Symbol Rate Estimation Based on Sparse Bayesian Learning

JIN Yan; TIAN Tian; JI Hongbing

doi:10.11999/JEIT170906

Volume 40 Issue 7

Jul. 2018

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JIN Yan, TIAN Tian, JI Hongbing. Symbol Rate Estimation Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1598-1603. doi: 10.11999/JEIT170906

Citation:

JIN Yan, TIAN Tian, JI Hongbing. Symbol Rate Estimation Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1598-1603. doi: 10.11999/JEIT170906

JIN Yan, TIAN Tian, JI Hongbing. Symbol Rate Estimation Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1598-1603. doi: 10.11999/JEIT170906

Citation:

JIN Yan, TIAN Tian, JI Hongbing. Symbol Rate Estimation Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1598-1603. doi: 10.11999/JEIT170906

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Symbol Rate Estimation Based on Sparse Bayesian Learning

doi: 10.11999/JEIT170906

JIN Yan TIAN Tian JI Hongbing

Funds:

The National Natural Science Foundation of China (61201286), The Natural Science Foundation of Shannxi Province (2014JMS304)

Received Date: 2017-09-26
Rev Recd Date: 2018-03-14
Publish Date: 2018-07-19

Abstract

Abstract

Existing methods for symbol rate estimation of phase coded signals require amounts of sensing data, and are of high computational complexity. This paper analyzes the structure characteristics of BPSK signals, which are employed as the prior information for signal compressing and dimensionality reduction. The sensing matrix can be split into sine and cosine component, combined with the Fourier transform parity. According to the fact that the real and imaginary components of a complex value share the same support set, the symbol rate estimation can be obtained, using unilateral spectral of the delay-product vector reconstructed by multi-task Bayesian compressive sensing. Theoretical analysis and simulation results show that compared with other parameter estimation algorithms, the proposed method can reduce the measurements and significantly improve the real-time ability, while keeping the high reconstruction accuracy.
- Symbol rate estimation、Sparse Bayesian learning、Cyclic autocorrelation、Unilateral spectrum,

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References(16)

References

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