MIMO Spectrum Sensing Method Based on the Local Variance

Jia Qiong; Li Bing-bing

doi:10.11999/JEIT141540

Volume 37 Issue 7

Jul. 2015

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Jia Qiong, Li Bing-bing. MIMO Spectrum Sensing Method Based on the Local Variance[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1525-1530. doi: 10.11999/JEIT141540

Citation:

Jia Qiong, Li Bing-bing. MIMO Spectrum Sensing Method Based on the Local Variance[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1525-1530. doi: 10.11999/JEIT141540

Jia Qiong, Li Bing-bing. MIMO Spectrum Sensing Method Based on the Local Variance[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1525-1530. doi: 10.11999/JEIT141540

Citation:

Jia Qiong, Li Bing-bing. MIMO Spectrum Sensing Method Based on the Local Variance[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1525-1530. doi: 10.11999/JEIT141540

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MIMO Spectrum Sensing Method Based on the Local Variance

doi: 10.11999/JEIT141540

Jia Qiong¹,
Li Bing-bing¹

Received Date: 2014-12-04
Rev Recd Date: 2015-02-09
Publish Date: 2015-07-19

Abstract

Abstract

Efficient and accurate spectrum sensing is a necessary part in cognitive radio network. This paper focuses on the spectrum sensing in MIMO environment. Based on the fact that the correlation structure of the received signals is different between the cases of signal-presence and signal-absence, a new concept of local variance is presented and the test statistic is constructed by the local variance. The theoretical threshold is derived according to the asymptotic distribution theorem. Finally, the detection performance comparison with other methods in AWGN channel and Rayleigh channel are simulated respectively. The results show that the proposed method outperforms other algorithms and needs small sample numbers, thus it has higher sensing accuracy and efficiency.
- Cognitive radio,
- Spectrum sensing,
- Sample covariance matrix,
- Local variance

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References(15)

References

Axell E, Leus G, Larsson E G, et al.. Spectrum sensing for cognitive radio: State-of-the-art and recent advances[J]. IEEE Signal Processing Magazine, 2012, 29(3): 101-116.

Yucek T and Arslan H. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys Tutorials, 2009, 11(1): 116-130.

Ghozzi M, Marx F, Dohler M, et al.. Cyclostatilonarilty- based test for detection of vacant frequency bands[C]. 1st IEEE International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Mykonos Island, 2006: 1-5.

Zhu Y, Liu J, Feng Z, et al.. Sensing performance of efficient cyclostationary detector with multiple antennas in multipath fading and lognormal shadowing environments[J]. Journal of Communications and Networks, 2014, 16(2): 162-171.

Sutton P D, ?zgl B, and Doyle L E. Cyclostationary signatures for LTE advanced and beyond[J]. Physical Communication, 2014, 2014(10): 179-189.

Srinu S and Sabat S L. Effective cooperative wideband sensing using energy detection under suspicious cognitive radio network[J]. Computers Electrical Engineering, 2013, 39(4): 1153-1163.

Sofotasios P C, Rebeiz E, Zhang L, et al.. Energy detection based spectrum sensing over and extreme fading channels[J]. IEEE Transactions on Vehicular Technology, 2013, 62(3): 1031-1040.

Gokceoglu A, Dikmese S, Valkama M, et al.. Energy detection under IQ imbalance with single-and multi-channel direct-conversion receiver: analysis and mitigation[J]. IEEE Journal on Selected Areas in Communications, 2014, 32(3): 411-424.

Zeng Y and Liang Y C. Maximum-minimum eigenvalue detection for cognitive radio[C]. Proceedings. IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, Athens, 2007: 1-5.

Lim T J, Zhang R, Liang Y C, et al.. GLRT-based spectrum sensing for cognitive radio[C]. IEEE Global Telecommunications Conference (GLOBECOM), New Orleans, LO, 2008: 1-5.

Lpez-Valcarce R, Vazquez-Vilar G, and Sala J. Multiantenna spectrum sensing for cognitive radio: overcoming noise uncertainty[C]. 2010 2nd IEEE International Workshop on Cognitive Information Processing (CIP), Elba, 2010: 310-315.

Zeng Y and Liang Y C. Spectrum-sensing algorithms for cognitive radio based on statistical covariances[J]. IEEE Transactions on Vehicular Technology, 2009, 58(4): 1804-1815.

Huang L, So H C, and Qian C. Volume-based method for spectrum sensing[J]. Digital Signal Processing, 2014, 2014(28): 48-56.

John G Proakis. Digital Communications, Fifth Edition [M]. New York, McGraw-Hill, 2009: 46-48.

Abdi A and Kaveh M. A space-time correlation model for multielement antenna systems in mobile fading channels[J]. IEEE Journal on Selected Areas in Communications, 2002, 20(3): 550-560.

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