A least-/mean-squares approach to channel identification and equalization in OFDM

Tareq Y. Al-Naffouri; Ghazi Al-Rawi; Ahmad Bahai; Arogyaswami Paulraj

A least-/mean-squares approach to channel identification and equalization in OFDM

Tareq Y. Al-Naffouri^*, Ghazi Al-Rawi, Ahmad Bahai, Arogyaswami Paulraj

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

5 Scopus citations

Abstract

This work proposes an iterative least-/mean-squares approach to channel identification and equalization in OFDM. This is achieved by exploiting the natural constraints imposed by the channel (sparsity and maximum delay spread) and those imposed by the transmitter (pilots, cyclic pre x, and the nite alphabet constraint). These constraints are used to reduce the number of pilots needed for channel and data recovery and also to perform this task within one packet. The diagonal nature of the OFDM channel makes it possible to perform optimal (nonlinear) mean-square detection of the data.

Original language	English (US)
Pages (from-to)	III/2577-III/2580
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	3
State	Published - 2002
Externally published	Yes
Event	2002 IEEE International Conference on Acoustic, Speech, and Signal Processing - Orlando, FL, United States Duration: May 13 2002 → May 17 2002

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Cite this

@article{481dd79167e64d6599e4604d3690cfee,

title = "A least-/mean-squares approach to channel identification and equalization in OFDM",

abstract = "This work proposes an iterative least-/mean-squares approach to channel identification and equalization in OFDM. This is achieved by exploiting the natural constraints imposed by the channel (sparsity and maximum delay spread) and those imposed by the transmitter (pilots, cyclic pre x, and the nite alphabet constraint). These constraints are used to reduce the number of pilots needed for channel and data recovery and also to perform this task within one packet. The diagonal nature of the OFDM channel makes it possible to perform optimal (nonlinear) mean-square detection of the data.",

author = "Al-Naffouri, {Tareq Y.} and Ghazi Al-Rawi and Ahmad Bahai and Arogyaswami Paulraj",

year = "2002",

language = "English (US)",

volume = "3",

pages = "III/2577--III/2580",

journal = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

issn = "1520-6149",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2002 IEEE International Conference on Acoustic, Speech, and Signal Processing ; Conference date: 13-05-2002 Through 17-05-2002",

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TY - JOUR

T1 - A least-/mean-squares approach to channel identification and equalization in OFDM

AU - Al-Naffouri, Tareq Y.

AU - Al-Rawi, Ghazi

AU - Bahai, Ahmad

AU - Paulraj, Arogyaswami

PY - 2002

Y1 - 2002

N2 - This work proposes an iterative least-/mean-squares approach to channel identification and equalization in OFDM. This is achieved by exploiting the natural constraints imposed by the channel (sparsity and maximum delay spread) and those imposed by the transmitter (pilots, cyclic pre x, and the nite alphabet constraint). These constraints are used to reduce the number of pilots needed for channel and data recovery and also to perform this task within one packet. The diagonal nature of the OFDM channel makes it possible to perform optimal (nonlinear) mean-square detection of the data.

AB - This work proposes an iterative least-/mean-squares approach to channel identification and equalization in OFDM. This is achieved by exploiting the natural constraints imposed by the channel (sparsity and maximum delay spread) and those imposed by the transmitter (pilots, cyclic pre x, and the nite alphabet constraint). These constraints are used to reduce the number of pilots needed for channel and data recovery and also to perform this task within one packet. The diagonal nature of the OFDM channel makes it possible to perform optimal (nonlinear) mean-square detection of the data.

UR - http://www.scopus.com/inward/record.url?scp=0036291216&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0036291216

SN - 1520-6149

VL - 3

SP - III/2577-III/2580

JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - 2002 IEEE International Conference on Acoustic, Speech, and Signal Processing

Y2 - 13 May 2002 through 17 May 2002

ER -

A least-/mean-squares approach to channel identification and equalization in OFDM

Abstract

ASJC Scopus subject areas

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