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| تعداد شماره‌ها | 1,240 |
| تعداد مقالات | 8,994 |
| تعداد مشاهده مقاله | 7,845,124 |
| تعداد دریافت فایل اصل مقاله | 4,706,717 |
سنجش طیف و تخصیص همزمان منابع با استفاده از دسترسی احتمالاتی به طیف در شبکه های رادیوشناختی چندحاملی | ||
| پدافند الکترونیکی و سایبری | ||
| مقاله 10، دوره 6، شماره 3 - شماره پیاپی 23، آذر 1397، صفحه 117-130 اصل مقاله (1.79 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| محمد کریمی؛ سیدمحمدسجاد صدوق* | ||
| شهید بهشتی | ||
| تاریخ دریافت: 22 دی 1396، تاریخ بازنگری: 19 اردیبهشت 1397، تاریخ پذیرش: 06 خرداد 1397 | ||
| چکیده | ||
| روش سنجش طیف و تخصیص منابع همزمان در شبکه‌های رادیوشناختی به منظور بهینه‌سازی همزمان مؤلفه‌های سنجش و دسترسی به طیف و تخصیص منابع رادیویی، نرخ ارسال بالاتری را برای کاربران شبکه رادیوشناختی فراهم می‌نماید. در این مقاله، سنجش طیف و تخصیص همزمان توان در یک شبکه رادیوشناختی چندحاملی بررسی می‌شود. بدین منظور، ابتدا با تعریف تابع احتمال دسترسی به طیف، روابط احتمال آشکارسازی، احتمال هشدار اشتباه، نرخ قابل دسترس و تداخل اعمالشده به کاربر اولیه بهدست آمده و سپس، مسئله سنجش طیف و تخصیص توان همزمان با تعریف یک مسئله بهینه‌سازی با هدف بیشینه‌سازی نرخ ارسال در شبکه رادیوشناختی تحت قید تداخل اعمالشده به شبکه کاربر اولیه و نیز محدودیت بودجه توان شبکه رادیوشناختی مدل‌سازی می‌شود. مسئله بهینه‌سازی حاصل یک مسئله غیرمحدب بوده که با ارائه دو راهکار مبتنی بر الگوریتم ژنتیک، جواب بهینه برای آن بهدست می‌آید. این دو راهکار عبارتند از: الف) بهینه‌سازی محدب با استفاده از روش ضرایب لاگرانژ و ب) روش برنامه‌ریزی خطی. در انتها، با ارائه نتایج شبیه‌سازی عددی، عملکرد روش‌های ارائهشده را در مقایسه با روش‌های موجود مورد تحلیل و ارزیابی قرار می‌دهیم. | ||
| کلیدواژه‌ها | ||
| فن‌آوری رادیوشناختی؛ سنجش طیف؛ تخصیص منابع رادیویی؛ تابع احتمال دسترسی به طیف؛ بهینه‌سازی محدب؛ الگوریتم ژنتیک | ||
| عنوان مقاله [English] | ||
| Joint Spectrum Sensing and Power Allocation for Multiband Cognitive Radio Networks Using Probabilistic Spectrum Access | ||
| نویسندگان [English] | ||
| Mohammad Karimi؛ Seyyed Mohammad Sajjad Sadough | ||
| چکیده [English] | ||
| Joint optimization of spectrum sensing and spectrum access parameters of a cognitive radio sensor network (CRSN) leads to a higher sum throughput of secondary users (SUs) while the interference introduced to primary users (PUs) is kept under certain tolerable level. In this work, first, by using the concept of probabilistic spectrum access, joint spectrum sensing and power allocation is performed in a multiband CRSN. The considered optimization problem is formulated with the aim of maximizing the average opportunistic secondary data rate under constraints on the interference introduced to PU and limited power budget of SU. The considered system model leads to a non-convex optimization problem which is converted into a convex problem. Based on using genetic algorithms, optimal solution of this problem is obtained using two different approaches: i) Lagrange multipliers method and ii) Linear programming method. We provide several numerical simulation results to evaluate the performance of our proposed methods in terms of achievable CR data rate, interference introduced to the PU and convergence properties of the proposed algorithms. | ||
| کلیدواژه‌ها [English] | ||
| Cognitive Radio Technology, Spectrum Sensing, Radio Resource Allocation, Probabilistic Spectrum Access Function, Convex Optimization, Genetic Algorithm | ||
| مراجع | ||
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[1] J. Mitola and G. Q. Maguire, “Cognitive radio: making software radios more personal,” IEEE Personal Communications, vol. 6, no. 4, pp. 13– 18, Aug. 1999. [2] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201–220, Feb. 2005. [3] Federal Communications Commission, pp. 03–322, Dec. 2003. [4] N. Janatian, M. Modarres-Hashemi, and S. Sun, “Joint versus separate spectrum sensing and resource allocation in OFDMA-based cognitive radio networks,” IET Communications, vol. 10, no. 7, pp. 839-847, 2016. [5] C. Kabiri, “On the Performance of Underlay Cognitive Radio Networks with Interference Constraints and Relaying,” 2015. [6] A. Sahai, N. Hoven, and R. Tandra, “Some fundamental limits on cognitive radio,” In Proc. 42nd Allerton Conf. Communication, Control, Computing, Monticello, IL, pp. 131–136, Oct. 2004. [7] S. Srinu and S. L. Sabat, “Optimal multinode sensing in a malicious cognitive radio network,” IEEE Systems Journal, vol. 9, no. 3, pp. 855–864, Sept. 2015. [8] E. Chatziantoniou, B. Allen, and V. Velisavljevic, “Threshold optimization for energy detection-based spectrum sensing over hyper-rayleigh fading channels,” IEEE Commun. Lett., vol. 19, no. 6, pp. 1077–1080, June 2015. [9] S. H. Hojjati, A. Ebrahimzadeh, M. Najimi, and A. Reihanian, “Sensor Selection for Cooperative Spectrum Sensing in Multiantenna Sensor Networks Based on Convex Optimization and Genetic Algorithm,” IEEE Sensors Journal, vol. 16, no. 10, pp. 3486–3487, 2016. [10] M. Tavana, A. Rahmati, V. Shah-Mansouri, and B. Maham, “Cooperative Sensing With Joint Energy and Correlation Detection in Cognitive Radio Networks,” IEEE Commun. Lett, vol. 21, no. 1, pp. 132-135, Jan. 2017. [11] N. R. Banavathu and M. Z. A. Khan, “Optimal Number of Cognitive Users in -Out-of- Rule,” IEEE Wireless Commun. Lett., vol. 6, no. 5, pp. 606-609, Oct. 2017. [12] M. Karimi and S. M. S. Sadough, “Efficient Transmission Strategy for Cognitive Radio Systems under Primary User Emulation Attack,” IEEE Syst. J., vol. 12, no. 4, pp. 3767–3774, 2018. [13] Z. Quan, S. Cui, A. Sayed, and H. Poor, “Optimal multiband joint detection for spectrum sensing in cognitive radio networks,” IEEE Trans. Signal Process, vol. 57, no. 3, pp. 1128–1140, March 2009. [14] Z. Quan, S. Cui, and A. H. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,” IEEE J. Sel. Topics Signal Process, vol. 2, no. 1, pp. 28–40, February 2008. [15] Y. C. Liang, Y. Zeng, E. C. Y. Peh, and A. T. Hoang, “Sensing throughput tradeoff for cognitive radio networks,” IEEE Trans. on Wireless Commun., vol. 7, no. 4, pp. 1326–1337, April 2008. [16] P. P. Hoseini and N. C. Beaulieu, “An optimal algorithm for wideband spectrum sensing in cognitive radio systems,” In 2010 IEEE International Conference on Communications, pp. 1–6, May 2010. [17] F. Rezaei, F. Torkamani-Azar, and S. M. S. Sadough, “An adaptive multitaper-svd spectrum sensing method for OFDM-based cognitive radio systems,” Wireless Pers. Commun., vol. 79, no. 2, pp. 831–846, 2014. [18] D. Cabric, S. Mishra, and R. Brodersen, “Implementation issues in spectrum sensing for cognitive radios,” in Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on, vol. 1, pp. 772–776, Nov. 2004. [19] S. Mishra, A. Sahai, and R. Brodersen, “Cooperative sensing among cognitive radios,” in Communications, 2006. ICC ’06. IEEE International Conference on, vol. 4, pp. 1658–1663, June 2006. [20] P. Kaligineedi, G. Bansal, and V. Bhargava, “Power loading algorithms for OFDM-based cognitive radio systems with imperfect sensing,” IEEE Trans. Wireless Commun., vol. 11, no. 12, pp. 4225–4230, December 2012. [21] G. Bansal, J. Hossain, and V. Bhargava, “Optimal and suboptimal power allocation schemes for OFDM-based cognitive radio systems,” IEEE Trans. Wireless Commun., vol. 7, no. 11, pp. 4710-4718, November 2008. [22] R. Fan, H. Jiang, Q. Guo, and Z. Zhang, “Joint optimal cooperative sensing and resource allocation in multichannel cognitive radio networks,” IEEE Trans. Veh. Technol., vol. 60, no. 2, pp. 722–729, Feb 2011. [23] Y. Wu, D. H. K. Tsang, L. Qian, and L. Meng, “Sensing based joint rate and power allocations for cognitive radio systems,” IEEE Wireless Commun. Lett, vol. 1, no. 2, pp. 113–116, April 2012. [24] S. Huang, H. Chen, and Y. Zhang, “Optimal power allocation for spectrum sensing and data transmission in cognitive relay networks,” IEEE Wireless Commun. Lett, vol. 1, no. 1, pp. 26–29, February 2012. [25] L. M. Lopez-Ramos, A. G. Marques, and J. Ramos, “Jointly optimal sensing and resource allocation for multiuser interweave cognitive radios,” IEEE Trans. Wireless Commun, vol. 13, no. 11, pp. 5954–5967, Nov 2014. [26] N. Janatian, S. Sun, and M. Modarres-Hashemi, “Joint optimal spectrum sensing and power allocation in cdma-based cognitive radio networks,” IEEE Trans. Veh. Technol., vol. 64, no. 9, pp. 3990–3998, Sept. 2015. [27] F. A. Awin, E. Abdel-Raheem, and M. Ahmadi, “Designing an optimal energy efficient cluster-based spectrum sensing for cognitive radio networks,” IEEE Commun. Lett., vol. 20, no. 9, pp. 1884–1887, Sept. 2016. [28] W. Xu, X. Li, C. H. Lee, M. Pan, and Z. Feng, “Joint Sensing Duration Adaptation, User Matching, and Power Allocation for Cognitive OFDM-NOMA Systems,” IEEE Trans. Wireless Commun., vol. PP, no. 99, p. 1, 2017. [29] M. Karimi and S. M. S. Sadough, “A probabilistic spectrum access approach to joint sensing and power allocation in multiband cognitive radio,” 2017 Iranian Conference on Electrical Engineering (ICEE), Tehran, pp. 1933-1937, 2017. [30] X. Liu, F. Li, and Z. Na, “Optimal Resource Allocation in Simultaneous Cooperative Spectrum Sensing and Energy Harvesting for Multichannel Cognitive Radio,” IEEE Access, vol. 5, pp. 3801-3812, 2017. [31] B. V. Gnedenko and A. N. Kolmogorov, “Limit Distributions for Sums of Independent Random Variables,” Addison-Wesley Publishing Co., Reading, Mass.-London-Don Mills., Ont., 1968. [32] P. Kaligineedi, G. Bansal, and V. K. Bhargava, “Power Loading Algorithms for OFDM-Based Cognitive Radio Systems with Imperfect Sensing,” IEEE Transactions on Wireless Communications, vol. 11, no. 12, pp. 4225-4230, December 2012. [33] S. Boyd and L. Vandenberghe, “Convex Optimization,” Cambridge University Press, 2004. [34] J. H. Holland, “Adaptation in Natural and Artificial System,” Ann Arbor, MI: Univ. of Michigan Press, 1975. [35] D. Luenberger, “Linear and Nonlinear Programming,” Addison-Wesley, 1984. | ||
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آمار تعداد مشاهده مقاله: 4,454 تعداد دریافت فایل اصل مقاله: 2,734 |
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