A New Deep Architecture for Digital Signal Modulation Classification over Rician Fading

Autor:	Na Pu, Xiang Ding, Wendong Yang, Peicong Hu, Yunfei Peng
Rok vydání:	2021
Předmět:	Artificial neural network Computer science business.industry Modulation Deep learning Rician fading Signal modulation Digital signal Pattern recognition Artificial intelligence Communications system business Convolutional neural network
Zdroj:	ICCCS
DOI:	10.1109/icccs52626.2021.9449146
Popis:	In this paper, we simulate digital signals of six usual modulation patterns considering Rician fading and propose a new deep neural network structure (CGDNN) combining Convolutional Neural Networks (CNNs) with Gated Recurrent Unit (GRU). Simulation results show that the proposed structure has the ability to classify the signal modulation patterns regardless the influence of different Rician K-factors and has better performance than conventional structures including CNNs and CLDNNs.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::bb5e074fa73f0d6e8446224b2e8fa313 https://doi.org/10.1109/icccs52626.2021.9449146 Zobrazit plný text záznamu