Design and evaluation of hierarchical hybrid automatic modulation classifier using software defined radios

Jagannath, Jithin; Polosky, Nicholas; Sheaffer, Brendan; Foulke, Svetlana; Theagarajan, Lakshmi Narasimhan; Varshney, Pramod K.

doi:10.1109/ccwc.2017.7868362

Cited by 24 publications

(19 citation statements)

References 17 publications

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“…Traditionally, AMC techniques are broadly classified as maximum likelihood-based approaches [149,150,151,152,153], feature-based approaches [154,155,156] and hybrid techniques [157]. Prior to the introduction of ML, AMC tasks were often solved using complex hand engineered features computed from the raw signal.…”

Section: Modulation Classificationmentioning

confidence: 99%

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

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The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters (e.g., frequency band, symbol modulation, coding rate, route selection, etc.) to reach the network's optimal operating point. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning's popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms.This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.

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Section: Modulation Classificationmentioning

confidence: 99%

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

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“…Therefore, γ max can be used to discriminate between M-ASK and M-QAM in addition to constant amplitude M-FSK and M-PSK digital modulation schemes [4,31,33,[35][36][37][38][39]. The average value of γ max is used to discriminate among continuous phase FSK, Gaussian FSK, and Gaussian minimum shift keying (GMSK) [40]. In addition, several studies have used these features to discriminate the order of modulation schemes, such as M-ASK and M-FSK [41].…”

Section: Kurtosis Of the Normalized Centered Instantaneous Amplitudementioning

confidence: 99%

“…The second utilization of cumulants is introduced in [50] by comparing the estimated value to the real value. HOS features are used to classify M-PSK and M-QAM modulation types, which are robust against AWGN [12,40,48,[51][52][53][54][55]. Furthermore, the multipath channel effects can be easily modelled using the HOS features [12,31,56,57], which are robust to frequency, phase offset, and timing errors [52,53,58].…”

Section: Statistical Features For Mrmentioning

confidence: 99%

“…Previous studies [48,49,52,53,58,60,88] used HOS as the features for DT, whereas [59] combined instantaneous and HOS as the features for DT. In [40], DT used a hierarchal hybrid by combining FB and LB, where the first stage was to discriminate among MFSK, PSK, and QAM subclasses depending on the variance threshold of instantaneous amplitude. The second stage in each DT depended on predefined threshold to recognize the types of FSK and fourth HOCs for differentiating M-PSK and M-QAM.…”

Section: Dtmentioning

confidence: 99%

“…In [48,63], the researchers used two and three features, respectively, based on the DT algorithm and hence reduced the computational complexity. In [40], a hybrid approach was used that implements both the FB and LB modulation classification for the efficient classification of multiple linear and nonlinear modulation formats. This hybrid classification approach is helpful in both reducing time and reducing computational complexity.…”

Section: Complexity Analysis For Classifiersmentioning

confidence: 99%

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Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

et al. 2019

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The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.

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Neural Networks for Signal Intelligence

Jagannath

Polosky

Jagannath

et al. 2019

Machine Learning for Future Wireless Communications

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Design and evaluation of hierarchical hybrid automatic modulation classifier using software defined radios

Cited by 24 publications

References 17 publications

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Neural Networks for Signal Intelligence

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