimization of the RF combining matrix is reformulated as a unimodular quadratic programming and solved by a generalized power method. Theoretical analyses and simulations indicate that the proposed ROD-based hybrid TH precoding and combining algorithm can offer a higher sum rates and a lower bit error rate with a comparable complexity in comparison to the previous works. (C) 2019 Elsevier Inc. All rights reserved.
机译:混合预编码是毫米波(MMWAVE)大规模多输入多输出(MIMO)系统的关键技术之一。本文考虑了非线性混合预编码架构,其包括非线性单元,还原数字预编码器和恒定模量射频(RF)预编码器,并提出了一种新颖的混合Tomlinson-Harashima(Th)预编码和组合算法。首先,由于这种非线性混合预编码架构的总和率的最大化问题的诡计,通过总和速率的下限构成了易于的三级优化问题,这允许数字预编码矩阵,RF预编码矩阵和RF组合矩阵以顺序且独立地优化。然后,为了有效地解决三阶段优化问题,定义了一种新颖的行正交分解(杆)。基于杆,有趣的是,可以获得最佳数字预编码矩阵的必要和充分条件,并且可以导出近最佳RF预编码矩阵。最后,RF组合矩阵的优化被重新重整为单致二次编程,并通过广义功率方法解决。理论分析和仿真表明,所提出的基于杆的混合动力Th预编码和组合算法可以提供更高的总和速率和较低的误码率,与先前的作品相比,具有相当的复杂性。 (c)2019 Elsevier Inc.保留所有权利。

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