A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Abdi, Younes; Ristaniemi, Tapani

Lehti tai sarja: IEEE Transactions on Wireless Communications

ISSN: 1536-1276

eISSN: 1558-2248

Julkaisuvuosi: 2020

Volyymi: 19

Lehden numero: 11

Artikkelin sivunumerot: 7585-7597

Kustantaja: Institute of Electrical and Electronics Engineers (IEEE)

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/71401

Julkaisu on rinnakkaistallennettu: https://arxiv.org/abs/1909.09402


Tiivistelmä

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.


YSO-asiasanat: algoritmit; Markovin ketjut

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


Liittyvät organisaatiot


OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 3


Viimeisin päivitys 2021-17-09 klo 16:22