Doubly-Selective Channel Estimation Using Superimposed Training and Weighted First-Order Statistics

• Published in: Wireless personal communications Vol. 73; no. 3; pp. 767 - 778
• Main Authors: Dou, Gaoqi, Zhang, Xianfeng, He, Chunquan, Gao, Jun
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.12.2013; Springer
• Subjects: Applied sciences; Channels; Communications Engineering; Computer Communication Networks; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Engineering; Exact sciences and technology; Networks; Radiocommunications; Signal,Image and Speech Processing; Software; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Transmission and modulation (techniques and equipments); Superimposed training; First-order statistics; Channel estimation; Doubly selective channel; Basis expansion model; Fading channels; Invariant; Averaging method; Statistical analysis; Probabilistic approach; Symbol; Competitiveness; Interference; Transmission channel; Order statistic; Time division multiplexing; Iterative method; Modeling; Time varying system; Symbol error rate
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-013-1215-8

Doubly-selective channel estimation using superimposed training and complex exponential basis expansion model is considered. By taking a weighted averaging operation of the received data, a weighted first-order statistical estimator is proposed, where the time-varying channel estimation is reduced to the simple average-based solution of time-invariant coefficients and the dominant effect of information-induced interference on channel estimation can be suppressed. To further improve the estimation performance with a limited training power, a joint iterative channel estimation and symbol detection scheme is developed where the detected symbol is exploited to enhance estimation performance instead of being viewed as interference. Theoretical analysis and simulation results show that the proposed scheme is superior to data-dependent superimposed training scheme and competitive with the conventional time-multiplexed training in terms of symbol error rate over doubly-selective channels.