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178 results on '"Li, Xianxu"'

28. Carrier Robust Reversible Watermark Model Based on Image Block Chain Authentication

Author

Gao, Yating, Zhang, Ru, Li, Xianxu, Liu, Jianyi, Zhao, Kaifeng, Cheng, Xue, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Peng, Yuxin, editor, Hu, Shi-Min, editor, Gabbouj, Moncef, editor, Zhou, Kun, editor, Elad, Michael, editor, and Xu, Kun, editor

Published
2021
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38. Environmentally relevant concentrations of butyl benzyl phthalate triggered oxidative stress and apoptosis in adult zebrafish (Danio rerio) liver: Combined analysis at physiological and molecular levels

Author

Wang, Qian, primary, Yao, Xiangfeng, additional, Jiang, Nan, additional, Zhang, Juan, additional, Liu, Guanyong, additional, Li, Xianxu, additional, Wang, Can, additional, Yang, Zhongkang, additional, Wang, Jinhua, additional, Zhu, Lusheng, additional, and Wang, Jun, additional

Published
2023
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46. Uplink NOMA signal transmission with convolutional neural networks approach

Author

Chang Qing, Li Xianxu, and Lin Chuan

Subjects
0209 industrial biotechnology, Signal processing, business.industry, Computer science, Deep learning, MIMO, 02 engineering and technology, Spectral efficiency, Convolutional neural network, 020901 industrial engineering & automation, Single antenna interference cancellation, Telecommunications link, Electronic engineering, Artificial intelligence, business, Decoding methods, Computer Science::Information Theory
Abstract

Non-orthogonal multiple access (NOMA), featuring high spectrum efficiency, massive connectivity and low latency, holds immense potential to be a novel multi-access technique in fifth- generation (5G) communication. Successive interference cancellation (SIC) is proved to be an effective method to detect the NOMA signal by ordering the power of received signals and then decoding them. However, the error accumulation effect referred to as error propagation is an inevitable problem. In this paper, we propose a convolutional neural networks (CNNs) approach to restore the desired signal impaired by the multiple input multiple output (MIMO) channel. Especially in the uplink NOMA scenario, the proposed method can decode multiple users' information in a cluster instantaneously without any traditional communication signal processing steps. Simulation experiments are conducted in the Rayleigh channel and the results demonstrate that the error performance of the proposed learning system outperforms that of the classic SIC detection. Consequently, deep learning has disruptive potential to replace the conventional signal detection method.

Published
2020

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