NDLI: OFDM channel estimation in the presence of NBI and the effect of misspecified NBI model

Content Provider	IEEE Xplore Digital Library
Author	Kalyani, S. Giridhar, K.
Copyright Year	2007
Description	Author affiliation: Dept. of Electr. Eng., Indian Inst. of Technol. Madras, Chennai (Kalyani, S.; Giridhar, K.)
Abstract	The presence of narrowband interference (NBI) and thermal noise leads to a contaminated Gaussian (CG) noise probability density function (pdf) at the OFDM receiver. The Cramer-Rao lower bound for channel estimation in the presence of CG noise is analyzed for two NBI pdfs, namely, the Gaussian and the Cauchy pdfs. We then derive the mean square error (MSE) when the actual NBI pdf differs from the NBI pdf for which the estimator is designed, i.e., when the NBI pdf is misspecified. Based on this theoretical MSE analysis, we show that: (i) Even if the maximum likelihood estimator (MLE) designed for the CG noise pdf assuming Cauchy NBI is used when the actual CG pdf has Gaussian NBI, the degradation in MSE performance is negligible; (ii) However, if the MLE for CG pdf designed assuming Gaussian NBI is used for channel estimation in CG noise where the NBI is actually Cauchy, a very poor MSE performance is obtained. This analysis suggests that it is pragmatic to use the MLE designed assuming Cauchy NBI, as it is robust to misspecification of the NBI pdf. Further, an iteratively reweighted least squares algorithm is proposed for implementing the MLE for the CG model with either Gaussian or Cauchy NBI.
Starting Page	1
Ending Page	5
File Size	188228
Page Count	5
File Format	PDF
ISBN	9781424409549
DOI	10.1109/SPAWC.2007.4401334
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-17
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	OFDM Channel estimation Character generation Gaussian noise Maximum likelihood estimation Narrowband Interference Probability density function Mean square error methods Performance analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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