ML and MAP channel estimation for distributed one-way relay networks with orthogonal training
ML and MAP channel estimation for distributed one-way relay networks with orthogonal training
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DOI：
10.1109/cc.2015.7385531
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发表时间：
2015
影响因子：
4.1
通讯作者：
Yao Chenhong;Zhang Shun;Pei Changxing
中科院分区：
文献类型：
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作者：
Yao Chenhong;Zhang Shun;Pei Changxing
在这篇文章中，我们研究了叠加训练框架下基于分布式时空编码（DSTC）的经典单向中继网络（own）的单个信道估计。我们没有像传统工作那样求助于复合信道估计，而是直接从最大似然（ML）和最大后验（MAP）估计器中估计单个信道。利用正交训练设计得到了闭型ML估计量。由于MAP信道内估计器结构复杂，我们设计了一个迭代梯度下降估计过程来寻找最优解。数值结果证实了我们的研究。
In this letter, we investigate the individual channel estimation for the classical distributed-space-time-coding (DSTC) based one-way relay network (OWRN) under the superimposed training framework. Without resorting to the composite channel estimation, as did in traditional work, we directly estimate the individual channels from the maximum likelihood (ML) and the maximum a posteriori (MAP) estimators. We derive the closed-form ML estimators with the orthogonal training designing. Due to the complicated structure of the MAP in-channel estimator, we design an iterative gradient descent estimation process to find the optimal solutions. Numerical results are provided to corroborate our studies.