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Space-Time Semi-Blind Equalizer for Dispersive QAM MIMO System Based on Modified Newton Method

机译:基于修正牛顿法的色散QAM MIMO系统时空半盲均衡器

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摘要

This paper proposes a space-time semi-blind equalizer (ST-SBE) for dispersive multiple-input multiple-output (MIMO) communication systems that employ high throughput quadrature amplitude modulation (QAM) signals. A novel cost function (CF) that integrates multimodulus algorithm (MMA) with soft decision-directed (SDD) scheme is established to efficiently obtain the weight vector associated with the ST-SBE. In the ST-SBE, a very short training sequence is used to provide a rough initial least squares estimate of the weight vector. An efficient modified Newton method (MNM) for minimizing the established cost function is proposed to fast search the optimal weight vector. Very interestingly, we prove that the proposed MNM has the same quadratic order of convergence as Newton methods. In addition, the proposed MNM has much lower computational complexity than Newton methods. Simulation results are provided to demonstrate that the ST-SBE has better performances than the gradient-Newton (GN)-based concurrent constant modulus algorithm (CMA) with SDD scheme (GN-CMA+SDD).
机译:本文提出了一种时空半盲均衡器(ST-SBE),用于采用高吞吐量正交幅度调制(QAM)信号的色散多输入多输出(MIMO)通信系统。建立了一种将多模算法(MMA)与软决策导向(SDD)方案相集成的新型成本函数(CF),以有效地获得与ST-SBE相关的权向量。在ST-SBE中,非常短的训练序列用于提供权重向量的粗略的初始最小二乘估计。为了快速搜索最优权向量,提出了一种有效的改进牛顿法(MNM),以最小化已建立的成本函数。非常有趣的是,我们证明了所提出的MNM具有与牛顿法相同的二次收敛级。此外,所提出的MNM的计算复杂度比牛顿法低得多。仿真结果表明,ST-SBE的性能优于带有SDD方案的基于梯度牛顿(GN)的并发恒模算法(CMA)(GN-CMA + SDD)。

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