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.
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页数：5
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