Research on Modulation Recognition Method of MSK Signals Based on Wavelet Transform

Feng, Yongxin; Zhang, Xiaoyu; Qian, Bo; Li, Guangshen

doi:10.1109/iscid.2014.242

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…The phase constant of the k th symbol depends on the k th information code, the k−1 st information code, and their phase constants. The symbols before and after the MSK signal are therefore strongly correlated [16].…”

Section: F Msk Modulation Principlementioning

confidence: 99%

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Performance Evaluation of a Tactical Data-Link System Based on MSK and 16QAM

Zhao

Zhai

et al. 2021

IEEE Access

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Tactical data links are becoming more widely used in the military, as they can greatly improve the efficiency of various combat units. Such tactical data links must be able to transmit large volumes of data at high speed in complex communication environments while ensuring reliability. This paper studies the influence of 16-ary Quadrature Amplitude Modulation (16QAM) and Minimum Frequency-shift Keying(MSK) modulation on the transmission performance of data chain system. The simulation results show that 16QAM modulation can meet the requirements of tactical data link system for BER and data transmission rate, and the technical complexity is in the middle; the system BER of MSK modulation is lower, and a large number of data transmission can be realized at high speed. When the SNR reaches 50dB, the BER of MSK modulation can be reduced to 10 -4 order of magnitude, which is better than Quadrature Phase Shift Keying (QPSK) modulation, that is, the data chain system based on MSK modulation can meet the transmission requirements of data chain when the signal-to-noise ratio is large.

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Section: F Msk Modulation Principlementioning

confidence: 99%

“…Therefore, QAM-based modulation is a better choice for high-speed transmission systems at present. Minimum frequency-shift keying (MSK) is an improvement over binary frequency-shift keying (2FSK) [16], [17]. The conventional FSK system has been widely used, but its performance is poor in many aspects.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of a Tactical Data-Link System Based on MSK and 16QAM

Zhao

Zhai

et al. 2021

IEEE Access

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“…Theoretical analysis and simulation results show that this algorithm can identify the 6 digital modulation modes effectively and has good recognition effect. The proposed method can provide a good theoretical basis in other application field [18].…”

Section: Introductionmentioning

confidence: 98%

Modulation Recognition based on Wavelet Transform and Fractal Theory

Guo¹

2019

IJPE

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With the rapid development of communication technology, digital signal processing and other technologies, wireless communication environment is becoming more and more complex. Communication signals with different frequencies and modulated modes are usually scattered over a wide frequency band. In this paper, an improved algorithm based on wavelet transform and fractal theory is proposed. To improve the traditional fractal theory, wavelet transform is applied to the modulation signal, and then four fractal dimensions (Fractal box dimension, Petrosian fractal dimension, Katz fractal dimension and Sevcik fractal dimension) are used to extract the features. Through the simulation of the six modulation signals generated by Matlab, it can be seen that the recognition rate of the proposed method reaches 90% at the SNR of 2dB. Moreover, by comparing the method of this paper with the short-time Fourier transform and the fractional Fourier transform, we can find that the recognition rate of this method is 3% ~ 10% higher than the two comparison methods. It can be seen that the proposed method can effectively identify different signals in the case of low SNR.

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“…FB classification is essentially a two stage process, in the first step it extracts certain distinct features of the received baseband modulated signal. Spectral‐based [3, 4], higher‐order moment (HOM) [5], higher‐order cumulants (HOCs) [6], wavelet transform [7] and cyclic cumulants‐based features [8] are some of the most commonly used features in the literature. After feature extraction, at the second stage either a hierarchical tree‐based comparison approach is used to identify the modulation class [9] or machine learning‐based classification algorithms such as k‐nearest neighbour (KNN) [10], support vector machine [11] and last but not the least, neural networks (NNs) [12] and its variations can be used for classification.…”

Section: Introductionmentioning

confidence: 99%