Abstract
Automatic modulation classification (AMC) is a fundamental process in wireless communication. In our work, we proposed a Convolutional neural network based Automatic Modulation Classification (AMC) over Rician fading channel. We constructed a system to simulate the received modulated samples through the fading channel where 6 commonly used modulation methods are considered. The scheme of proposed convolutional neural network classifier is illustrated, and the classification accuracy is demonstrated. The classification accuracy of different mod methods are shown, respectively. And accuracies among different K-factor and maximum Doppler shift are investigated. Finally, we demonstrated the bit error rate of the system assume a successive modulation detection.
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Wang, Z., Liang, Q. (2022). CNN-Based Automatic Modulation Classification over Rician Fading Channel. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 878. Springer, Singapore. https://doi.org/10.1007/978-981-19-0390-8_35
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DOI: https://doi.org/10.1007/978-981-19-0390-8_35
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