基于马尔科夫模型的认知无线电动态双门限能量检测策略

刘玉磊; 梁俊; 肖楠; 扈瑜龙; 胡猛

doi:10.11999/JEIT151400

基于马尔科夫模型的认知无线电动态双门限能量检测策略

doi: 10.11999/JEIT151400

基金项目:

国家自然科学基金(61501496)，陕西省自然科学基金(2012JM8004)，航空科学基金(2013ZC15008)

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出版历程
- 收稿日期: 2015-12-14
- 修回日期: 2016-05-16
- 刊出日期: 2016-10-19

Dynamic Double Threshold Energy Detection Based on Markov Model in Cognitive Radio

Funds:

The National Natural Science Foundation of China (61501496), The Natural Science Foundation of Shaanxi Province (2012JM8004), The Aeronautical Science Foundation of China (2013ZC15008)

摘要

摘要: 该文针对低信噪比条件下频谱感知精度低的问题，提出一种基于马尔科夫模型的动态双门限能量检测算法。该算法根据信道时变特性建立基于马尔科夫的频谱占用模型，利用信道历史状态信息实现模型参数的修正。然后采用先听后说的机制对处于双门限之间的困惑信道状态进行判决，并详细分析了噪声不确定性对频谱感知性能的影响。在此基础上，为了克服噪声不确定性的影响，以频谱检测概率最大为优化目标，对双门限进行实时更新。仿真结果表明，所提频谱感知算法在减小噪声不确定性影响的同时增加了频谱感知精度，降低了认知用户的感知时间。
- 频谱感知 /
- 能量检测 /
- 马尔科夫模型 /
- 动态双门限
Abstract: With the development of the technology of cognitive radio, the standards of spectrum sensing performance become the higher and the higher, especially in low Signal-to-Noise Ratio (SNR) environments. A Dynamic Double-threshold Energy sensing method based on Markov Model (DDEMM) is proposed in this paper. By following the double-threshold energy sensing approach, the modified Markov model that accounts for the time varying nature of the channel occupancy is presented to resolve the confused channel state. Furthermore, in order to overcome the effect of noise uncertainty, a dynamic double-threshold spectrum sensing method is proposed, which adjusts its thresholds according to the achievable maximal detection probability. The results of extensive simulation demonstrate that the proposed DDEMM can achieve better detection performance than the conventional double-threshold energy sensing schemes, especially under very low SNR region.
- Spectrum sensing /
- Energy detection /
- Markov model /
- Dynamic double-threshold

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参考文献(20)

YUE W J, ZHENG B Y, MENG Q M, et al. Robust cooperative spectrum sensing schemes for fading channels in cognitive radios networks[J]. Sicence China, 2011, 41(2): 207-218. doi: 10.1007/s11432-010-4147-x.

LOREDANA A and DANIELE T. Statistical modeling of spectrum sensing energy in multi-hop cognitive radio networks[J]. IEEE Signal Processing Letters, 2015, 22(3): 356-360. doi: 10.1109/LSP.2014.2360234.

YCEK and ARSLAN H. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys Tutorials, 2009, 11(1): 116-130. doi: 10.1109/SURV.2009.090109.

GILBERT S, KUHN F, NEWPORT C, et al. Efficient communication in cognitive radio networks[C]. Proceedings of ACM SPDC. California, USA, 2015: 119-128. doi: 10.1145 /2767386.2767422.

BAGWARI A, TOMAR G S, and VERMA S. Cooperative spectrum sensing based on two-stage detectors with multiple energy detectors and adaptive double threshold in cognitive radio networks[J]. Canadian Journal of Electrical and Computer Engineering, 2013, 36(4): 172-180. doi: 10.1109/ CJECE.2014.2303519.

MALEKI S, PANDHARIPANDE A, and LEUS G. Two-stage spectrum sensing for cognitive radios[C]. Proceedings of IEEE ICASSP. New York, USA, 2010: 2946-2949. doi: 10.1109/ICASSP.2010.5496149.

CHEN C X, FU H, NIU Z D, et al. Cooperative spectrum sensing algorithm based on double-threshlod energy detection[J]. Systems Engineering and Electronics, 2013, 35(8): 1742-1746. doi: 10.3969/j.issn.1001-506X.2013.08.26.

HORGAN D and MURPHY C. Voting rule optimisation for double threshold energy detector-based cognitive radio networks[C]. Proceedings of IEEE 4th International Conference on Signal Processing and Communicating Systems, Australia, 2010: 1-8. doi: 10.1109/ICSPCS.2010.- 5709679.

FANG L, WANG J K, and HAN Y H. An adaptive double thresholds scheme for spectrum sensing in cognitive radio networks[J]. Defence Science Journal, 2013, 63(1): 47-52. doi: 10.1109/ICSPCC.2013.6664104.

HERATH S, RAJATHEVA N, and TELLAMBURA C. Energy detection of unknown signals in fading and diversity reception[J]. IEEE Transactions on Communications, 2011, 59(9): 2443-2453. doi: 10.1109/TCOMM.2011.071111.090349.

SUN H J and NALLANATHAN A. Wideband spectrum sensing for cognitive radio networks: a survey[J]. IEEE Wireless Communications, 2013, 20(2): 74-81. doi: 10.1109/ MWC.2013.6507397.

CABRIC D, TKACHENKO A, and BRODERSEN R W. Experimental study of spectrum sensing based on energy detection and network cooperation[C]. Proceedings of the First International Workshop on Technology and Policy for Accessing Spectrum. New York, USA, 2006: 12. doi: 10.1145/1234388.1234400.

LI Z X, WANG H, and KONG J. A two-step spectrum sensing scheme for cognitive radio network[C]. Proceeding of the International Conference on Information Science and Technology. Nanjing, China, 2011: 694-698. doi: 10.1109/ ICIST.2011.5765341.

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. doi: 10.1109/TVT.2008.2005267.

CHEN W B, YANG C K, and HUANG Y H. Energy-saving cooperative spectrum sensing processor for cognitive radio systems[J]. IEEE Transactions on Circuits and Systems, 2011, 58(4): 711-723. doi: 10.1109/TCSI.2010.2078691.

ATAPATTU S, TELLAMBURA C, HAI J, et al. Unified analysis of low-SNR energy detection and threshold selection[J]. IEEE Transactions on Circcular Technology, 2015, 64(11): 5006-5019. doi: 10.1109/TVT.2014.2381648.

谢显中, 胡小峰, 马彬. 噪声功率不确定性区间估计和降低SNA WALL恶化的能量检测算法[J]. 电子与信息学报, 2014, 36(2): 364-370. doi: 10.3724/SP.J.1146.2013.00928.

XIE X Z, HU X F, and MA B. Estimation of noise power uncertainty interval and energy detector with lowering SNR WALL detection[J]. Journal of Electronics Information Technology, 2014, 36(2): 364-370. doi: 10.3724/SP.J.1146. 2013.00928.

SHEN B, WANG S, and HUANG Q. Gerschgorin disk theorem based spectrum sensing for wideband cognitive radio[J]. Journals on Communications, 2014, 35(4): 1-10. doi: 10.1109/ICASSP.2014.6855013.

ATAOLLAH E, MARYAM N, SEYED M H A, et al. Sensor selection and optimal energy detection threshold for efficient cooperative spectrum sensing[J]. IEEE Transactions on Vehicular Technology, 2015, 64(4): 1565-1577. doi: 10.1109/ TVT.2014.2331681.

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