Low-Complexity Bayesian Estimation of Cluster-Sparse Channels

Handle URI:
http://hdl.handle.net/10754/578821
Title:
Low-Complexity Bayesian Estimation of Cluster-Sparse Channels
Authors:
Ballal, Tarig; Al-Naffouri, Tareq Y.; Ahmed, Syed
Abstract:
This paper addresses the problem of channel impulse response estimation for cluster-sparse channels under the Bayesian estimation framework. We develop a novel low-complexity minimum mean squared error (MMSE) estimator by exploiting the sparsity of the received signal profile and the structure of the measurement matrix. It is shown that due to the banded Toeplitz/circulant structure of the measurement matrix, a channel impulse response, such as underwater acoustic channel impulse responses, can be partitioned into a number of orthogonal or approximately orthogonal clusters. The orthogonal clusters, the sparsity of the channel impulse response and the structure of the measurement matrix, all combined, result in a computationally superior realization of the MMSE channel estimator. The MMSE estimator calculations boil down to simpler in-cluster calculations that can be reused in different clusters. The reduction in computational complexity allows for a more accurate implementation of the MMSE estimator. The proposed approach is tested using synthetic Gaussian channels, as well as simulated underwater acoustic channels. Symbol-error-rate performance and computation time confirm the superiority of the proposed method compared to selected benchmark methods in systems with preamble-based training signals transmitted over clustersparse channels.
KAUST Department:
Electrical Engineering Program
Citation:
Low-Complexity Bayesian Estimation of Cluster-Sparse Channels 2015:1 IEEE Transactions on Communications
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Communications
Issue Date:
18-Sep-2015
DOI:
10.1109/TCOMM.2015.2480092
Type:
Article
ISSN:
0090-6778
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7272056
Appears in Collections:
Articles; Electrical Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorBallal, Tarigen
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.contributor.authorAhmed, Syeden
dc.date.accessioned2015-09-28T13:57:25Zen
dc.date.available2015-09-28T13:57:25Zen
dc.date.issued2015-09-18en
dc.identifier.citationLow-Complexity Bayesian Estimation of Cluster-Sparse Channels 2015:1 IEEE Transactions on Communicationsen
dc.identifier.issn0090-6778en
dc.identifier.doi10.1109/TCOMM.2015.2480092en
dc.identifier.urihttp://hdl.handle.net/10754/578821en
dc.description.abstractThis paper addresses the problem of channel impulse response estimation for cluster-sparse channels under the Bayesian estimation framework. We develop a novel low-complexity minimum mean squared error (MMSE) estimator by exploiting the sparsity of the received signal profile and the structure of the measurement matrix. It is shown that due to the banded Toeplitz/circulant structure of the measurement matrix, a channel impulse response, such as underwater acoustic channel impulse responses, can be partitioned into a number of orthogonal or approximately orthogonal clusters. The orthogonal clusters, the sparsity of the channel impulse response and the structure of the measurement matrix, all combined, result in a computationally superior realization of the MMSE channel estimator. The MMSE estimator calculations boil down to simpler in-cluster calculations that can be reused in different clusters. The reduction in computational complexity allows for a more accurate implementation of the MMSE estimator. The proposed approach is tested using synthetic Gaussian channels, as well as simulated underwater acoustic channels. Symbol-error-rate performance and computation time confirm the superiority of the proposed method compared to selected benchmark methods in systems with preamble-based training signals transmitted over clustersparse channels.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7272056en
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.titleLow-Complexity Bayesian Estimation of Cluster-Sparse Channelsen
dc.typeArticleen
dc.contributor.departmentElectrical Engineering Programen
dc.identifier.journalIEEE Transactions on Communicationsen
dc.eprint.versionPost-printen
dc.contributor.institutionElectrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 3126, Saudi Arabiaen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorBallal, Tarigen
kaust.authorAl-Naffouri, Tareq Y.en
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