Optimal training sequences for efficient MIMO frequency-selective fading channel estimation
Document Type
Conference Proceeding
Publication Date
12-1-2006
Abstract
In this paper, novel channel estimation schemes using uncorrelated periodic complementary sets of unitary sequences are proposed for multiple-input multiple-output (MIMO) frequency-selective fading channels. When the additive noise is Gaussian, the proposed best linear unbiased estimator (BLUE) achieves the minimum possible classical Cramér-Rao lower bound (CRLB), if the channel coefficients are regarded as unknown deterministics. On the other hand, the proposed linear minimum mean square error (LMMSE) estimator attains the minimum possible Bayesian CRLB, when the underlying channel coefficients are Gaussian and independent of the additive Gaussian noise. The proposed channel estimators can be implemented with very low complexity via FFT, which makes them very suitable for practical systems such as, but not limited to, MIMO orthogonal frequency division multiplexing (MIMO-OFDM) systems.
Identifier
50649110289 (Scopus)
ISBN
[1424400023, 9781424400027]
Publication Title
2006 IEEE Sarnoff Symposium
External Full Text Location
https://doi.org/10.1109/SARNOF.2006.4534770
Recommended Citation
Wang, Shuangquan and Abdi, Ali, "Optimal training sequences for efficient MIMO frequency-selective fading channel estimation" (2006). Faculty Publications. 18600.
https://digitalcommons.njit.edu/fac_pubs/18600
