基于空間譜的頻譜感知算法及性能分析
doi: 10.11999/JEIT150823
國(guó)家自然科學(xué)基金(61172078, 61201208),教育部留學(xué)回國(guó)人員科研啟動(dòng)基金和中央高校基本科研業(yè)務(wù)費(fèi)(NS2014038),南京航空航天大學(xué)研究生創(chuàng)新基地開(kāi)放基金(kfjj20150404)
Spatial Spectrum Based Spectrum Sensing Algorithm and Performance Analysis
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)
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摘要: 現(xiàn)有的基于特征值或譜密度的頻譜感知算法,多分別使用近似高斯分布和Tracy-Widom分布來(lái)分別分析求解檢驗(yàn)統(tǒng)計(jì)量在信號(hào)是否存在時(shí)的分布,未能給出統(tǒng)一的解析表達(dá)式。該文提出均勻線陣(ULA)條件下基于空間譜密度比的頻譜感知算法,并且基于順序統(tǒng)計(jì)量的最新研究成果,給出檢驗(yàn)統(tǒng)計(jì)量統(tǒng)一的閉合表達(dá)式。該算法基于離散空間譜密度最大最小值的比建立檢驗(yàn)統(tǒng)計(jì)量。仿真結(jié)果表明,對(duì)于8陣元的ULA,在采樣點(diǎn)數(shù)為1000、檢測(cè)概率為0.9時(shí),所提算法比最大最小特征值(MME)比算法有約1.7 dB的性能優(yōu)勢(shì),同時(shí)也有效驗(yàn)證了檢驗(yàn)統(tǒng)計(jì)量理論分布的準(zhǔn)確性。
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關(guān)鍵詞:
- 認(rèn)知無(wú)線電 /
- 頻譜感知 /
- 均勻線陣 /
- 順序統(tǒng)計(jì)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.-
Key words:
- Cognitive Radio (CR) /
- Spectrum sensing /
- Uniform Linear Array (ULA) /
- Order statistics
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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. 趙曉暉, 李曉燕. 認(rèn)知無(wú)線電中基于陣列天線和協(xié)方差矩陣的頻譜感知算法[J]. 電子與信息學(xué)報(bào), 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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