Modulation classification in unknown dispersive environments

Paris, B.-P.; Orsak, G.C.; Chen, H.; Warke, N.

doi:10.1109/icassp.1997.604731

Cited by 10 publications

(7 citation statements)

References 2 publications

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“…Examples of features are the correlation between the in-phase and quadrature signal components [27], the variance of the centered normalized signal amplitude, phase and frequency [28], the variance of the zero-crossing interval [32], [33], the variance of the magnitude of the signal wavelet transform (WT) after peak removal [36]- [38], the phase PDF [44]- [46] and its statistical moments [47]- [49], moments, cumulants, and cyclic cumulants of the signal itself [41]- [43], [53], [54], [58]- [66], etc. The entropy [67], [68], fuzzy logic [69], [70], a moment matrix technique [71], [72] and a constellation shape recovery method [73] were also used for AMC.…”

Section: Feature-based Approach To Amcmentioning

confidence: 99%

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

et al. 2007

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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, etc., 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. In this paper we 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 summarized in compact forms. This helps the reader to see the main characteristics of each technique. However, in many cases, the reported results 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. At the end, open problems and possible directions for future research are briefly discussed.

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Section: Feature-based Approach To Amcmentioning

confidence: 99%

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

et al. 2007

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“…22,23 The feature-based (FB) methods comprise feature extraction and classification. In the feature extraction stage, features of the received signals such as spectral features, 24,25 statistical features, [26][27][28][29][30][31] transform-domain features, [32][33][34][35][36] and other features [37][38][39][40][41][42][43] are extracted. In the classification stage, a suitable classifier works on the extracted features from the feature extraction stage to classify the different modulation types.…”

Section: F I G U R E 2 Summary Of Amc Methodsmentioning

confidence: 99%

A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

Abdel‐Moneim

El‐Shafai

Abdel-Salam

et al. 2021

Int J Communication

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Automatic modulation classification (AMC) is an important stage in intelligent wireless communication receivers. It is a necessary process after signal detection, and before demodulation. It plays a vital role in various applications. Blind modulation classification is a very difficult task without information about the transmitted signal and the receiver parameters like carrier frequency, signal power, timing information, phase offset, existence of frequency-selective multipath fading, and time-varying channels in real-world applications. The AMC methods are divided into traditional and advanced methods. Traditional methods include likelihood-based (LB) and feature-based (FB) methods. The advanced methods include deep learning (DL) methods. In addition, the AMC methods are used to classify different modulation schemes such as ASK, PSK, FSK, PAM, and QAM with different orders and different signal-to-noise ratios (SNRs). This paper focuses on summarizing the AMC methoods, comparing between them, presenting the commercial software packages for AMC, and finally considering the new challenges in the implementation of AMC. K E Y W O R D S automatic modulation classification (AMC), deep learning (DL), feature-based (FB) methods, likelihood-based (LB) methods | INTRODUCTIONAutomatic modulation classification (AMC) is important in wireless communication systems used in military and civilian applications to enhance the efficiency of the spectrum utilization, redue the overhead, and resolve the shortage problems. Unfortunately, the restricted spectrum resources barely satisfy the ever-increasing demand for 5G 1,2 and Internet of Things (IoT) networks. 3 The AMC can be used for better management of the available spectrum. A simple block diagram of a communication system based on AMC is presented in Figure 1. 4 The AMC architecture contains two steps: signal preprocessing and a proper algorithm for classification. The preprocessing tasks involve reduction of noise, carrier frequency estimation, symbol period estimation, equalization, and signal power evaluation. On the other hand, the AMC methods comprise traditional methods including decisiontheoretic methods and feature-based methods 4,5 along with advanced methods 6 as shown in Figure 2.

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“…When the condition is not satisfied, the performance degrades rapidly. For the purpose of comparison, the classification probability achieved by the method in [3] is plotted in Fig. 3.…”

Section: The Procedures Of Proposed Approachmentioning

confidence: 99%

“…Flusser [2] used so-called fading invariants to classify the signals, but the construction of the fading invariants requires the channel impulse response must be symmetrical, which is impractical in the applications. Paris [3] presented a classifier that incorporated blind channel identification into universal classifier, and discriminated only between 4QAM and 8QAM. Wang [4] presented an approach that also uses the blind equalization to mitigate effects of multipath fading, then clusters signal spatial distribution, and the resulting centres are extracted to match the standard constellation patterns.…”

Section: Introductionmentioning

confidence: 99%

Modulation Classification In Multipath Fading Environments

Yang

Jiang

et al. 2007

2007 4th International Symposium on Wireless Communication Systems

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A new approach to classification of MQAM/MPSK signals in multipath fading environments is presented. The proposed approach, in which the two-step equalization strategy and higher-order cumulants based classifier are adopted, can effectively classify the MPSK and high-order QAM signals. The performance of proposed approach is evaluated by the computer simulations, which shows it has better classification ability. I. INTRODUCTIONModulation Classification of a received signal plays an important role in a variety of military and commercial applications. The examples include spectrum monitoring and management, surveillance and control of broadcasting activities, adaptive transmission schemes, and electronic warfare. Although significant progress has been made in the areas of modulation classification in flat fading environments, less attention is paid to modulation classification in multipath fading environments. In multipath fading environments, the inter-symbol interference (ISI) causes the distortion of received signal, which deteriorates the performance of classification methods developed for the flat fading channels.Among the existing methods, Lay[1] employed persurvivor processing (PSP) to address modulation classification with an assumption of a known multipath channel impulse. Flusser[2] used so-called fading invariants to classify the signals, but the construction of the fading invariants requires the channel impulse response must be symmetrical, which is impractical in the applications. Paris[3] presented a classifier that incorporated blind channel identification into universal classifier, and discriminated only between 4QAM and 8QAM. Wang[4] presented an approach that also uses the blind equalization to mitigate effects of multipath fading, then clusters signal spatial distribution, and the resulting centres are extracted to match the standard constellation patterns. Theoretically, the method can classify MASK, MPSK and MQAM signals, but the actual ability to classify is restricted by the performance of the blind equalization. This method, generally speaking, can only classify the signal with modulation order no more than 32. Xi[5] utilized higher-order statistics (HOS) to identify the channel, then the estimated channel was used to modify the higher-order cumulants based classifier for the flat fading channel. It should be noted that the classifier itself don't distinguish the signals with high order modulation. On the other hand, if the error of channel estimation is considered, the method performs more poorly.

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Modulation classification in unknown dispersive environments

Cited by 10 publications

References 2 publications

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

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

A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

Modulation Classification In Multipath Fading Environments

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