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Joint LMMSE and MAP channel estimation using BCRLB training signals for correlated two-way MIMO relay systems

机译:相关双向MIMO中继系统使用BCRLB训练信号的联合LMMSE和MAP信道估计

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In this paper, we propose a two-stage channel estimation scheme for two-way MIMO relay systems with a single relay antenna. We assume the training process is performed under correlated Rayleigh fading channels. At the first stage, the backward channel is estimated by using linear minimum mean square estimator (LMMSE). Based on the backward channel estimator, we propose a novel forward channel estimation scheme by using asymptotic maximum a posterior (MAP) method. Since Bayesian Cramér-rao Lower Bound (BCRLB) is more amenable to handle and has been widely used in practice, we seek BCRLB based on asymptotic likelihood function as the criterion for training signal design. Finally, the numerical results show that the proposed training signal can improve the MSE performance.
机译:在本文中,我们针对具有单个中继天线的双向MIMO中继系统提出了一种两阶段信道估计方案。我们假设训练过程是在相关的瑞利衰落信道下执行的。在第一阶段,通过使用线性最小均方估计器(LMMSE)估计后向信道。基于后向信道估计器,我们提出了一种新的前向信道估计方案,采用了渐近最大后验(MAP)方法。由于贝叶斯Cramér-rao下界(BCRLB)更易于处理并且已在实践中广泛使用,因此我们寻求基于渐近似然函数的BCRLB作为训练信号设计的标准。最后,数值结果表明,所提出的训练信号可以改善MSE性能。

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