PROJECT TITLE :

Distributed Maximum Likelihood Classification of Linear Modulations Over Nonidentical Flat Block-Fading Gaussian Channels

ABSTRACT:

In this paper, we have a tendency to take into account distributed most probability (ML) classification of digital amplitude-section modulated signals using multiple sensors that observe the identical sequence of unknown symbol transmissions over nonidentical flat blockfading Gaussian noise channels. A variant of the expectation-maximization (EM) algorithm is employed to obtain the ML estimates of the unknown channel parameters and compute the global log-chance of the observations received by all the sensors in a very distributed manner by suggests that of a mean consensus filter. This procedure is repeated for all candidate modulation formats within the reference library, and a classification decision, that is obtainable at any of the sensors in the network, is asserted in favor of the modulation with the very best log-chance score. The proposed scheme improves the classification accuracy by exploiting the signal-to-noise ratio (SNR) diversity within the network while proscribing the communication to a little neighborhood of each sensor. Numerical examples show that the proposed distributed EM-based classifier will achieve the same classification performance as that of a centralized classifier, which has all the sensor measurements, for a wide selection of SNR values.


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