Higher-order cyclic cumulants for high order modulation classification
13 Oct 2003-Vol. 1, pp 112-117
TL;DR: This paper investigates automatic modulation classification (AMC) using homogeneous feature-vectors based on cyclic cumulants of fourth, sixth- and eight-orders for QAM, PSK and ASK signals within a pattern recognition framework.
Abstract: In this paper we investigate automatic modulation classification (AMC) using homogeneous feature-vectors based on cyclic cumulants (CCs) of fourth-, sixth- and eight-orders, respectively, for QAM, PSK and ASK signals within a pattern recognition framework. Analysis of CCs of the baseband signal at the receiver is performed and used for feature selection. The cycle spectrum of the baseband signal at the receiver is derived as a function of excess bandwidth for a raised cosine pulse shape and a necessary and sufficient condition on the oversampling factor is obtained. Theoretical arguments regarding the discrimination capability of the examined feature-vectors are verified through extensive simulations.
Citations
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TL;DR: The authors provide a comprehensive survey of different modulation recognition techniques in a systematic way, and simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies.
Abstract: The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information and so on, blind identification of the modulation is a difficult task. This becomes even more challenging in real-world scenarios with multipath fading, frequency-selective and time-varying channels. With this in mind, the authors provide a comprehensive survey of different modulation recognition techniques in a systematic way. A unified notation is used to bring in together, under the same umbrella, the vast amount of results and classifiers, developed for different modulations. The two general classes of automatic modulation identification algorithms are discussed in detail, which rely on the likelihood function and features of the received signal, respectively. The contributions of numerous articles are summarised in compact forms. This helps the reader to see the main characteristics of each technique. However, in many cases, the results reported in the literature have been obtained under different conditions. So, we have also simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies. Furthermore, new problems that have appeared as a result of emerging wireless technologies are outlined. Finally, open problems and possible directions for future research are briefly discussed.
1,140Â citations
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.
284Â citations
TL;DR: This paper explores the use of Genetic Programming in combination with K-nearest neighbor (KNN) for AMC and demonstrates that the proposed method provides better classification performance compared to other recent methods.
Abstract: Automatic Modulation Classification (AMC) is an intermediate step between signal detection and demodulation. It is a very important process for a receiver that has no, or limited, knowledge of received signals. It is important for many areas such as spectrum management, interference identification and for various other civilian and military applications. This paper explores the use of Genetic Programming (GP) in combination with K-nearest neighbor (KNN) for AMC. KNN has been used to evaluate fitness of GP individuals during the training phase. Additionally, in the testing phase, KNN has been used for deducing the classification performance of the best individual produced by GP. Four modulation types are considered here: BPSK, QPSK, QAM16 and QAM64. Cumulants have been used as input features for GP. The classification process has been divided into two-stages for improving the classification accuracy. Simulation results demonstrate that the proposed method provides better classification performance compared to other recent methods.
263Â citations
Cites background from "Higher-order cyclic cumulants for h..."
...Dobre, Ness and Su [24] reported classification accuracy of 70% at an SNR of 10 dB using 2000 samples....
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TL;DR: The use of cyclostationarity based methods for distributed signal detection and classification are presented and examples are given to illustrate the concepts.
Abstract: Cognitive radios have become a key research area in communications over the past few years. Automatic modulation classification (AMC) is an important component that improves the overall performance of the cognitive radio. Most modulated signals exhibit the property of cyclostationarity that can be exploited for the purpose of classification. In this paper, AMCs that are based on exploiting the cyclostationarity property of the modulated signals are discussed. Inherent advantages of using cyclostationarity based AMC are also addressed. When the cognitive radio is in a network, distributed sensing methods have the potential to increase the spectral sensing reliability, and decrease the probability of interference to existing radio systems. The use of cyclostationarity based methods for distributed signal detection and classification are presented. Examples are given to illustrate the concepts. The Matlab codes for some of the algorithms described in the paper are available for free download at http://filebox.vt.edu/user/bramkum.
226Â citations
TL;DR: The present bibliography represents a comprehensive list of references on cyclostationarity and its applications by listing most of the existing references up to the year 2005 and by providing a detailed classification group.
153Â citations
References
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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.
Abstract: A simple method, based on elementary fourth-order cumulants, is proposed for the classification of digital modulation schemes. These statistics are natural in this setting as they characterize the shape of the distribution of the noisy baseband I and Q samples. 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. Thus, the method can be used as a preliminary classifier if desired. Computational complexity is order N, where N is the number of complex baseband data samples. This method is robust in the presence of carrier phase and frequency offsets and can be implemented recursively. Theoretical arguments are verified via extensive simulations and comparisons with existing approaches.
974Â citations
22 Oct 2000
TL;DR: Simulation results show that two novel modulation classification algorithms that are based on the decision theoretic approach can offer a significant performance gain for classification of dense, non-constant envelope constellations.
Abstract: We discuss modulation classification (MC) algorithms that are based on the decision theoretic approach, where the MC problem is viewed as a multiple-hypothesis testing problem. In particular, a random-phase AWGN channel is considered and possible solutions to this hypothesis testing problem are reviewed. We present two novel algorithms and we compare their performance with existing ones for a variety of modulation pairs. Simulation results show that these new algorithms can offer a significant performance gain for classification of dense, non-constant envelope constellations.
273Â citations
TL;DR: It is shown that time averages of such mixtures converge in the mean-square sense to their ensemble averages and that sample averages of arbitrary orders are jointly complex normal and provide their covariance expressions.
Abstract: We generalize Parzen's (1961) analysis of "asymptotically stationary" processes to mixtures of deterministic, stationary, nonstationary, and generally complex time series. Under certain mixing conditions expressed in terms of joint cumulant summability, we show that time averages of such mixtures converge in the mean-square sense to their ensemble averages. We additionally show that sample averages of arbitrary orders are jointly complex normal and provide their covariance expressions. These conclusions provide us with statistical tools that treat random and deterministic signals on a common framework and are helpful in defining generalized moments and cumulants of mixed processes. As an important consequence, we develop consistent and asymptotically normal estimators for time-varying, and cyclic-moments and cumulants of kth-order cyclostationary processes and provide computable variance expressions. Some examples are considered to illustrate the salient features of the analysis. >
259Â citations
TL;DR: The development of the theory of nonlinear processing of cyclostationary time-series that is initiated in Part I is continued and a new type of cumulant for complex-valued variables is introduced and used to generalize the temporal and spectral moments and cumulants for cyclostators from real-valued tocomplex-valued time- series.
Abstract: For pt.I see ibid., vol.42, no.12, p.3387-3408 (1994). The development of the theory of nonlinear processing of cyclostationary time-series that is initiated in Part I is continued. A new type of cumulant for complex-valued variables is introduced and used to generalize the temporal and spectral moments and cumulants for cyclostationary time-series from real-valued to complex-valued time-series. The relations between the temporal and spectral moments and cumulants at the inputs and outputs of several signal processing operations are determined. Formulas for the temporal and spectral cumulants of complex-valued pulse-amplitude-modulated time-series are derived. Estimators for the temporal moments and cumulants and for the cyclic polyspectra are presented and their properties are discussed. The performance of these estimators is illustrated by several computer simulation examples for pulse-amplitude-modulated time-series. The theory is applied to the problems of weak-signal detection and interference-tolerant time-delay estimation. >
221Â citations