Partial Data-Dependent Superimposed Training Based Iterative Channel Estimation for OFDM Systems over Doubly Selective Channels

被引：0

作者：

He, Lanlan ^{[1
]}

Ma, Shaodan ^{[1
]}

Wu, Yik-Chung ^{[1
]}

Ng, Tung-Sang ^{[1
]}

机构：

[1] Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Hong Kong, Peoples R China

来源：

2010 IEEE GLOBAL TELECOMMUNICATIONS CONFERENCE GLOBECOM 2010 | 2010年

关键词：

Superimposed training; Channel estimation; Orthogonal frequency division multiplexing (OFDM);

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, partial data-dependent superimposed training based channel estimation for OFDM systems over doubly selective channels (DSCs) is addressed. Due to the presence of unknown data as interference, we first derive a minimum mean square error (MMSE) channel estimator by treating the effect of unknown data as noise. To further improve the performance, a novel iterative algorithm which jointly estimates channel and suppresses interference from data is proposed via variational inference approach. Simulation results show that the proposed algorithm converges after a few iterations. Furthermore, after convergence, the performance of the proposed channel estimator is very close to that with full training at high SNRs.

引用

页数：5

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