Emmanuel Kanterakis

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A modulation classification (MC) scheme based on Independent Component Analysis (ICA) in conjunction with either maximum likelihood (ML) or Support Vector Machines (SVM) is proposed for MIMO-OFDM signals over frequency selective, time varying channels. The method is blind in the sense that it is assumed that the receiver has no information about the channel(More)
Likelihood-based algorithms identify the modulation of the transmitted signal based on the computation of the likelihood function of received signals under different hypotheses (modulation formats). An important class of likelihood-based algorithms for modulation classification problems first treats the unknown channels as deterministic, and replaces the(More)
OFDM signal classification has become an important requirement in many cognitive radios and military systems. Implementing such a requirement under Frequency Selective Rayleigh fading is a challenging task. In this paper, we explore different blind OFDM parameter and channel estimation techniques based on Maximum Likelihood techniques and higher order(More)
A coarsely-synchronized, easy to deploy, distributed RF sensor network using embedded USRP software-defined radios on a dedicated IP network was developed and tested. The goal was the evaluation of a simple network and signal observation synchronization strategy on measured physical signals for use with collaborative sensor spectral estimation algorithms.(More)
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