EURASIP Journal on Wireless Communications and Networking
Volume 2008 (2008), Article ID 892193, 13 pages
doi:10.1155/2008/892193
Research Article

Reduced-Rank Shift-Invariant Technique and Its Application for Synchronization and Channel Identification in UWB Systems

Jian (Andrew) Zhang,1,2 Rodney A. Kennedy,2 and Thushara D. Abhayapala2

1Networked Systems Research Group, NICTA, Canberra, ACT 2601, Australia
2Department of Information Engineering, Research School of Information Sciences and Engineering, The Australian National University, Canberra, ACT 0200, Australia

Received 31 March 2008; Revised 20 August 2008; Accepted 26 November 2008

Academic Editor: Chi Ko

Copyright © 2008 Jian (Andrew) Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We investigate reduced-rank shift-invariant technique and its application for synchronization and channel identification in UWB systems. Shift-invariant techniques, such as ESPRIT and the matrix pencil method, have high resolution ability, but the associated high complexity makes them less attractive in real-time implementations. Aiming at reducing the complexity, we developed novel reduced-rank identification of principal components (RIPC) algorithms. These RIPC algorithms can automatically track the principal components and reduce the computational complexity significantly by transforming the generalized eigen-problem in an original high-dimensional space to a lower-dimensional space depending on the number of desired principal signals. We then investigate the application of the proposed RIPC algorithms for joint synchronization and channel estimation in UWB systems, where general correlator-based algorithms confront many limitations. Technical details, including sampling and the capture of synchronization delay, are provided. Experimental results show that the performance of the RIPC algorithms is only slightly inferior to the general full-rank algorithms.