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首页> 外文期刊>Eurasip Journal on Wireless Communications and Networking >Superimposed Training-Based Joint CFO and Channel Estimation for CP-OFDM Modulated Two-Way Relay Networks
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Superimposed Training-Based Joint CFO and Channel Estimation for CP-OFDM Modulated Two-Way Relay Networks

机译:CP-OFDM调制双向中继网络基于训练的叠加联合CFO和信道估计

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摘要

Joint carrier frequency offset (CFO) and channel estimation is considered for two-way relay networks (TWRNs). Existing estimators provide only the convolved channel parameters and the mixed CFO values. In contrast, estimators using a superimposed training strategy are developed for the individual frequency and channel parameters. Depending on the number of pilots, three different estimators are developed. An iterative estimator with low complexity is also developed to further improve the estimation accuracy. The Cramér-Rao Bounds (CRBs) are derived. The simulations show that the iterative estimator converges rapidly, and the resultant estimation mean square error (MSE) approaches the CRB. For the special case of small CFO between the two source terminals, the MSE achieves the CRB at high SNRs, and the iterative algorithm is not necessary. However, for the general case, the gap between the MSE and the CRB indicates that there is room for further improvement of the estimation accuracy.
机译:对于双向中继网络(TWRN),考虑了联合载波频率偏移(CFO)和信道估计。现有的估算器仅提供卷积的信道参数和混合的CFO值。相反,针对单个频率和信道参数开发了使用叠加训练策略的估计器。根据飞行员的数量,开发了三种不同的估算器。还开发了低复杂度的迭代估计器,以进一步提高估计精度。得出Cramér-Rao界限(CRB)。仿真结果表明,迭代估计器收敛迅速,估计的均方误差(MSE)接近CRB。对于两个源终端之间的CFO较小的特殊情况,MSE可以在高SNR时获得CRB,因此不需要迭代算法。但是,对于一般情况,MSE和CRB之间的差距表明存在进一步提高估计精度的空间。

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