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سنجش طیف فرکانسی توسط الگوریتم چند مرحله ای وفقی با روش غیر همکارانه بهینه در رادیو شناختگر به همراه پیاده سازی روی سخت افزار | ||
پدافند الکترونیکی و سایبری | ||
مقاله 4، دوره 8، شماره 3 - شماره پیاپی 31، آبان 1399، صفحه 39-51 اصل مقاله (1.53 M) | ||
نوع مقاله: مقاله پژوهشی | ||
نویسندگان | ||
حمیدرضا خدادادی* 1؛ محمدعلی عطایی2 | ||
1امام حسین (ع) | ||
2دانشجوی دکترای دانشکده فاوای دانشگاه امام حسین (ع) | ||
تاریخ دریافت: 16 مرداد 1398، تاریخ بازنگری: 04 آبان 1398، تاریخ پذیرش: 12 بهمن 1398 | ||
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 | ||
رادیو شناختگر؛ حسگرهای طیفی؛ آشکارساز انرژی؛ حفرههای طیفی؛ مقادیر ویژه؛ ماتریس کوواریانس | ||
عنوان مقاله [English] | ||
Frequency Spectrum Sensing by Multi-Stage Adaptive Optimization Algorithm with the Efficient Non-Cooperative Technique in Cognitive radios with hardware implementation | ||
نویسندگان [English] | ||
H. R. Khodadadi1؛ M. A. Ataee2 | ||
1Imam hossein | ||
2PHD student of Imam Hossein University, communication and electronic collage | ||
چکیده [English] | ||
Cognitive sensors, as the main part of cognitive radio systems, are the instruments which determine the spectral cavity, and thus provide optimal use of the bandwidth and prevent interference between permissible users. For reasons such as environmental noise effects, low levels of the signal, fading and multi-path phenomena, and receiver sensitivity, the functionality of these sensors encounters many problems. In this paper, by first applying the multi-antenna method in the receiver to obtain environmental signals and then applying the energy detector method, the detection threshold is adaptively determined with the CFAR method and the initial measurements of the environmental spectrum are achieved. The range of the spectrum where the signal is not detected is entered into the final step for decision making. In this stage, the final measurement of the spectrum is performed blindly and non-cooperatively by finding specific values of the signal covariance matrix by the MME method, to increase the reliability in decision making and also to increase the likelihood of correct detection of the spectral cavity, in addition to preventing interference between authorized users. Simulation results show the probability of detection in the -25dB environmental SNR to be 75 %, which has improved by 15 dB compared to the benchmarks. After hardware implementation, the simulation results are compared with the results obtained by experimental tests in the real environment. | ||
کلیدواژهها [English] | ||
Cognitive Radio, Spectral Sensors, Energy Detector, Spectral Holes, Specific Values, Covariance Matrix | ||
مراجع | ||
[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.##
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