Journal ArticleDOI
Automatic modulation classification algorithm using higher-order cumulants under real-world channel conditions
TLDR
Proposed algorithm can achieve much better classification accuracy in distinguishing BPSK from complex-valued modulation techniques, under real-world channel propagation conditions.Abstract:
An automatic modulation classification algorithm for application in communications via multipath fading channels, without a priori information on channel characteristics, based on normalized sixth-order cumulants is described. In comparison with existing approaches, proposed algorithm can achieve much better classification accuracy in distinguishing BPSK from complex-valued modulation techniques. Theoretical analysis is verified via extensive simulations, under real-world channel propagation conditions.read more
Citations
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Journal ArticleDOI
Signal Identification for Multiple-Antenna Wireless Systems: Achievements and Challenges
TL;DR: The aim of this work is to provide a comprehensive state-of-the-art survey on algorithms proposed for the new and challenging signal identification problems specific to MIMO systems, including space-time block code (STBC) identification, MIMo modulation identification, and detection of the number of transmit antennas.
Journal ArticleDOI
A Survey on Deep Learning Techniques in Wireless Signal Recognition
TL;DR: A brief overview of signal recognition approaches is presented in this article, where classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system.
Journal ArticleDOI
Hierarchical Hypothesis and Feature-Based Blind Modulation Classification for Linearly Modulated Signals
TL;DR: A hierarchical hypothesis-based theoretical framework has been developed to find the probability of error for the proposed BMC method, which is more robust than the one based on EC and at the same time it requires lower complexity than the maximum likelihood approach.
Journal ArticleDOI
Automatic Modulation Identification Based on the Probability Density Function of Signal Phase
Qinghua Shi,Yoshio Karasawa +1 more
TL;DR: This paper considers a phase based maximum likelihood approach for identifying the modulation format of a linearly modulated signal, and proposes two approximate ML alternatives, which can offer close-to-optimal performance with reduced complexity.
Journal ArticleDOI
Blind Modulation Classification Algorithm for Single and Multiple-Antenna Systems Over Frequency-Selective Channels
Mohamed Marey,Octavia A. Dobre +1 more
TL;DR: It is shown that the correlation functions of the received signals for certain modulation formats exhibit peaks at a particular set of time lags, a result which can be exploited as a discriminating feature.
References
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Journal ArticleDOI
Hierarchical digital modulation classification using cumulants
Ananthram Swami,Brian M. Sadler +1 more
TL;DR: It is shown that cumulant-based classification is particularly effective when used in a hierarchical scheme, enabling separation into subclasses at low signal-to-noise ratio with small sample size.
Journal ArticleDOI
A statistical model of urban multipath propagation
TL;DR: An urban multipath propagation experiment, involving the simultaneous transmission from a fixed site of 100-ns pulses at 488, 1280, and 2920 MHz and their reception at a mobile van, is described, and a statistical analysis of the data in the resulting multipath responses is given.
Journal ArticleDOI
Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels
Abstract: Nowadays, automatic modulation classification (AMC) plays an important role in both cooperative and non-cooperative communication applications. Very often, multipath fading channels result in the severe AMC performance degradation or induce large classification errors. The negative impacts of multipath fading channels on AMC have been discussed in the existing literature but no solution has ever been proposed so far to the best of our knowledge. In this paper, we propose a new robust AMC algorithm, which applies higher-order statistics (HOS) in a generic framework for blind channel estimation and pattern recognition. We also derive the Cramer-Rao lower bound for the fourth-order cumulant estimator when the AMC candidates are BPSK and QPSK over the additive white Gaussian noise channel, and it is a nearly minimum-variance estimator leading to robust AMC features in a wide variety of signal-to-noise ratios. The advantage of our new algorithm is that, by carefully designing the essential features needed for AMC, we do not really have to acquire the complete channel information and therefore it can be feasible without any a priori information in practice. The Monte Carlo simulation results show that our new AMC algorithm can achieve the much better classification accuracy than the existing AMC techniques.
Journal ArticleDOI
Simulation of urban vehicle-monitoring systems
TL;DR: The results of experimentally based computer simulations of phase-ranging and pulse-ranging urban vehicle-monitoring systems show that such systems are quite feasible even in the worst environments.
Journal ArticleDOI
A Multivariate Extension of Hoeffding's Lemma.
Henry W. Block,Z. Fang +1 more
TL;DR: In this paper, a generalization of Hoeffding's lemma for more than two random variables is given, which involves an integral representation of the multivariate joint cumulant.
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