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Semi-Blind Joint Timing-Offset and Channel Estimation for Amplify-and-Forward Two-Way Relaying

机译:用于放大和向前双向中继的半盲联同时偏移和信道估计

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In this paper, we consider the problem of joint timing-offset and channel estimation for amplify-and-forward (AF) two-way relay networks (TWRNs). This problem is solved for generic pulse-shaping filters, taking into account the filter truncation in practical communication and considering both pilot-based and semi-blind estimation strategies. Beginning with pilot-based estimation, we propose a novel Maximum-likelihood joint timing-offset and channel estimator, as well as an alternative estimator based on the special properties of Zadoff-Chu sequences. The first algorithm offers high accuracy, almost overlapping with the Cramer-Rao bound (CRB), while the second offers very low computational complexity. We then develop a semi-blind estimator based on the expectation maximization (EM) framework, exploiting the underlying Hidden Markov Model to apply Baum-Welch forward-backward recursion. The semi-blind CRB is also obtained as an indicator of the best achievable performance. Using simulations, we show that the semi-blind algorithm yields superior accuracy to pilot-based estimation, as well as improved symbol-error-rates and performs very close to the semi-blind CRB. Additionally, a low-complexity approximate EM algorithm is proposed for the case of rectangular pulses. Finally, we consider the possibility of errors in integer-offset estimation and propose pilot-based and semi-blind generalized likelihood ratio test (GLRT) schemes for correcting such errors.
机译:在本文中,我们考虑了扩增和向前(AF)双向继电器网络(TWRN)的联合时序偏移和信道估计的问题。对于通用脉冲整形滤波器,解决了该问题,考虑到实际通信中的滤波截断,并考虑到基于试点和半盲估计策略。从基于试点的估计开始,我们提出了一种新颖的最大似然联合时序偏移和信道估计,以及基于Zadoff-Chu序列的特殊特性的替代估计。第一算法提供高精度,几乎与Cramer-Rao绑定(CRB)重叠,而第二个提供了非常低的计算复杂性。然后,我们基于期望最大化(EM)框架来开发半盲估计,利用底层隐马尔可夫模型应用Baum-Welch前后递归。半盲CRB也被获得作为最佳性能的指标。使用仿真,我们表明半盲算法对基于试点的估计来说产生了卓越的准确性,以及改进的符号误差率,并且非常接近半盲CRB。另外,为矩形脉冲的情况提出了低复杂性近似EM算法。最后,我们考虑了整数偏移估计中错误的可能性,并提出了基于试验和半盲目广义似然比测试(GLRT)方案来校正这些错误。

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