An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems

Shah, Maqsood Hussain; Dang, Xiaoyu

doi:10.1631/fitee.1800306

Cited by 16 publications

(4 citation statements)

References 32 publications

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“…Chen et al [12] designed a maximum likelihood function estimator that could operate even in a fading channel environment. Shan et al [13] designed a likelihood algorithm based on the average likelihood ratio test method, independent of channel conditions and antenna numbers.…”

Section: Introductionmentioning

confidence: 99%

Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters

Xiuquan,

Zhen,

Yeyin

et al. 2024

Preprint

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Digital signal modulation recognition technology serves as the foundation and basis for signal demodulation, playing a crucial role in communication signal reconnaissance and holding significant research significance. This paper conducts research on digital signal modulation recognition technology from the perspectives of signal preprocessing and feature extraction. Seven modulation signals, namely 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, and OFDM, are selected as recognition targets. The paper compares the effects of four different endpoint detection algorithms on modulation signal recognition. The results indicate that, for these seven modulation signals, the short-time energy entropy ratio algorithm performs the best, achieving a correct endpoint detection rate of over 93% in a Gaussian channel with a signal-to-noise(SNR) ratio of 0dB. Based on this, three different denoising algorithms are introduced to further enhance the performance of the short-time energy entropy ratio algorithm. The results show that the wavelet denoising algorithm achieves the greatest improvement in the performance of the short-time energy entropy ratio algorithm, with a short processing time. In a Gaussian channel with a SNR ratio greater than − 10dB, the endpoint detection accuracy of this algorithm can be maintained at over 95%. Finally, for the accurate identification and differentiation of 2FSK and 4FSK, this paper optimized the relevant algorithm in the cyclic spectrum. The kurtosis coefficient value Kur of the cyclic spectrum parameter matrix at the cyclic frequency \(\alpha =0\) is utilized to distinguish between these two signals. The results show that, at a SNR ratio of 4dB, the modulation recognition algorithm proposed in this paper can effectively distinguish between these two signals, achieving a recognition accuracy of over 99%.

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Section: Introductionmentioning

confidence: 99%

Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters

Xiuquan,

Zhen,

Yeyin

et al. 2024

Preprint

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.

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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.

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“…The works [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] related to feature-based pattern recognition approach have been listed in Table 1. In the literature, authors have been utilized various classifier structures to classify the modulation formats [23,24]. The classifiers are based on hidden Markov model (HMM), neural network based, support vector machine based, convolutional neural network based, recurrent neural network based, deep neural network based and Gabor filter network [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Optimized Polynomial Classiﬁer for Classiﬁcation of M-PSK Signals

Muhammad¹,

Sarfraz²,

Ghauri³

et al. 2021

MMEP

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Automatic modulation classiﬁcation (AMC) is the emerging research area for military and civil applications. In this paper, M-PSK signals are classiﬁed using the optimized polynomial classiﬁer. The distinct features i.e., higher order cumulants (HOC’s) are extracted from the noisy received signal and the dataset is generated with different number of samples, various SNR’s and on several fading channels. The proposed classiﬁer structure classiﬁes the overall modulation classiﬁcation problem into binary sub-classiﬁcations. In each sub-classiﬁcation, the extracted features are expanded using polynomial expansion into higher dimension space. In higher dimension space numerous non-linearly separable classes becomes linearly separable. The performance of the proposed classiﬁer is evaluated on Rayleigh and Rician fading channels in the presence of additive white gaussian noise (AWGN). The polynomial classiﬁer performance is optimized using one of the famous heuristic computational techniques i.e., Genetic Algorithm (GA). The extensive simulations have been carried with and without optimization, which shows relatively better percentage classiﬁcation accuracy (PCA) as compared with the state of art existing techniques.

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An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems

Cited by 16 publications

References 32 publications

Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters

Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters

Automatic modulation recognition based on CNN and GRU

Optimized Polynomial Classiﬁer for Classiﬁcation of M-PSK Signals

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