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稀疏信道下基于稀疏貝葉斯學(xué)習(xí)的精簡星座盲均衡算法

張凱,  于宏毅,  胡赟鵬,  沈智翔

張凱, 于宏毅, 胡赟鵬, 沈智翔. 稀疏信道下基于稀疏貝葉斯學(xué)習(xí)的精簡星座盲均衡算法[J]. 電子與信息學(xué)報, 2016, 38(9): 2255-2260. doi: 10.11999/JEIT151307
引用本文: 張凱, 于宏毅, 胡赟鵬, 沈智翔. 稀疏信道下基于稀疏貝葉斯學(xué)習(xí)的精簡星座盲均衡算法[J]. 電子與信息學(xué)報, 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貝葉斯學(xué)習(xí)的精簡星座盲均衡算法

doi: 10.11999/JEIT151307 cstr: 32379.14.JEIT151307
基金項目: 

國家自然科學(xué)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: 針對稀疏信道的盲均衡問題，在精簡星座均衡算法框架下建立線性模型，利用稀疏信道下均衡器固有的稀疏特性，引入具有稀疏促進(jìn)作用的先驗分布對均衡器系數(shù)加以約束，使用稀疏貝葉斯學(xué)習(xí)方法迭代求解均衡器系數(shù)得到最大后驗估計值。該文提出的均衡方法屬于數(shù)據(jù)復(fù)用類均衡算法的范疇，能夠適用于數(shù)據(jù)較短的應(yīng)用場合。與隨機梯度方法相比，算法性能受均衡器長度影響較小，收斂后誤符號率性能更好，仿真實驗驗證了算法的有效性。
    Abstract: This paper deals with blind equalization of sparse multipath channels. A linear model is built under the framework of Reduced Constellation Algorithm (RCA). And the inherent sparse nature of the equalizer is exploited by employing a sparse promoting prior distribution. Then, the sparse Bayesian learning iterative inference method is applied to the proposed model in order to obtain the optimal sparse equalizer. The new proposed algorithm, which belongs to data recycling equalization algorithm domain, can be applied to short packet data applications. Compared with traditional Stochastic Gradient Descent (SGD) method, the new proposed algorithm performs more steadily under different equalizer order and has superior steady-state Symbol-Error-Rate (SER) performance. The effectiveness of the proposed algorithm is verified by simulations.
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出版歷程
  • 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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