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稀疏信道下基于稀疏贝叶斯学习的精简星座盲均衡算法

张凯 于宏毅 胡赟鹏 沈智翔

张凯, 于宏毅, 胡赟鹏, 沈智翔. 稀疏信道下基于稀疏贝叶斯学习的精简星座盲均衡算法[J]. 电子与信息学报, 2016, 38(9): 2255-2260. doi: 10.11999/JEIT151307
引用本文: 张凯, 于宏毅, 胡赟鹏, 沈智翔. 稀疏信道下基于稀疏贝叶斯学习的精简星座盲均衡算法[J]. 电子与信息学报, 2016, 38(9): 2255-2260. doi: 10.11999/JEIT151307
ZHANG Kai, YU Hongyi, HU Yunpeng, SHEN Zhixiang. Reduced Constellation Equalization Algorithm for Sparse Multipath Channels Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2255-2260. doi: 10.11999/JEIT151307
Citation: ZHANG Kai, YU Hongyi, HU Yunpeng, SHEN Zhixiang. Reduced Constellation Equalization Algorithm for Sparse Multipath Channels Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2255-2260. doi: 10.11999/JEIT151307

稀疏信道下基于稀疏贝叶斯学习的精简星座盲均衡算法

doi: 10.11999/JEIT151307
基金项目: 

国家自然科学基金(61201380, 61501517)

Reduced Constellation Equalization Algorithm for Sparse Multipath Channels Based on Sparse Bayesian Learning

Funds: 

The National Natual Science Foundation of China (61201380, 61501517)

  • 摘要: 针对稀疏信道的盲均衡问题,在精简星座均衡算法框架下建立线性模型,利用稀疏信道下均衡器固有的稀疏特性,引入具有稀疏促进作用的先验分布对均衡器系数加以约束,使用稀疏贝叶斯学习方法迭代求解均衡器系数得到最大后验估计值。该文提出的均衡方法属于数据复用类均衡算法的范畴,能够适用于数据较短的应用场合。与随机梯度方法相比,算法性能受均衡器长度影响较小,收敛后误符号率性能更好,仿真实验验证了算法的有效性。
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    RUAN Xiukai, JIANG Xiao, LIU Li, et al. A novel Bussgang category of blind equalization with exponential expanded multi-modulus algorithm[J]. Journal of Electronics Information Technology, 2013, 35(9): 2188-2193. doi: 10.3724/SP.J.1146.2012.01544.
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出版历程
  • 收稿日期:  2015-11-23
  • 修回日期:  2016-04-08
  • 刊出日期:  2016-09-19

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