New lower bounds for noncoherent channel estimation and ML performance

Ryan, Daniel J., Clarkson, I. Vaughan L. and Collings, Iain B. (2006). New lower bounds for noncoherent channel estimation and ML performance. In: Proceedings of the 49th Annual IEEE Global Telecommunications Conference (GLOBECOM 2006). 49th Annual IEEE Global Telecommunications Conference (GLOBECOM 2006), San Francisco, United States, (4150727-1-4150727-5). 27 November - 1 December 2006. doi:10.1109/GLOCOM.2006.97


Author Ryan, Daniel J.
Clarkson, I. Vaughan L.
Collings, Iain B.
Title of paper New lower bounds for noncoherent channel estimation and ML performance
Conference name 49th Annual IEEE Global Telecommunications Conference (GLOBECOM 2006)
Conference location San Francisco, United States
Conference dates 27 November - 1 December 2006
Convener Institute of Electrical and Electronic Engineers (IEEE) Communications Society
Proceedings title Proceedings of the 49th Annual IEEE Global Telecommunications Conference (GLOBECOM 2006)
Journal name Globecom 2006 - 2006 Ieee Global Telecommunications Conference
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2006
Sub-type Fully published paper
DOI 10.1109/GLOCOM.2006.97
ISBN 1-4244-0356-1
ISSN 1930-529X
Issue 142440357X; 1424403561
Start page 4150727-1
End page 4150727-5
Total pages 5
Language eng
Abstract/Summary We consider the optimal performance of noncoherent channel estimation, that is, where the codebook is known to the receiver but the actual transmitted data is not. It is well known that when training data is known to the receiver, the minimum variance of the channel estimation error for unbiased channel estimation is bounded by the Cramer-Rao lower bound. However, in the noncoherent case, where joint estimation of both a continuous channel and discrete data is required, the Cramer- Rao bound is not applicable. We derive a new bound for this mixed multiple parameter estimation problem for flat fading channels, based on the Hammersley-Chapman-Robbins bound for restricted parameters. We show that the new noncoherent bound asymptotically approaches the Cramer-Rao bound with increasing SNR and sequence length. As an example we consider channel estimation for BPSK over a positive real-valued channel. We show that the noncoherent ML detector is asymptotically unbiased and achieves the lower bound with increasing SNR. We also observe that for moderate SNR the noncoherent ML estimator can actually outperform the optimal coherent ML estimator.
Subjects 090609 Signal Processing
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Article number 4150727

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Thu, 02 Apr 2009, 12:15:35 EST by Mary-Anne Marrington on behalf of School of Information Technol and Elec Engineering