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Superposed probabilistically shaped QAM constellation design based on nonlinear coding for MIMO visible light communication systems
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作者 xinyue guo Meixia Lu Qibin Zheng 《Chinese Optics Letters》 SCIE EI CAS CSCD 2024年第6期109-115,共7页
In this study, we propose a superposed probabilistically shaped(PS) quadrature amplitude modulation(QAM) constellation scheme for multiple-input multiple-output visible light communication systems. PS QAM signals are ... In this study, we propose a superposed probabilistically shaped(PS) quadrature amplitude modulation(QAM) constellation scheme for multiple-input multiple-output visible light communication systems. PS QAM signals are generated from a nonlinear coding equation that converts uniformly distributed 8-level signals into PS 9-or 10-level signals, which are then mapped into PS 9QAM or 10QAM signals. Square-shaped 9QAM and trapezoid-shaped 10QAM constellations are introduced to maximize the minimum Euclidean distance(MED) of the superposed constellation. Finally, the PS 9QAM and 10QAM signals are superposed with the 4QAM signals in a flipped manner to obtain PS 36QAM or 40QAM signals at the receiver, respectively.To exploit the temporal correlation of the resulting signal from nonlinear coding, we developed a detection algorithm based on Viterbi decoding. Experimental results confirmed the superiority of the proposed schemes by achieving a higher MED and stronger ability to resist nonlinearity. Compared with the traditional scheme, the peak-to-peak voltage dynamic ranges of the superposed 36QAM and 40QAM constellation schemes were improved by 52% and 48%, respectively. 展开更多
关键词 visible light communication superposed constellation nonlinear coding
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基于改进RFM与GMDH算法的MOOC用户流失预测 被引量:10
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作者 魏玲 郭新悦 《中国远程教育》 CSSCI 北大核心 2020年第9期39-43,61,76,77,共8页
MOOC在全球引起在线学习风靡的同时存在着严峻的挑战。通过调查发现MOOC用户中途放弃课程学习的现象十分严重。为最大限度保持和发展更多的MOOC用户,需要对其流失状态进行准确预测,确保对学习危机用户及时发出预警。本研究首先通过改进... MOOC在全球引起在线学习风靡的同时存在着严峻的挑战。通过调查发现MOOC用户中途放弃课程学习的现象十分严重。为最大限度保持和发展更多的MOOC用户,需要对其流失状态进行准确预测,确保对学习危机用户及时发出预警。本研究首先通过改进商业领域中RFM模型建立针对MOOC用户学习行为与流失预测的RFLP指标体系;其次通过直方图检验与卡方检验确定影响MOOC用户流失的特征变量;最后结合数据分组处理(GMDH)网络作为后置处理信息系统构建MOOC用户流失预测模型。利用该模型对中国大学MOOC上一门课程的学习者流失状态进行预测,并与经典决策树C5.0和支持向量机SVM算法进行实验对比。研究结果表明,该模型对MOOC用户流失判别的预测精度更高且在不同数据规模与极端值干扰下均有良好表现。 展开更多
关键词 MOOC 在线学习 学习者 学习者流失预测 学习预警 学习危机 GMDH算法 RFM模型 学习分析
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