28,555 research outputs found
Modulation Classification for MIMO-OFDM Signals via Approximate Bayesian Inference
The problem of modulation classification for a multiple-antenna (MIMO) system
employing orthogonal frequency division multiplexing (OFDM) is investigated
under the assumption of unknown frequency-selective fading channels and
signal-to-noise ratio (SNR). The classification problem is formulated as a
Bayesian inference task, and solutions are proposed based on Gibbs sampling and
mean field variational inference. The proposed methods rely on a selection of
the prior distributions that adopts a latent Dirichlet model for the modulation
type and on the Bayesian network formalism. The Gibbs sampling method converges
to the optimal Bayesian solution and, using numerical results, its accuracy is
seen to improve for small sample sizes when switching to the mean field
variational inference technique after a number of iterations. The speed of
convergence is shown to improve via annealing and random restarts. While most
of the literature on modulation classification assume that the channels are
flat fading, that the number of receive antennas is no less than that of
transmit antennas, and that a large number of observed data symbols are
available, the proposed methods perform well under more general conditions.
Finally, the proposed Bayesian methods are demonstrated to improve over
existing non-Bayesian approaches based on independent component analysis and on
prior Bayesian methods based on the `superconstellation' method.Comment: To be appear in IEEE Trans. Veh. Technolog
An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel
Computing the distinct features from input data, before the classification,
is a part of complexity to the methods of Automatic Modulation Classification
(AMC) which deals with modulation classification was a pattern recognition
problem. Although the algorithms that focus on MultiLevel Quadrature Amplitude
Modulation (M-QAM) which underneath different channel scenarios was well
detailed. A search of the literature revealed indicates that few studies were
done on the classification of high order M-QAM modulation schemes like128-QAM,
256-QAM, 512-QAM and1024-QAM. This work is focusing on the investigation of the
powerful capability of the natural logarithmic properties and the possibility
of extracting Higher-Order Cumulant's (HOC) features from input data received
raw. The HOC signals were extracted under Additive White Gaussian Noise (AWGN)
channel with four effective parameters which were defined to distinguished the
types of modulation from the set; 4-QAM~1024-QAM. This approach makes the
recognizer more intelligent and improves the success rate of classification.
From simulation results, which was achieved under statistical models for noisy
channels, manifest that recognized algorithm executes was recognizing in M-QAM,
furthermore, most results were promising and showed that the logarithmic
classifier works well over both AWGN and different fading channels, as well as
it can achieve a reliable recognition rate even at a lower signal-to-noise
ratio (less than zero), it can be considered as an Integrated Automatic
Modulation Classification (AMC) system in order to identify high order of M-QAM
signals that applied a unique logarithmic classifier, to represents higher
versatility, hence it has a superior performance via all previous works in
automatic modulation identification systemComment: 18 page
Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module
The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe
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