The performance of feature-based classification of digital modulations under varying SNR and fading channel conditions

Uys, Lisa; Gouws, M.; Strydom, J.J.; Helberg, A.S.J.

doi:10.1109/afrcon.2017.8095481

Cited by 11 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They can be obtained in many ways, and a common method used is Hilbert transform [34]. Instantaneous amplitude A(t), phase ø(t), and frequency f N can be obtained using Equations (5)-(7), respectively, [8,35]:…”

Section: Spectral Features For Mrmentioning

confidence: 99%

“…Generalized likelihood ratio test (GLRT) computes the probability density function (PDF) of incoming signals by applying maximum likelihood estimations (MLEs) based on unknown quantities. Then, the LF defines the most possible modulation type of the signal [8]. In addition to the two techniques, hybrid likelihood ratio test (HLRT), quasi ALRT, and quasi HLRT have been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…The FB approach can be considered a mapping relationship that conducts mapping of time-series signals to the feature field, and these featured parameters are used for signal recognition [8]. Feature extraction is based on various aspects of signals, such as instantaneous amplitude, phase, frequency, wavelet transform (WT), zero-crossing intervals, statistical features (e.g., higher-order moments and cumulants), cyclic cumulants [8,12], and signal spectra [6]. In feature selection, the most relevant features must be utilized to improve the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Spectral Features For Mrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…ALRT takes unknown variables as random variables and calculates the likelihood function by computing the average value. GLRT calculates the probability density function of the input signal on the basis of the maximum likelihood estimation of unknown quantity and determines the modulation mode accordingly [2][3][4] . The LB classification method can theoretically obtain the optimal classification performance, but it requires substantial prior knowledge and a considerable amount of computation.…”

Section: Introductionmentioning

confidence: 99%

Automatic modulation recognition based on CNN and GRU

Liu

Zhang

Zhou

2022

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

Based on a comparative analysis of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, we optimize the structure of the GRU network and propose a new modulation recognition method based on feature extraction and a deep learning algorithm. High-order cumulant, Signal-to-Noise Ratio (SNR), instantaneous feature, and the cyclic spectrum of signals are extracted firstly, and then input into the Convolutional Neural Network (CNN) and the parallel network of GRU for recognition. Eight modulation modes of communication signals are recognized automatically. Simulation results show that the proposed method can achieve high recognition rate at low SNR.

show abstract

“…32,33 The key to implement this method is how to find the features that can effectively classify different modulation schemes, that is, selection of feature space. 34,35 Ozdemir et al 36 proposed a modulation classification method based on high-order cumulants. The idea was first to use the independent component analysis to estimate the multiple-input multiple-output (MIMO) channel matrix, second to calculate the estimate of the transmitted signal vector, finally to calculate the fourth-order cumulants of transmitted symbol and fourth-order cumulants formed the feature vector.…”

Section: Introductionmentioning

confidence: 99%

Wavelet transform and cyclic cumulant based modulation classification in wireless network

Dou

2019

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

With the development of Internet of things, a large number of embedded devices are interconnected by ad hoc and wireless network. The embedded devices can work correctly, only by ensuring correct communication between them. Identifying modulation scheme is the precondition to ensure the correct communication between embedded devices. However, in the multipath channel, ensuring the correct communication between embedded devices is a great challenge. Multipath channel always exists in the wireless network. However, most of the available modulation classification algorithms are based on ideal channel. It leads to the low-modulation classification probability in multipath channel. To resolve this problem, we propose a novel modulation classification algorithm. The proposed algorithm can classify signal without prior information about multipath channel. We calculate feature by high-order cyclic cumulant and wavelet transform. The feature is robust to multipath channel. The simulation results show that the proposed algorithm can achieve the much better classification accuracy than the available method in multipath channel.

show abstract

The performance of feature-based classification of digital modulations under varying SNR and fading channel conditions

Cited by 11 publications

References 10 publications

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

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

Automatic modulation recognition based on CNN and GRU

Wavelet transform and cyclic cumulant based modulation classification in wireless network

Contact Info

Product

Resources

About