Paper Info
Title
Doubly selective channel estimation using superimposed training and exponential bases models
Abstract
Channel estimation for single-input multiple-output (SIMO) frequency-selective time-varying channels is considered using superimposed training. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). A periodic (nonrandom) training sequence is arithmetically added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission. A two-step approach is adopted where in the first step we estimate the channel using CE-BEM and only the first-order statistics of the data. Using the estimated channel from the first step, a Viterbi detector is used to estimate the information sequence. In the second step, a deterministic maximum-likelihood (DML) approach is used to iteratively estimate the SIMO channel and the information sequences sequentially, based on CE-BEM. Three illustrative computer simulation examples are presented including two where a frequency-selective channel is randomly generated with different Doppler spreads via Jakes' model.
Year
DOI
Venue
2006
10.1155/ASP/2006/85303
EURASIP J. Adv. Sig. Proc.
Keywords
Field
DocType
estimated channel,simo channel,time-varying channel,information sequence,frequency-selective time-varying channel,exponential bases model,doubly selective channel estimation,two-step approach,channel estimation,information sequences sequentially,training sequence,frequency-selective channel
Telecommunications,Computer science,Modulation,Artificial intelligence,Estimation theory,Order statistic,Quantum information,Deterministic system (philosophy),Computer vision,Exponential function,Iterative method,Communication channel,Algorithm
Journal
Volume
Issue
ISSN
2006,
1
1687-6180
Citations 
PageRank 
References 
5
0.51
14
Authors
3
Name
Order
Citations
PageRank
Jitendra K. Tugnait1444130.56
Xiaohong Meng21239.64
Shuangchi He31389.31