A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Optimization of Linearized Belief Propagation for Distributed Detection (2020)

Abdi, Y., & Ristaniemi, T. (2020). Optimization of Linearized Belief Propagation for Distributed Detection. IEEE Transactions on Communications, 68(2), 959-973. https://doi.org/10.1109/TCOMM.2019.2956037

JYU-tekijät tai -toimittajat

Julkaisun tiedot

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

Lehti tai sarja: IEEE Transactions on Communications

ISSN: 0090-6778

eISSN: 1558-0857

Julkaisuvuosi: 2020

Volyymi: 68

Lehden numero: 2

Artikkelin sivunumerot: 959-973

Kustantaja: IEEE

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1109/TCOMM.2019.2956037

Julkaisun avoin saatavuus: 

Julkaisukanavan avoin saatavuus: 

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/68987

Tiivistelmä

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a priori describing the statistical behavior of the wireless environment concerned. In addition, we propose a blind threshold adaptation method to guarantee a certain performance level in a BP-based distributed detection system. To clarify the points discussed, we design a novel linear-BP-based distributed spectrum sensing scheme for cognitive radio networks and illustrate the performance improvement obtained, over an existing BP-based detection method, via computer simulations.

YSO-asiasanat: tilastolliset mallit; verkkoteoria; algoritmit; hajautetut järjestelmät; signaalinkäsittely

Vapaat asiasanat: statistical inference; distributed systems; belief-propagation algorithm; linear data-fusion; Markov random fields; spectrum sensing; blind signal processing

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 2