A Novel Local Most Powerful Invariant Test Spectrum Sensing Method for Non-circular Signals

JIA Qiong; LI Bingbing

doi:10.11999/JEIT150974

Volume 38 Issue 6

Jun. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(6): 1391-1397

JIA Qiong, LI Bingbing. A Novel Local Most Powerful Invariant Test Spectrum Sensing Method for Non-circular Signals[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1391-1397. doi: 10.11999/JEIT150974

Citation:

JIA Qiong, LI Bingbing. A Novel Local Most Powerful Invariant Test Spectrum Sensing Method for Non-circular Signals[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1391-1397. doi: 10.11999/JEIT150974

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A Novel Local Most Powerful Invariant Test Spectrum Sensing Method for Non-circular Signals

doi: 10.11999/JEIT150974 cstr: 32379.14.JEIT150974

JIA Qiong¹,
LI Bingbing¹

Funds:

The National Natural Science Foundation of China (61271299), 111 Project (B08038)

Received Date: 2015-09-06
Rev Recd Date: 2016-03-03
Publish Date: 2016-06-19

Abstract

Abstract

Spectrum sensing is a key technology in the cognitive radio network, in order to protect the primary user, the sensing algorithms must have a high detection efficiency and detection accuracy. This paper mainly focuses on the spectrum sensing in MIMO environment. Considering that the non-circular signal is usually used in the communication system, a novel spectrum sensing method is proposed for non-circular signals based on the Locally Most Powerful Invariant Test (LMPIT). The theoretical threshold is derived according to the asymptotic distribution theorem. Finally, the detection performance comparisons with other methods in various channels are simulated respectively. The results show that the proposed method outperforms other algorithms and only need small sample numbers, thus having higher sensing accuracy and efficiency.
- Cognitive radio,
- Spectrum sensing,
- Non-circular signal,
- Locally Most Powerful Invariant Test (LMPIT)

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References(20)

References

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