OFDM channel estimation by singular value decomposition

TLDR: The theory of optimal rank-reduction is applied to linear minimum mean-squared error (LMMSE) estimators and it is shown that these estimators, when using a fixed design, are robust to changes in channel correlation and signal-to-noise ratio (SNR).

Abstract: We present and analyze low-rank channel estimators for orthogonal frequency-division multiplexing (OFDM) systems using the frequency correlation of the channel. Low-rank approximations based on the discrete Fourier transform (DFT) have been proposed, but these suffer from poor performance when the channel is not sample spaced. We apply the theory of optimal rank-reduction to linear minimum mean-squared error (LMMSE) estimators and show that these estimators, when using a fixed design, are robust to changes in channel correlation and signal-to-noise ratio (SNR). The performance is presented in terms of uncoded symbol-error rate (SER) for a system using 16-quadrature amplitude modulation (QAM).

Full text

Division of Signal Processing
Visiting address: UniversitetsomrŒdet, Porsšn, LuleŒ
Postal address: SE-971 87, LuleŒ, Sweden
Telephone: +46 920 910 00. Fax: +46 920 720 43
URL: http://www.sm.luth.se/csee/sp/
OFDM Channel Estimation
by Singular Value Decomposition
Ove Edfors, Magnus Sandell, Jan-Jaap van de Beek,
Sarah Kate Wilson and Per Ola Bšrjesson
In IEEE Transactions on Communications, vol.
46 no.7, pp. 931-939, July 1998.
© 1998 IEEE. Personal use of this material is permitted.
However, permission to reprint/republish this material for
advertising or promotional purposes or for creating new collective
works for resale or redistribution to servers or lists, or to reuse
any copyrighted component of this work in other works must be
obtained from the IEEE.