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

ZHANG Kai; YU Hongyi; HU Yunpeng; SHEN Zhixiang

doi:10.11999/JEIT151307

Volume 38 Issue 9

Sep. 2016

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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

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

doi: 10.11999/JEIT151307

Funds:

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

Received Date: 2015-11-23
Rev Recd Date: 2016-04-08
Publish Date: 2016-09-19

Abstract

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.
- Digital communication,
- Blind equalization,
- Sparse channel,
- Reduced Constellation Algorithm (RCA),
- Sparse Bayesian learning

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References(18)

References

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