A Weighted First-Order Statistical Method for Time-Varying Channel and DC-offset Estimation Using Superimposed Training

作者：Dou Gaoqi^*; He Chunquan; Li Congying; Gao Jun

来源：IEEE Communications Letters, 2013, 17(5): 852-855.

DOI：10.1109/LCOMM.2013.022813.122340

• Time-varying channel estimation
• basis expansion models
• weighted first-order statistics
• superimposed training

Time-varying channel and dc-offset estimation using superimposed training and first-order statistics are considered. A weighted first-order statistics-based estimator using complex exponential basis expansion model (CE-BEM) is proposed, which explicitly exploits the cyclostationary characteristic of periodic training sequence and extends to time-varying channel estimation. By subtracting the cyclic mean from each data block, only partial unknown data interference is removed to make a tradeoff between interference cancellation and symbol recovery. A theoretical performance analysis is presented. Simulation results show that the proposed scheme has low computational complexity and exhibits good performance in terms of the symbol error rate.

• 出版日期2013-5
• 单位中国人民解放军海军工程大学