The number of coefficients for estimating time-varying channel is generally much larger than the maximum number of observable data. Linearly time-varying channel (LTV) models have been widely used as a way of reducing the number of these channel parameters. This approach allows estimation using the channel slope and channel frequency response between symbols. A fatal problem with this approach, however, is that before it is even initiated, the estimated channel in the frequency domain has already been degraded by inter-carrier interference (ICI). We demonstrate this by analyzing the channel estimation mean square error (MSE) and signal to interference and noise ratio (SINR) after ICI cancellation with respect to the LTV model. We then propose a new channel estimation technique using dual-ICI cancellation which resolves this problem by pre-canceling ICI for the channel estimation using the channel of the previous symbol and post-canceling ICI for symbol detection with the more accurate channel information. The process is initialized using the first symbol as a preamble to estimate the channel in the time domain. Performance comparisons of MSE and SINR show that the proposed method is better suited to fast-fading channels than the conventional method, where ICI dominates the interference and noise power.
Bibliographical noteFunding Information:
Manuscript received September 20, 2009; revised February 10, 2010; accepted July 8, 2010. The associate editor coordinating the review of this paper and approving it for publication was V. Bhargava. This work was supported in part by the Korea Science and Engineering Foundation through the NRL Program (grant R0A-2007-000-20043-0), and in part by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2010–(C1090-1011-0005)). K. Kwak is with LGE. S. Lee is with Georgia Institute of Tech. as post doctorial researcher. H. Min is with Yonsei Univ. as Ph.D. candidate. S. Choi is with Yonsei Univ. as Assistant Professor. D. Hong is with Yonsei Univ. as Professor (e-mail: Daesikh@yonsei.ac.kr). Digital Object Identifier 10.1109/TWC.2010.090210.091458
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics