Hybrid Maximum Likelihood Modulation Classification for Continuous Phase Modulations

Yuan, Yabo; Zhao, Peng; Wang, Bo; Wu, Bin

doi:10.1109/lcomm.2016.2517007

Cited by 24 publications

(4 citation statements)

References 9 publications

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“…Research in the modulation classification of digital signals approximately started since forty years ago, and the robustness evaluation of conventional AMC methods under the AWGN channel has been well established in the literature [37][38][39]. Similarly, the use of AWGN as the channel model is a common practice for communication engineers and researchers [19,40].…”

Section: Under Different Channel Modelsmentioning

confidence: 99%

“…However, the constellation shape can be classified into M-PSK and M-QAM, and at the same time, they are sensitive to several wireless channels that could seriously make confusions in work; these comprise frequency offset, phase rotation, and the application of raised cosine roll-off filters. Under this situation, the symbol rate and frequency offset setting must be more accurate during the periods of presentment, and at the same time, the most widely used features are a cyclic spectrum and cyclic frequency that could help multiple unwanted signals to be detected with each of temporal and frequency-based overlaps [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Ali

Erçelebi

2019

Scientific Programming

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Computing the distinct features from input data, before the classification, is a part of complexity to the methods of automatic modulation classification (AMC) which deals with modulation classification and is a pattern recognition problem. However, the algorithms that focus on multilevel quadrature amplitude modulation (M-QAM) which underneath different channel scenarios is well detailed. A search of the literature revealed that few studies were performed on the classification of high-order M-QAM modulation schemes such as 128-QAM, 256-QAM, 512-QAM, and 1024-QAM. is work focuses on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting higher order cumulant's (HOC) features from input data received raw. e HOC signals were extracted under the additive white Gaussian noise (AWGN) channel with four effective parameters which were defined to distinguish the types of modulation from the set: 4-QAM∼1024-QAM. is approach makes the classifier more intelligent and improves the success rate of classification. e simulation results manifest that a very good classification rate is achieved at a low SNR of 5 dB, which was performed under conditions of statistical noisy channel models. is shows the potential of the logarithmic classifier model for the application of M-QAM signal classification. furthermore, most results were promising and showed that the logarithmic classifier works well under both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero). It can be considered as an integrated automatic modulation classification (AMC) system in order to identify the higher order of M-QAM signals that has a unique logarithmic classifier to represent higher versatility. Hence, it has a superior performance in all previous works in automatic modulation identification systems.

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Section: Under Different Channel Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Ali

Erçelebi

2019

Scientific Programming

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“…Two general categories of algorithms can be used to solve AMC problems: likelihood-based (LB) [6][7][8][9][10][11][12] and feature-based (FB) [13][14][15][16][17][18][19][20][21][22]. LB algorithms are derived from three the likelihood ratio tests: average likelihood, generalized likelihood, and hybrid ratio test likelihood.…”

Section: Introductionmentioning

confidence: 99%

A Novel Self-Learning Filters for Automatic Modulation Classification Based on Deep Residual Shrinking Networks

2023

KSII TIIS

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Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and openset signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

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“…But the computational complexity of this method is high. 28,29 The maximum likelihood-based method is optimal in the Bayesian criterion, but its computational complexity is high and is easy to have the problem of model mismatch. 30,31 The feature-based method accomplishes the modulation classification by extracting features from the received signal.…”

Section: Introductionmentioning

confidence: 99%

Wavelet transform and cyclic cumulant based modulation classification in wireless network

Dou

2019

International Journal of Distributed Sensor Networks

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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.

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Hybrid Maximum Likelihood Modulation Classification for Continuous Phase Modulations

Cited by 24 publications

References 9 publications

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

A Novel Self-Learning Filters for Automatic Modulation Classification Based on Deep Residual Shrinking Networks

Wavelet transform and cyclic cumulant based modulation classification in wireless network

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