Time Varying Channel Estimation for DSTC-Based Relay Networks: Tracking, Smoothing and BCRBs
Time Varying Channel Estimation for DSTC-Based Relay Networks: Tracking, Smoothing and BCRBs
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DOI：
10.1109/twc.2015.2431672
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发表时间：
2015-05
影响因子：
10.4
通讯作者：
Shun Zhang;F. Gao;Jiandong Li;Hongyan Li
中科院分区：
文献类型：
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作者：
Shun Zhang;F. Gao;Jiandong Li;Hongyan Li
本文研究了在时间选择性平坦衰落情况下，在中继节点采用分布式空时编码（DSTC）的单向放大转发中继网络（ownn）中的信道估计问题。与大多数现有工作不同的是，我们的目标是估计和跟踪每个中继跳的单个信道，而不是复合信道。为了减少需要估计的信道参数的数量，我们采用多项式基展开模型（P-BEM）并将问题转化为估计每个中继跳的信道系数向量（称为in- bem - cv）。借助自回归（AR）模型，建立了in-BEM-CV估计的动态状态空间。具体来说，我们采用无气味卡尔曼滤波器（unscented Kalman filter， UKF）向前跟踪in- bem - cv的动态变化，并利用无气味Rauch-Tung-Striebel平滑器（URTSS）向后平滑UKF的估计。为了使研究更加完整，我们还推导了in-BEM-CV估计的贝叶斯cram<s:1>下限（BCRBs）。最后，给出了数值结果来证实所提出的研究。
In this paper, we examine the channel estimation in an amplify-and-forward (AF) one-way relay network (OWRN) under time selective flat fading scenario, where the distributed space-time coding (DSTC) is adopted at relay nodes. Different from most existing works, our target is to estimate and track the individual channels of each relay hop instead of the composite channels. To reduce the number of the channel parameters to be estimated, we apply the polynomial basis-expansion-model (P-BEM) and convert the problem to estimating the channel coefficient-vectors (called in-BEM-CVs) of each relay hop. With the aid of the autoregressive (AR) model, we formulate the dynamic state space for the in-BEM-CV estimation. Specifically, we adopt the unscented Kalman filter (UKF) to track the in-BEM-CV dynamic variations in an forward manner, and utilize the unscented Rauch-Tung-Striebel smoother (URTSS) to smooth the UKF's estimations in an backward manner. To make the study complete, we also derive Bayesian Cramér lower bounds (BCRBs) for the in-BEM-CV estimation. Finally, numerical results are provided to corroborate the proposed studies.