Compressed Sensing Channel Estimation Algorithm Based on Deterministic Sensing with Golay Complementary Sequences

YAO Zhiqiang; LI Guanglong; WANG Shiguo; YOU Zhihong

doi:10.11999/JEIT150594

Volume 38 Issue 2

Feb. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(2): 282-287

YAO Zhiqiang, LI Guanglong, WANG Shiguo, YOU Zhihong. Compressed Sensing Channel Estimation Algorithm Based on Deterministic Sensing with Golay Complementary Sequences[J]. Journal of Electronics & Information Technology, 2016, 38(2): 282-287. doi: 10.11999/JEIT150594

Citation:

YAO Zhiqiang, LI Guanglong, WANG Shiguo, YOU Zhihong. Compressed Sensing Channel Estimation Algorithm Based on Deterministic Sensing with Golay Complementary Sequences[J]. Journal of Electronics & Information Technology, 2016, 38(2): 282-287. doi: 10.11999/JEIT150594

Citation:

YAO Zhiqiang, LI Guanglong, WANG Shiguo, YOU Zhihong. Compressed Sensing Channel Estimation Algorithm Based on Deterministic Sensing with Golay Complementary Sequences[J]. Journal of Electronics & Information Technology, 2016, 38(2): 282-287. doi: 10.11999/JEIT150594

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Compressed Sensing Channel Estimation Algorithm Based on Deterministic Sensing with Golay Complementary Sequences

doi: 10.11999/JEIT150594

Funds:

The National Natural Science Foundation of China (61372127), The Natural Science Foundation of Hunan Province (13JJ3065)

Received Date: 2015-05-18
Rev Recd Date: 2015-09-16
Publish Date: 2016-02-19

Abstract

Abstract

To deal with problems of large Peak-to-Average Power Ratio (PAPR) and computation complexity in existing channel estimation algorithm based on compressed sensing, Golay complementary sequence is utilized to estimate sparse channel as a deterministic training sequence. Estimation model and algorithm are provided in detail. The PAPR of this method is deduced and its performance, PAPR and computation complexity are compared with Gaussian random sequence. The simulation result indicates that Golay sequence and Gaussian random sequence can reconstruct nonzero tap coefficients. But Golay sequence outperforms Gaussian random sequence both in the Mean Square Error (MSE) and Match Rate (MR) for estimating sparse channel impulse response. And the computation and PAPR are reduced significantly in the OFDM system.
- Channel estimation,
- Compressed Sensing (CS),
- Golay complementary sequences,
- Sparse multipath

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References(23)

References

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