The Max-Product Algorithm Viewed as Linear Data-Fusion : A Distributed Detection Scenario
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
Julkaistu sarjassa
IEEE Transactions on Wireless CommunicationsPäivämäärä
2020Tekijänoikeudet
© 2020 IEEE
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.
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Institute of Electrical and Electronics Engineers (IEEE)ISSN Hae Julkaisufoorumista
1536-1276Asiasanat
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https://converis.jyu.fi/converis/portal/detail/Publication/41697701
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