基于空间谱的频谱感知算法及性能分析

党小宇; 李阿明; 虞湘宾

doi:10.11999/JEIT150823

基于空间谱的频谱感知算法及性能分析

doi: 10.11999/JEIT150823

基金项目:

国家自然科学基金(61172078, 61201208)，教育部留学回国人员科研启动基金和中央高校基本科研业务费(NS2014038)，南京航空航天大学研究生创新基地开放基金(kfjj20150404)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-07-09
- 修回日期: 2015-12-02
- 刊出日期: 2016-05-19

Spatial Spectrum Based Spectrum Sensing Algorithm and Performance Analysis

Funds:

The National Natural Science Foundation of China (61172078, 61201208), The State Education Ministry Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars and the Fundamental Research Funds for the Central Universities (NS2014038), The Foundation of Graduate Innovation Center in NUAA (kfjj20150404)

摘要

摘要: 现有的基于特征值或谱密度的频谱感知算法，多分别使用近似高斯分布和Tracy-Widom分布来分别分析求解检验统计量在信号是否存在时的分布，未能给出统一的解析表达式。该文提出均匀线阵(ULA)条件下基于空间谱密度比的频谱感知算法，并且基于顺序统计量的最新研究成果，给出检验统计量统一的闭合表达式。该算法基于离散空间谱密度最大最小值的比建立检验统计量。仿真结果表明，对于8阵元的ULA，在采样点数为1000、检测概率为0.9时，所提算法比最大最小特征值(MME)比算法有约1.7 dB的性能优势，同时也有效验证了检验统计量理论分布的准确性。
- 认知无线电 /
- 频谱感知 /
- 均匀线阵 /
- 顺序统计量
Abstract: Spectrum sensing algorithms based on eigenvalue or spectral density usually use the Gaussian approximated distribution and Tracy-Widom distribution to analyze the test statistic with the presence of the primary user or not respectively, but it is hard to find the analysis expression with unified form. In this paper, a spectrum sensing algorithm is proposed based on spatial spectrum density ratio using a Uniform Linear Array (ULA), and a unified expression for the distribution of test statistic is proposed using the latest research results of order statistics. In this algorithm, the test statistic is established using the maximum and minimum values of the discrete spatial spectrum density. Simulation results show that the performance of the proposed algorithm is about 1.7 dB better than the Maximum-Minimum Eigenvalue (MME) ratio algorithm with the detection probability equal to 0.9. At the same time, the results also verify the accuracy of the theoretical distribution of the test statistic.
- Cognitive Radio (CR) /
- Spectrum sensing /
- Uniform Linear Array (ULA) /
- Order statistics

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参考文献(20)

MITOLA J and MAGUIRE G Q. Cognitive radio: making software radios more personal[J]. IEEE Personal Communications, 1999, 6(4): 13-18. doi: 10.1109/98.788210.

HATTAB G and IBNKAHALA M. Multiband spectrum access: great promises for future cognitive radio networks[J]. Proceedings of the IEEE, 2014, 102(3): 282-306. doi: 10.1109/ JPROC.2014.2303977.

HONG Xuemin, WANG Jing, WANG Chengxiang, et al. Cognitive radio in 5G: a perspective on energy-spectral efficiency trade-off[J]. IEEE Communications Magazine, 2014, 52(7): 46-53. doi: 10.1109/MCOM.2014.6852082.

LPEZ-BENTEZ M. Sensing-based spectrum awareness in cognitive radio: challenges and open research problems[C]. International Symposium on Communication Systems, Networks Digital Signal Processing, Manchester, 2014: 459-464. doi: 10.1109/CSNDSP.2014.6923873.

ZHANG Rui, TENG Joonlim, LIANG Yingchang, et al. Multi-antenna based spectrum sensing for cognitive radios: a GLRT approach[J]. IEEE Transactions on Communications, 2010, 58(1): 84-88. doi: 10.1109/TCOMM.2010.01.080158.

TANDRA R and SAHAI A. SNR walls for signal detection[J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1): 4-17. doi: 10.1109/JSTSP.2007.914879.

赵晓晖, 李晓燕. 认知无线电中基于阵列天线和协方差矩阵的频谱感知算法[J]. 电子与信息学报, 2014, 36(7): 1693-1698. doi: 10.3724/SP.J.1146.2013.01057.

ZHAO Xiaohui and LI Xiaoyan. Spectrum sensing algorithm in cognitive radio based on array antenna and covariance matrix[J]. Journal of Electronics Information Technology, 2014, 36(7): 1693-1698. doi: 10.3724/SP.J.1146.2013.01057.

CHOPRA R, GHOSH D, and MEHRA D K. Spectrum sensing for cognitive radios based on space-time FRESH filtering[J]. IEEE Transactions on Wireless Communications, 2014, 13(7): 3903-3913. doi: 10.1109/TWC.2014.2314125.

ZENG Yonghong and LIANG Yingchang. Eigenvalue-based spectrum sensing algorithms for cognitive radio[J]. IEEE Transactions on Communications, 2009, 57(6): 1784-1793. doi: 10.1109/TCOMM.2009.06.070402.

LIU Chang and JIN Minglu. Maximum-minimum spatial spectrum detection for cognitive radio using parasitic antenna arrays[C]. IEEE/CIC International Conference on Communications, Shanghai, 2014: 365-369. doi: 10.1109/ ICCChina.2014.7008303.

LU W and TIRKKONEN O. Spectrum sensing with Gaussian approximated eigenvalue ratio based detection[C]. International Symposium on Wireless Communication Systems, York, 2010: 961-965. doi: 10.1109/ISWCS.2010. 5624271.

PILLAI S U and BURRUS C S. Array Signal Processing[M]. New York: Springer, 1989: 8-45.

KAY S M. Fundamentals of Statistical Signal Processing, Volume II: Detection Theory[M]. New Jersey: Prentice Hall, 1998: 33-41.

CHAN R H and NG M K. Conjugate gradient methods for Toeplitz systems[J]. SIAM Review, 1996, 38(3): 427-482.

LI Tong and TANG Yinhui. Frequency estimation based on modulation FFT and MUSIC algorithm[C]. Pervasive Computing Signal Processing and Applications, Harbin, 2010: 525-528. doi: 10.1109/PCSPA.2010.132.

MADANAYAKE A, WIJENAYAKE C, BELOSTOTSKI L, et al. An overview of multi-dimensional RF signal processing for array receivers[C]. Moratuwa Engineering Research Conference, Moratuwa, 2015: 255-259. doi: 10.1109/ MERCon.2015.7112355.

ARNOLD B C, BALAKRISHNAN N, and NAGARAJA H N. A First Course in Order Statistics[M]. Philadelphia: Society for Industrial and Applied Mathematics, 2008: 16-21.

GNGR M, BULUT Y, andALIK S. Distributions of order statistics[J]. Applied Mathematical Sciences, 2009, 3(16): 795-802.

GNGR M. On joint distributions of order statistics from innid variables[J]. Bulletin of the Malaysian Mathematical Sciences Society, 2012, 35(1): 215-225.

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