A1 Journal article (refereed)
The Max-Product Algorithm Viewed as Linear Data-Fusion : A Distributed Detection Scenario (2020)

Abdi, Y., & Ristaniemi, T. (2020). The Max-Product Algorithm Viewed as Linear Data-Fusion : A Distributed Detection Scenario. IEEE Transactions on Wireless Communications, 19(11), 7585-7597. https://doi.org/10.1109/twc.2020.3012910

JYU authors or editors

Publication details

All authors or editors: Abdi, Younes; Ristaniemi, Tapani

Journal or series: IEEE Transactions on Wireless Communications

ISSN: 1536-1276

eISSN: 1558-2248

Publication year: 2020

Volume: 19

Issue number: 11

Pages range: 7585-7597

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/twc.2020.3012910

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/71401

Publication is parallel published: https://arxiv.org/abs/1909.09402

Abstract

In this paper, we disclose the statistical behavior of the max-product algorithm configured to solve a maximum a posteriori (MAP) estimation problem in a network of distributed agents. Specifically, we first build a distributed hypothesis test conducted by a max-product iteration over a binary-valued pairwise Markov random field and show that the decision variables obtained are linear combinations of the local log-likelihood ratios observed in the network. Then, we use these linear combinations to formulate the system performance in terms of the false-alarm and detection probabilities. Our findings indicate that, in the hypothesis test concerned, the optimal performance of the max-product algorithm is obtained by an optimal linear data-fusion scheme and the behavior of the max-product algorithm is very similar to the behavior of the sum-product algorithm. Consequently, we demonstrate that the optimal performance of the max-product iteration is closely achieved via a linear version of the sum-product algorithm, which is optimized based on statistics received at each node from its one-hop neighbors. Finally, we verify our observations via computer simulations.

Keywords: algorithms; Markov chains

Free keywords: statistical inference; distributed systems; max-product algorithm; sum-product algorithm; linear data-fusion; Markov random fields; factor graphs; spectrum sensing

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2020

JUFO rating: 3