Distributed in-network channel decoding

Hao Zhu; Georgios B. Giannakis; Alfonso Cano

doi:10.1109/TSP.2009.2023936

Distributed in-network channel decoding

Hao Zhu, Georgios B. Giannakis, Alfonso Cano

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

56 Scopus citations

Abstract

Average log-likelihood ratios (LLRs) constitute sufficient statistics for centralized maximum-likelihood block decoding as well as for a posteriori probability evaluation which enables bit-wise (possibly iterative) decoding. By acquiring such average LLRs per sensor it becomes possible to perform these decoding tasks in a low-complexity distributed fashion using wireless sensor networks. At affordable communication overhead, the resultant distributed decoders rely on local message exchanges among single-hop neighboring sensors to achieve iteratively consensus on the average LLRs per sensor. Furthermore, the decoders exhibit robustness to non-ideal inter-sensor links affected by additive noise and random link failures. Pairwise error probability bounds benchmark the decoding performance as a function of the number of consensus iterations. Interestingly, simulated tests corroborating the analytical findings demonstrate that only a few consensus iterations suffice for the novel distributed decoders to approach the performance of their centralized counterparts.

Original language	English (US)
Pages (from-to)	3970-3983
Number of pages	14
Journal	IEEE Transactions on Signal Processing
Volume	57
Issue number	10
DOIs	https://doi.org/10.1109/TSP.2009.2023936
State	Published - 2009

Bibliographical note

Funding Information:
Manuscript received December 27, 2008; accepted April 14, 2009. First published May 27, 2009; current version published September 16, 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Walid Hachem. This work was supported by NSF Grants CCF 0830480 and CON 014658; and also through collaborative participation in the Communications and Networks Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Co-operative Agreement DAAD19-01-2-0011. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. This work was presented in part at the Conference on Information Sciences and Systems, Princeton University, Princeton, NJ, March 19–21, 2008.

Keywords

Channel coding
Decoding
Distributed detection
Wireless sensor networks (WSNs)

Publisher link

10.1109/TSP.2009.2023936

Find It

Find It at U of M Libraries

Cite this

@article{caa5ca3659604606b6c57a008206b37c,

title = "Distributed in-network channel decoding",

abstract = "Average log-likelihood ratios (LLRs) constitute sufficient statistics for centralized maximum-likelihood block decoding as well as for a posteriori probability evaluation which enables bit-wise (possibly iterative) decoding. By acquiring such average LLRs per sensor it becomes possible to perform these decoding tasks in a low-complexity distributed fashion using wireless sensor networks. At affordable communication overhead, the resultant distributed decoders rely on local message exchanges among single-hop neighboring sensors to achieve iteratively consensus on the average LLRs per sensor. Furthermore, the decoders exhibit robustness to non-ideal inter-sensor links affected by additive noise and random link failures. Pairwise error probability bounds benchmark the decoding performance as a function of the number of consensus iterations. Interestingly, simulated tests corroborating the analytical findings demonstrate that only a few consensus iterations suffice for the novel distributed decoders to approach the performance of their centralized counterparts.",

keywords = "Channel coding, Decoding, Distributed detection, Wireless sensor networks (WSNs)",

author = "Hao Zhu and Giannakis, {Georgios B.} and Alfonso Cano",

note = "Funding Information: Manuscript received December 27, 2008; accepted April 14, 2009. First published May 27, 2009; current version published September 16, 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Walid Hachem. This work was supported by NSF Grants CCF 0830480 and CON 014658; and also through collaborative participation in the Communications and Networks Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Co-operative Agreement DAAD19-01-2-0011. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. This work was presented in part at the Conference on Information Sciences and Systems, Princeton University, Princeton, NJ, March 19–21, 2008.",

year = "2009",

doi = "10.1109/TSP.2009.2023936",

language = "English (US)",

volume = "57",

pages = "3970--3983",

journal = "IRE Transactions on Audio",

issn = "1053-587X",

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

number = "10",

}

TY - JOUR

T1 - Distributed in-network channel decoding

AU - Zhu, Hao

AU - Giannakis, Georgios B.

AU - Cano, Alfonso

N1 - Funding Information: Manuscript received December 27, 2008; accepted April 14, 2009. First published May 27, 2009; current version published September 16, 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Walid Hachem. This work was supported by NSF Grants CCF 0830480 and CON 014658; and also through collaborative participation in the Communications and Networks Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Co-operative Agreement DAAD19-01-2-0011. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. This work was presented in part at the Conference on Information Sciences and Systems, Princeton University, Princeton, NJ, March 19–21, 2008.

PY - 2009

Y1 - 2009

N2 - Average log-likelihood ratios (LLRs) constitute sufficient statistics for centralized maximum-likelihood block decoding as well as for a posteriori probability evaluation which enables bit-wise (possibly iterative) decoding. By acquiring such average LLRs per sensor it becomes possible to perform these decoding tasks in a low-complexity distributed fashion using wireless sensor networks. At affordable communication overhead, the resultant distributed decoders rely on local message exchanges among single-hop neighboring sensors to achieve iteratively consensus on the average LLRs per sensor. Furthermore, the decoders exhibit robustness to non-ideal inter-sensor links affected by additive noise and random link failures. Pairwise error probability bounds benchmark the decoding performance as a function of the number of consensus iterations. Interestingly, simulated tests corroborating the analytical findings demonstrate that only a few consensus iterations suffice for the novel distributed decoders to approach the performance of their centralized counterparts.

AB - Average log-likelihood ratios (LLRs) constitute sufficient statistics for centralized maximum-likelihood block decoding as well as for a posteriori probability evaluation which enables bit-wise (possibly iterative) decoding. By acquiring such average LLRs per sensor it becomes possible to perform these decoding tasks in a low-complexity distributed fashion using wireless sensor networks. At affordable communication overhead, the resultant distributed decoders rely on local message exchanges among single-hop neighboring sensors to achieve iteratively consensus on the average LLRs per sensor. Furthermore, the decoders exhibit robustness to non-ideal inter-sensor links affected by additive noise and random link failures. Pairwise error probability bounds benchmark the decoding performance as a function of the number of consensus iterations. Interestingly, simulated tests corroborating the analytical findings demonstrate that only a few consensus iterations suffice for the novel distributed decoders to approach the performance of their centralized counterparts.

KW - Channel coding

KW - Decoding

KW - Distributed detection

KW - Wireless sensor networks (WSNs)

UR - http://www.scopus.com/inward/record.url?scp=70349648102&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70349648102&partnerID=8YFLogxK

U2 - 10.1109/TSP.2009.2023936

DO - 10.1109/TSP.2009.2023936

M3 - Article

AN - SCOPUS:70349648102

VL - 57

SP - 3970

EP - 3983

JO - IRE Transactions on Audio

JF - IRE Transactions on Audio

SN - 1053-587X

IS - 10

ER -

Distributed in-network channel decoding

Abstract

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this