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

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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.

Key words： Digital communication Blind equalization Sparse channel Reduced Constellation Algorithm (RCA) Sparse Bayesian learning

Received: 23 November 2015 Published: 25 May 2016

PACS:

TN914.3

Fund:

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

Corresponding Authors: ZHANG Kai E-mail: zk_xxgc@163.com

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	ZHANG Kai
	YU Hongyi
	HU Yunpeng
	SHEN Zhixiang

Cite this article:

ZHANG Kai,YU Hongyi,HU Yunpeng等. Reduced Constellation Equalization Algorithm for Sparse Multipath Channels Based on Sparse Bayesian Learning[J]. JEIT, 2016, 38(9): 2255-2260.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT151307 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I9/2255

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