Distributed in-network channel decoding

Hao Zhu, Georgios B. Giannakis, Alfonso Cano

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


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 languageEnglish (US)
Pages (from-to)3970-3983
Number of pages14
JournalIEEE Transactions on Signal Processing
Issue number10
StatePublished - 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.


  • Channel coding
  • Decoding
  • Distributed detection
  • Wireless sensor networks (WSNs)


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