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.

Survey of automatic modulation classification techniques: classical approaches and new trends

A new approach to modulation classification based on the Kolmogorov-Smirnov (K-S) test is proposed. The K-S test is a non-parametric method to measure the goodness of fit. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic derived from the received signal, and comparing it with the CDFs or the ECDFs of the signal under each candidate modulation format. The K-S-based modulation classifiers are developed for various channels, including the AWGN channel, the flat-fading channel, the OFDM channel, and the channel with unknown phase and frequency offsets, as well as the non-Gaussian noise channel, for both QAM and PSK modulations. Extensive simulation results demonstrate that compared with the traditional cumulant-based classifiers, the proposed K-S classifiers offer superior classification performance, require less number of signal samples (thus is fast), and is more robust to various channel impairments.

Fast and Robust Modulation Classification via Kolmogorov-Smirnov Test

This paper presents a method for the automatic classification of digital modulations without a priori knowledge of the signal parameters. This method can recognize classical single- carrier modulations such as M-ary phase-shift keying, M-ary frequency-shift keying, M-ary amplitude-shift keying, and M-ary quadrature amplitude modulation, as well as orthogonal frequency-division multiplexing modulations such as discrete mul- titone that is used for asymmetric digital subscriber line and very high speed digital subscriber line standards and for power-line carrier transmissions. After identification of the modulation type, the method automatically estimates some parameters characterizing the modulation. To evaluate the method performance, several simulations have been carried out in different operating conditions that should be particularly critical by varying the values of signal- to-noise ratio and the main parameters of each identifiable modulation. To assess the advantages, comparison with other classification methods has been given. To validate the assumption that is made, experimental tests have been performed.

An Automatic Digital Modulation Classifier for Measurement on Telecommunication Networks

Automatic modulation recognition is advantageous for wireless communication systems employing adaptive modulation, software-defined radio, and cognitive radio. In this paper, we consider a phase based maximum likelihood (ML) approach for identifying the modulation format of a linearly modulated signal. Since the optimal ML scheme is computationally intensive, we propose two approximate ML alternatives, which can offer close-to-optimal performance with reduced complexity. We then present a general performance analysis for classification of K types of modulation constellations. For K ;5, we obtain a set of upper bounds on Pcc, which provide a tradeoff between accuracy and complexity in calculating the Pcc. In addition, asymptotic behavior of phase based ML classification schemes is investigated.

Automatic Modulation Identification Based on the Probability Density Function of Signal Phase

Modulation classification is an intermediate step between signal detection and data demodulation. It is attempted for a variety of reasons including reconnaissance, surveillance and other intelligence gathering activities. In practice, the level of a received signal is not known and not constant because of many different reasons, such as fading. In such a case, the classifier needs to estimate the signal level first and then take it into account in the classification algorithm to increase the accuracy of classification. In this paper a modulation classification algorithm with unknown signal level has been developed and tested on binary phase shift keying (BPSK) signal and quadrature phase shift keying (QPSK) signal embedded in additive white Gaussian noise (AWGN). The algorithm applies the generalized likelihood ratio test. The receiver first estimates the unknown signal level using the maximum likelihood method. The estimated signal level is then being used in the likelihood ratio test for classification. Simulations show that the proposed classifier has high accuracy and is superior to the classifier without taking into account the variations in signal level. The classification accuracy increases as the observed symbol number or the variance of the signal level increases.

BPSK and QPSK modulation classification with unknown signal level

A suboptimal algorithm for modulation classification was proposed for classifying the modulation type of general M-ary phase-shifted keying (MPSK) signals. Yang and Soliman (see IEEE Trans. Aerosp. Electron. Syst., vol.33, no.1, p.38-45, 1997) approximated the phase probability density function of a received signal to be the Tikhonov function. Instead, we employ the exact phase density function and derive an asymptotic optimal classification algorithm. We show a structure of this proposed classifier for continuous wave (CW), binary phase-shift keying (BPSK), quaternary phase-shift keying (QPSK), and 8PSK. Besides, we give an example to demonstrate the capability of this algorithm and compare its performance to that in Yang and Soliman. It is shown that the performance is more effective.

Ching-Hwa Liu

Papers

An asymptotic optimal algorithm for modulation classification