Distributed consensus-based demodulation: Algorithms and error analysis

Hao Zhu, Alfonso Cano, Georgios Giannakis

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

This paper deals with distributed demodulation of space-time transmissions of a common message from a multiantenna access point (AP) to a wireless sensor network. Based on local message exchanges with single-hop neighboring sensors, two algorithms are developed for distributed demodulation. In the first algorithm, sensors consent on the estimated symbols. By relaxing the finite-alphabet constraints on the symbols, the demodulation task is formulated as a distributed convex optimization problem that is solved iteratively using the method of multipliers. Distributed versions of the centralized zero-forcing (ZF) and minimum mean-square error (MMSE) demodulators follow as special cases. In the second algorithm, sensors iteratively reach consensus on the average (cross-) covariances of locally available per-sensor data vectors with the corresponding AP-tosensor channel matrices, which constitute sufficient statistics for maximum likelihood demodulation. Distributed versions of the sphere decoding algorithm and the ZF/MMSE demodulators are also developed. These algorithms offer distinct merits in terms of error performance and resilience to non-ideal inter-sensor links. In both cases, the per-iteration error performance is analyzed, and the approximate number of iterations needed to attain a prescribed error rate are quantified. Simulated tests verify the analytical claims. Interestingly, only a few consensus iterations (roughly as many as the number of sensors), suffice for the distributed demodulators to approach the performance of their centralized counterparts.

Original languageEnglish (US)
Article number5475348
Pages (from-to)2044-2054
Number of pages11
JournalIEEE Transactions on Wireless Communications
Volume9
Issue number6
DOIs
StatePublished - Jun 2010

Bibliographical note

Funding Information:
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, Cooperative Agreement DAAD19-01-2-0011. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation therein. Part of this work was presented at the 42nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 2008.

Keywords

  • Cooperative diversity
  • Detection and estimation
  • Sensor networks

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