On the optimality of data-aided coarse timing with dirty templates

Wenshu Zhang, Liuqing Yang, Xiang Cheng, Wei Zang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Rapid and accurate timing synchronization is a critical task in ultrawideband (UWB) systems. Yang and Giannakis introduced a promising algorithm, i.e., data-aided timing with dirty templates (TDT), which is known for low complexity and relaxed operation conditions in the presence of unknown time hopping and multipath channels. In this paper, we will explore the optimality of TDT. We develop a maximum-likelihood (ML) timing algorithm and obtain its optimum training sequence. It is shown that the optimum training sequence of the ML timing estimator coincides with that of the TDT algorithm. In addition, we prove that the ML algorithm can be simplified using this training sequence and that the simplified ML (SML) is equivalent to TDT.

Original languageEnglish (US)
Article number6655977
Pages (from-to)1759-1769
Number of pages11
JournalIEEE Transactions on Vehicular Technology
Issue number4
StatePublished - May 2014
Externally publishedYes


  • Maximum-likelihood (ML) estimation
  • Timing synchronization
  • Ultrawideband (UWB) systems


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