Optimal training sequences for efficient MIMO frequency-selective fading channel estimation

Shuangquan Wang; Ali Abdi

doi:10.1109/SARNOF.2006.4534770

Optimal training sequences for efficient MIMO frequency-selective fading channel estimation

Shuangquan Wang, Ali Abdi

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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.

Original language	English (US)
Title of host publication	2006 IEEE Sarnoff Symposium
DOIs	https://doi.org/10.1109/SARNOF.2006.4534770
State	Published - 2006
Event	2006 IEEE Sarnoff Symposium - Princeton, NJ, United States Duration: Mar 27 2006 → Mar 28 2006

Publication series

Name	2006 IEEE Sarnoff Symposium

Other

Other	2006 IEEE Sarnoff Symposium
Country/Territory	United States
City	Princeton, NJ
Period	3/27/06 → 3/28/06

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Electrical and Electronic Engineering
Communication

Access to Document

10.1109/SARNOF.2006.4534770

Cite this

@inproceedings{65b3087dcc014de4a9e456640a47b5a2,

title = "Optimal training sequences for efficient MIMO frequency-selective fading channel estimation",

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 Cram{\'e}r-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.",

author = "Shuangquan Wang and Ali Abdi",

year = "2006",

doi = "10.1109/SARNOF.2006.4534770",

language = "English (US)",

isbn = "1424400023",

series = "2006 IEEE Sarnoff Symposium",

booktitle = "2006 IEEE Sarnoff Symposium",

note = "2006 IEEE Sarnoff Symposium ; Conference date: 27-03-2006 Through 28-03-2006",

}

TY - GEN

T1 - Optimal training sequences for efficient MIMO frequency-selective fading channel estimation

AU - Wang, Shuangquan

AU - Abdi, Ali

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=50649110289&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=50649110289&partnerID=8YFLogxK

U2 - 10.1109/SARNOF.2006.4534770

DO - 10.1109/SARNOF.2006.4534770

M3 - Conference contribution

AN - SCOPUS:50649110289

SN - 1424400023

SN - 9781424400027

T3 - 2006 IEEE Sarnoff Symposium

BT - 2006 IEEE Sarnoff Symposium

T2 - 2006 IEEE Sarnoff Symposium

Y2 - 27 March 2006 through 28 March 2006

ER -

Optimal training sequences for efficient MIMO frequency-selective fading channel estimation

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this