A new minimum-consensus distributed particle filter for blind equalization in receiver networks

We describe in this paper a novel distributed particle filtering algorithm that performs blind equalization of frequency-selective channels in a setup with a single transmitter and multiple receivers. The algorithm employs parallel minimum consensus iterations to determine some a posteriori probabil...

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Bibliographic Details
Main Authors: Bordin, Claudio J., Bruno, Marcelo G. S.
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Approximation methods
Bit error rate
Blind Equalization
Blind equalizers
Distributed Algorithms
Joints
Monte Carlo methods
Particle Filters
Receivers
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Summary:We describe in this paper a novel distributed particle filtering algorithm that performs blind equalization of frequency-selective channels in a setup with a single transmitter and multiple receivers. The algorithm employs parallel minimum consensus iterations to determine some a posteriori probability functions, providing equal approximations on all network nodes in a finite, deterministic, network-dependent number of steps. We verify via computer simulations that the new algorithm exhibits a bit error rate (BER) performance similar to that of the centralized particle-filter estimator with communication requirements milder than that of previous approaches, as the new method drops the need to evaluate quantities via average consensus.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2013.6638882