Wideband compressed spectrum sensing based on modified sparsity adaptive matching pursuit algorithm

Jiao Chuanhai; Li Yongcheng

doi:10.11884/HPLPB201830.170395

Volume 30 Issue 3

Mar. 2018

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Article Navigation > High Power Laser and Particle Beams > 2018 > 30(3): 033203

Jiao Chuanhai, Li Yongcheng. Wideband compressed spectrum sensing based on modified sparsity adaptive matching pursuit algorithm[J]. High Power Laser and Particle Beams, 2018, 30: 033203. doi: 10.11884/HPLPB201830.170395

Citation:

Jiao Chuanhai, Li Yongcheng. Wideband compressed spectrum sensing based on modified sparsity adaptive matching pursuit algorithm[J]. High Power Laser and Particle Beams, 2018, 30: 033203. doi: 10.11884/HPLPB201830.170395

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Wideband compressed spectrum sensing based on modified sparsity adaptive matching pursuit algorithm

doi: 10.11884/HPLPB201830.170395

Jiao Chuanhai^{1, 2
,},
Li Yongcheng¹

1.
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 473000, China
2.
Army Artillery and Air Defense Academy, Hefei 230031, China

Funds:

State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Open Foundation CEMEE2015Z0203B

Natural Science Foundation of Anhui Province 1608085QF143

More Information

Author Bio:
Jiao Chuanhai(1983—), male, PhD, engaged in wireless signal processing and cognitive radio research; jiao_chuanhai@126.com
Received Date: 2017-10-10
Rev Recd Date: 2017-11-13
Publish Date: 2018-03-15

Abstract

Abstract

Traditional spectrum sensing based on compressed sensing assumes that the sparsity is known, while actually, it is unknown. To solve the problem, we proposed a modified sparsity adaptive matching pursuit (MSAMP) algorithm. The MSAMP algorithm pre-estimates the value of sparsity in the process of choosing the support set. A cooperative wideband spectrum compressed sensing method is developed based on the MSAMP algorithm and sequential compression inspection technique. Theoretical analysis and simulation results show that the method can enhance the spectrum sensing capability without a priori knowledge of sparsity.
- spectrum sensing,
- compressed sensing,
- reconstruction algorithm,
- sparsity

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References(19)

References

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