سنجش طیف فرکانسی توسط الگوریتم چند مرحله ای وفقی با روش غیر همکارانه بهینه در رادیو شناختگر به همراه پیاده سازی روی سخت افزار

نوع مقاله: مقاله پژوهشی

نویسندگان

1 امام حسین (ع)

2 دانشجوی دکترای دانشکده فاوای دانشگاه امام حسین (ع)

چکیده

حسگرهای طیفی، به‌عنوان اصلی­ترین بخش یک سامانه رادیو شناختگر، ابزاری هستند که با تشخیص حفره­های طیفی، موجب استفاده بهینه از پهنای باند فرکانسی محیط شده و از تداخل بین کاربران مجاز ممانعت می­کنند. عملکرد این حسگرها به دلایلی مانند اثرات نویز محیطی، سطح پایین سیگنال، محوشدگی، چند مسیرگی و حساسیت گیرنده، همواره با مشکل مواجه می­شود. در این مقاله، ابتدا با استفاده از روش چند آنتنه در گیرنده با اخذ سیگنال­های محیطی و استفاده از روش آشکارساز انرژی، آستانه آشکارسازی به‌صورت وفقی با روش CFAR تعیین ‌شده و سنجش اولیه طیف محیطی انجام می­گیرد. محدوده­ای از طیف که سیگنال در آن تشخیص داده نشده جهت تصمیم­گیری به مرحله نهایی وارد می­گردد. در این مرحله، سنجش نهایی طیف با یافتن مقادیر ویژه ‌ماتریس کوواریانس سیگنال توسط روش MME  به‌صورت کاملاً کور و غیر همکارانه صورت می‌گیرد که باعث افزایش قابلیت اطمینان در تصمیم­گیری و افزایش احتمال آشکارسازی صحیح حفره­های طیفی و جلوگیری از تداخل کاربران مجاز می­شود. نتایج شبیه­سازی­ها حاکی از احتمال آشکارسازی 75درصدی درSNR محیطی         dB25- می‌باشد که در مقایسه با مراجع بهبود dB15را داشته است. همچنین نتایج شبیه­سازی این مقاله بعد از پیاده­سازی روی برد سخت‌افزاری با نتایج حاصل از آزمون عملی در محیط واقعی مقایسه شده است.

کلیدواژه‌ها


[1]     J. Mitola and G. Q. J. I. p. c. Maguire, “Cognitive Radio: Making Software Radios More Personal,”IEEE Personal Communications,vol. 6, no. 4, pp. 13-18, 1999.##
[2]     J. Chen, A. Gibson, and J. Zafar, “Cyclostationary Spectrum Detection in Cognitive Radios,” 2008.##
[3]     Y. Mingchuan, L. Yuan, L. Xiaofeng, and T. J. C. C. Wenyan, “Cyclostationary feature Detection Based Spectrum Sensing Algorithm under Complicated Electromagnetic Environment in Cognitive Radio Networks,” China Communications,vol. 12, no. 9, pp. 35-44, 2015.##
[4]     Z. Wen, T. Luo, W. Xiang, S. Majhi, and Y. Ma, “Autoregressive Spectrum Hole Prediction Model for Cognitive Radio Systems,” In ICC Workshops-2008 IEEE Int. Conf. on Communications Workshops, IEEE, pp. 154-157, 2008.##
[5]     S. Kandeepan, S. Reisenfeld, T. C. Aysal, D. Lowe, and R. Piesiewicz, “Bayesian Tracking in cooperative Localization for Cognitive Radio Networks,” In VTC Spring 2009-IEEE 69th Vehicular Technology Conf., IEEE, pp. 1-5, 2009.##
[6]     M. A. Shorche, H. Khaleghi Bizaki , “Estimation Of Frequency Spectrum In Cognitive Radios Using Particle Filtering Based On Open Sampling Method,”Electronic Industries Quarterly, Volume: 5, Issue: 2, 2014 (In Persian).##
[7]     S. Imani, A. B. Dehkordi, and M. Kamarei, “Using Weighted Multilevel Wavelet Decomposition for Wideband Spectrum Sensing in Cognitive Radios,” In Electrical Engineering (ICEE), 2011 19th Iranian Conf. on, IEEE, pp. 1-5, 2011.##
[8]     D. Joshi, N. Sharma, and J. Singh, “Spectrum Sensing for Cognitive Radio Using Hybrid Matched Filter Single Cycle Cyclostationary Feature Detector,” Int. J. Inf. Eng. Electron. Bus, vol. 7, 2015.##
[9]     F. Salahdine, H. El Ghazi, N. Kaabouch, and W. F. Fihri, “Matched Filter Detection with Dynamic Threshold for Cognitive Radio Networks,” In Wireless Networks and Mobile Communications (WINCOM), 2015 Int. Conf. on, IEEE, pp. 1-6, 2015.##
[10]  F. A.-R. Awin, Esam Tepe, Kemal, “Blind Spectrum Sensing Approaches for Interweaved Cognitive Radio System: A Tutorial and Short Course,” IEEE Communications Surveys&Tutorials,vol. 10, no. 2, pp. 650-674, 2018.##
[11]  Y. Zeng and Y.-C. Liang, “Eigenvalue-based spectrum Sensing Algorithms for Cognitive Radio,” IEEE Transactions on Communications,vol. 57, no. 6, pp. 1784-1793, 2009.##
[12]  M. A. Abdulsattar and Z. A. J. I. J. o. C. N. Hussein, “Energy Detection Technique for Spectrum Sensing In Cognitive Radio: A Survey,” Int. J. of Computer Networks,vol. 4, no. 5, p. 223, 2012.##
[13]  H. M. Farag and E. M. Mohamed, “Soft decision Cooperative Spectrum Sensing with Noise Uncertainty Reduction,” Pervasive and Mobile Computing, vol. 35, pp. 146-164, 2017/02 2017, doi: 10.1016/j.pmcj.2016.04.001.##
[14]  A. Goldsmith, S. A. Jafar, I. Maric, and S. J. p. I. Srinivasa, “Breaking Spectrum Gridlock with Cognitive Radios: An Information Theoretic Perspective,” Proc. IEEE, vol. 97, no. 5, pp. 894-914, 2009.##
[15]  M. Al-Husseini, K. Y. Kabalan, A. El-Hajj, and C. G. Christodoulou, “Reconfigurable Microstrip Antennas for Cognitive Radio,” In Advancement in Microstrip Antennas with Recent Applications: InTech., 2013.##
[16]  J. Nikonowicz, P. Kubczak, and Ł. Matuszewski, “Hybrid Detection Based on Energy and Entropy Analysis as a Novel Approach for Spectrum Sensing,” In Signals and Electronic Systems (ICSES), 2016 Int. Conf. on, IEEE, pp. 206-211, 2016.##
[17]  T. Yucek, H. J. I. c. s. Arslan, and tutorials, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications,” vol. 11, no. 1, pp. 116-130, 2009.##
[18]  Y. Zeng and Y.-C. Liang, “Spectrum Sensing Algorithms for Cognitive Radio Based on Statistical Covariances,” IEEE Transactions on Vehicular Technology,vol. 58, no. 4, pp. 1804 - 1815, 2008.##
[19]  A. Bishnu and V. J. I. T. o. V. T. Bhatia, “LogDet Covariance Based Spectrum Sensing under Colored Noise,” IEEE Transactions on Vehicular Technology, vol. 67, no. 7, pp. 6716 - 6720, 2018.##
[20]  C. Liu, H. Li, J. Wang, and M. J. I. T. o. W. C. Jin, “Optimal Eigenvalue Weighting Detection for Multi-Antenna Cognitive Radio Networks,” IEEE Transactions on Wireless Communications, vol. 16, no. 4, pp. 2083-2096, 2017.##
[21]  C. Tepedelenlioglu and R. Challagulla, “Low Complexity Multipath Diversity through Fractional Sampling in OFDM,” In Conf: Record of the Thirty-Sixth Asilomar Conf. on Signals, Systems and Computers, vol. 2: IEEE, pp. 1813-1817, 2002.##
[22]  H. Nishimura, M. Inamori, and Y. Sanada, “Sampling Rate Selection for Fractional Sampling in OFDM,” IEICE Transactions on Communications,vol. 91, no. 9, pp. 2876-2882, 2008.##
[23]  C. G. Tsinos and K. s. Berberidis, “Decentralized Adaptive Eigenvalue-Based Spectrum Sensing for Multiantenna Cognitive Radio Systems,” IEEE Transactions on Wireless Communications, vol. 14, no. 3, pp. 1703-1715, 2015.##
[24]  L. S. Cardoso, M. Debbah, P. Bianchi, and J. Najim, “Cooperative Spectrum Sensing Using Random Matrix Theory,” In 2008 3rd Int. Symposium on Wireless Pervasive Computing, 2008: IEEE, pp. 334-338.##
[25]  F. Penna, R. Garello, and M. A. Spirito, “Cooperative Spectrum Sensing Based on the Limiting Eigenvalue Ratio Distribution in Wishart Matrices,” arXiv preprint arXiv:0902.1947, 2009.##
[26]  P. Wang, J. Fang, N. Han, and H. Li, “Multiantenna-Assisted Spectrum Sensing for Cognitive Radio,” IEEE Transactions on Vehicular Technology, vol. 59, no. 4, pp. 1791-1800, 2009.##
[27]  P. Bianchi, M. Debbah, M. Maïda, and J. Najim, “Performance of Statistical Tests for Single-Source Detection Using Random Matrix Theory,” IEEE Transactions on Information Theory,vol. 57, no. 4, pp. 2400-2419, 2011.##
[28]  B. Nadler, F. Penna, and R. Garello, “Performance of Eigenvalue-Based Signal Detectors with Known and Unknown Noise Level,” In 2011 IEEE International Conference on Communications (ICC), IEEE, pp. 1-5, 2011.##
[29]  J. W. Mauchly, “Significance Test for Sphericity of a Normal N-Variate Distribution,” The Annals of Mathematical Statistics, vol. 11, no. 2, pp. 204-209, 1940.##
[30]  R. Zhang, T. J. Lim, Y.-C. Liang, and Y. Zeng, “Multi-Antenna Based Spectrum Sensing for Cognitive Radios: A GLRT Approach,” IEEE Transactions on Communications,vol. 58, no. 1, pp. 84-88, 2010.##
[31]  L. Wei and O. Tirkkonen, “Spectrum Sensing in the Presence of Multiple Primary Users,” IEEE Transactions on Communications, vol. 60, no. 5, pp. 1268-1277, 2012.##
 
[32]  S. John, “Some Optimal Multivariate Tests,” Biometrika, vol. 58, no. 1, pp. 123-127, 1971.##
[33]  A. Edelman, “On the Distribution of a Scaled Condition Number,” Mathematics of computation,vol. 58, no. 197, pp. 185-190, 1992.##
[34]  C. Zhong, M. R. McKay, T. Ratnarajah, and K.-K. Wong, “Distribution of the Demmel Condition Number of Wishart Matrices,” IEEE Transactions on Communications, vol. 59, no. 5, pp. 1309-1320, 2011.##
[35]  S. Qin, W. Zhang, H. Xiong, and D. Chen, “Cooperative Spectrum Sensing Using Finite Demmel Condition Numbers,” Wireless Personal Communications,vol. 80, no. 1, pp. 335-346, 2015.##
[36]  X. Ren and C. J. A.-I. J. o. E. C. Chen, “Spectrum Sensing Algorithm Based on Sample Variance in Multi-Antenna Cognitive Radio Systems,” AEU-Int. J. of Electronics Communications,vol. 70, no. 12, pp. 1601-1609, 2016.##