اخبار و اعلانات
آمار
| تعداد نشریات | 34 |
| تعداد شماره‌ها | 1,306 |
| تعداد مقالات | 9,427 |
| تعداد مشاهده مقاله | 9,188,323 |
| تعداد دریافت فایل اصل مقاله | 5,620,748 |
کاهش تداخل عمدی در سیستم‌های مخابراتی رادیوشناختگر با استفاده از تبدیل موجک | ||
| پدافند الکترونیکی و سایبری | ||
| مقاله 5، دوره 9، شماره 4 - شماره پیاپی 36، اسفند 1400، صفحه 55-66 اصل مقاله (1.09 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| علی ابراهیمی سوخت آبندانی1؛ میثم بیات* 2؛ رضا هوشمند2 | ||
| 1کارشناسی ارشد، دانشگاه علوم و فنون هوایی شهید ستاری، تهران، ایران | ||
| 2استادیار، دانشگاه علوم و فنون هوایی شهید ستاری، تهران، ایران | ||
| تاریخ دریافت: 24 بهمن 1399، تاریخ بازنگری: 17 خرداد 1400، تاریخ پذیرش: 13 تیر 1400 | ||
| چکیده | ||
| یکی از عوامل مخرب در سامانههای مخابراتی و راداری، تداخل عمدی است، تداخل عمدی با استفاده از جمر به منظور تخریب سامانههای ارتباطی و راداری دشمن ایجاد میشود. اگر تداخل عمدی به خوبی کاهش داده نشود، کارایی سامانه مخابراتی به طور کامل مختل میگردد. جمرها به صورت هدفمند، ایجاد تداخل مینمایند و عملکرد بهینه سامانه را تحت تأثیر قرار میدهند. الگوریتم تطبیقی NLMS یکی از الگوریتم‌های مؤثر در حذف تداخل عمدی است. در این مقاله الگوریتمی جدید برای حذف تداخل عمدی در سامانههای رادیوشناختگر با استفاده از تبدیل موجک ارائه شده است. در شبیهسازیهای انجام شده، از یک سیستم رادیوشناختگر 25 کاربره (به عنوان شبکه قربانی) در مجاورت شبکهای از کاربران اولیه با عملکرد کانالی مارکوف، استفاده شده است. با در نظر گرفتن یازده سناریو مختلف به بررسی عملکرد الگوریتم پیشنهادی پرداخته شده است. برای بررسی عملکرد الگوریتم پیشنهادی از معیار ارسال موفق اطلاعات بر حسب نسبت سیگنال به جمر در هر یک از سناریوها، پرداخته شده است. با توجه به نتایج شبیهسازی، الگوریتم پیشنهادی، در مقایسه با الگوریتم تطبیقی NLMS، بهبود قابل ملاحظهای را از خود نشان میدهد. بر اساس نتایج به دست آمده، الگوریتم پیشنهادی در مقایسه با الگوریتم تطبیقی NLMS، 13درصد بهبود در ارسال موفق را در dB 5= SJR از خود نشان میدهد. | ||
| کلیدواژه‌ها | ||
| تبدیل موجک؛ تداخل عمدی؛ رادیو شناختگر؛ کاربران ثانویه؛ کاهش تداخل؛ مارکوف | ||
| عنوان مقاله [English] | ||
| Interference Mitigation in Cognitive Radio Communication Systems Based on the Wavelet Transform | ||
| نویسندگان [English] | ||
| Ali Ebrahimi Sookht Abanani1؛ meysam Bayat2؛ Reza Hooshmand2 | ||
| 1M.Sc., Shahid Sattari University of Aeronautical Sciences and Technology, Tehran, Iran | ||
| 2Assistant Professor, Shahid Sattari University of Aeronautical Sciences and Technology, Tehran, Iran | ||
| چکیده [English] | ||
| One of the destructive factors in communication and radar systems is intentional interference which is created by using jammers to disrupt the enemy's systems. If the intentional interference is not reduced well, the efficiency of the communication system would be completely disrupted. Jammers purposefully interfere and affect the optimal performance of the system. The NLMS adaptive algorithm is one of effective algorithms in eliminating intentional interference. In this paper, a new algorithm for eliminating intentional interference in cognitive radio systems using wavelet transform is presented. In the simulations, a 25-user cognitive radio system is used as a victim network in the vicinity of a network of primary users with Markov channel functionality. Considering eleven different scenarios, the performance of the proposed algorithm is investigated. To evaluate the performance of the proposed algorithm, the criterion of successful transmission of information in terms of signal to jammer ratio in each of the scenarios is discussed. According to the simulation results, the proposed algorithm, compared to the adaptive algorithm (NLMS), shows a significant improvement. The results, show 13% improvement for the proposed algorithm in successful transmission at SJR=5dB compared to the NLMS adaptive algorithm . | ||
| کلیدواژه‌ها [English] | ||
| Cognitive Radio Network, Intentional Interference, Interference Mitigation, Markov, Secondary Users, Wavelet Transform | ||
| مراجع | ||
|
[1] Y. Liang, K. Chen, G. Li, and P. Mahonen, “Cognitive radio networking and communications: An overview,” Vehicular Technology, IEEE Transactions on, vol. 60, no. 7, pp. 3386–3407, 2011. [2] Q. Zhao and B. Sadler, “A survey of dynamic spectrum access,” Signal Processing Magazine, IEEE, vol. 24, no. 3, pp. 79–89, 2007. [3] I. Akyildiz, W. Lee, M. Vuran, and S. Mohanty, “A survey on spectrum management in cognitive radio networks,” Communications Magazine, IEEE, vol. 46, no. 4, pp. 40–48, 2008. [4] S. Haykin, D. Thomson, and J. Reed, “Spectrum sensing for cognitive radio,” Proceedings of the IEEE, vol. 97, no. 5, pp. 849–877, 2009. [5] J. Guo, J. Li and Q. Lv, "Survey on Radar ECCM Methods and Trends in its Developments," 2006 CIE International Conference on Radar, Shanghai, 2006, pp. 1-4. doi: 10.1109/ICR.2006.343210. [6] M. Lv and L. Wang, "Research on the Efficiency of the Chaff Jamming Corridor," 2017 4th International Conference on Information Science and Control Engineering (ICISCE), Changsha, 2017, pp. 1673-1676. doi: 10.1109/ICISCE.2017.349. [7] R. D. Pietro and G. Oligeri, "Jamming mitigation in cognitive radio networks," in IEEE Network, vol. 27, no. 3, pp. 10-15, May-June 2013, doi: 10.1109/MNET.2013.6523802. [8] F. Slimeni, B. Scheers, Z. Chtourou and V. Le Nir, "Jamming mitigation in cognitive radio networks using a modified Q-learning algorithm," 2015 International Conference on Military Communications and Information Systems (ICMCIS), Cracow, Poland, 2015, pp. 1-7, doi: 10.1109/ICMCIS.2015.7158697. [9] Dabcevic, K., Betancourt, A., Marcenaro, L. et al. Intelligent cognitive radio jamming - a [10] H. Pirayesh and H. Zeng, “Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey”, arXiv:2101.00292v1, Jan., 2021. [11] Grover, Kanika & Lim, Alvin & Yang, Qing. (2014). Jamming and anti-jamming techniques in wireless networks: A survey. International Journal of Ad [12] Bayat, M.; Madani, M.H. “Design and Simulation of a linear adaptive system to remove the CWI in Loran navigation system receivers “; J. Advanced Defence Sci. & Tech. 2018, 5, 50-62. [13] Bayat, M., “Analysis and simulation of noise reduction using wavelet transform for detecting defects in materials,” Scientific Journal of Information and communication Technology and Law Enforcement (ICT) on Vol.1. No.3, 2020. [14] L. Run -hua and K. Duan, “Barrage Noise Jamming Suppression methods for Airborne phased array radar” IET International Conf. on Radar, Guillin, China, Apr. 2009, pp.333˗339. [15] Bayat, M.; Madani, M.H. “Analysis and Simulation of a Hybrid Filter to Cancel Cross Rate Interference in Loran System “; J. Advanced Defence Sci. & Tech. 2017, 4, 51-60. [16] Teja MVSR, K. Meghashyam and A. Verma, "Comprehensive analysis of LMS and NLMS algorithms using adaptive equalizers," 2014 International Conference on Communication and Signal Processing, Melmaruvathur, 2014, pp. 1101-1104. doi: 10.1109/ICCSP.2014.6950018. [17] Quan, Houde & Zhao, Huan & Cui, Peizhang. (2014). Anti-jamming Frequency Hopping System Using Multiple Hopping Patterns. Wireless Personal Communications. 81. 1159-1176. 10.1007/s11277-014-2177-1. [18] Teimouri, M. and Kakaei Motlagh, H. R. and Garshasbi, J., " Blind Identification of Communications Networks in Service Layer," Scientific Journal of Passive Defense Quarterly, vol. 10, no. 4, pp. 91-101, Imam Hossein University, 2019. [19] Bayat, M.; Madani, M.H. “A New Algorithm For Synchronous Continuous Wave Interference Cancellation in Loran Navigation System,” J. of Electronical & Cyber Defence, vol. 4, no. 2, pp. 23-38, 2016. [20] C. Y. Guo and J. P. Li, "Development and future of wavelet analysis," 2013 10th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, 2013, pp. 335-338. doi: 10.1109/ICCWAMTIP.2013.6716661 [21] Yang Lin, Wang Chong-yang and Z. Wu, "Research on the selection of wavelet bases for wavelet-based signal trend elimination," 2012 International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP), Chengdu, 2012, pp. 20-24. doi: 10.1109/ICWAMTIP.2012.6413430 [22] S. N. Keshavarz, S. Hajizadeh, M. Hamidi and M. G. Omali, "A Novel UWB Pulse Waveform Design Method," 2010 Fourth International Conference on Next Generation Mobile Applications, Services and Technologies, Amman, 2010, pp. 168-173. [23] I. Mitola, J. and J. Maguire, G. Q., "Cognitive radio: making software radios more personal," IEEE PersonaL Communications, vol. 6, no. 4, pp. 13-18, Aug. 1999. [24] S. Haykin, "Cognitive radio: brain-empowered wireless communications," IEEE JournaL on SeLected Areas in Communications, vol. 23, no. 2,pp. 201-220,Feb. 2005. [25] G. Cheng, W. Liu, Y. Li, and W. Cheng, "Joint on-demand routing and spectrum assignment in cognitive radio networks," in Proc. of IEEE InternationaL Con! On Communications, Jun. 2007, pp. 6499-6503. [26] Li Tong, Tang Yinhui and Lv Jun, "Parameter estimation of FH signals based on STFT and music algorithm," 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, 2010, pp. V5-232-V5-236. doi: 10.1109/ICCASM.2010.5619186 [27] Wang, D., Safavi, A.A. and Romagnoli, J.A. “Wavelet-based adaptive robust M-estimator for non-linear system identification”., AIChE Journal, Vol. 46, No. 8,pp. 1607-1615 , August (2000). [28] Won Lee, J.,” Noise Reduction in Wavelet Domain”, EE368, Digital image processing class project, (2000). [29] Ebrahimi Sookht Abandani, A. and Bayat, M. " Interference Mitigation in cognitive Radio Communication Systems Based on Wavelet Transform", Msc. These, Sattari University, Octobre 2020 (in Persian). [30] L. Ding, T. Melodia, S. Batalama, 1. Matyjas, and M. Medley, "Crosslayer routing and dynamic spectrum allocation in cognitive radio ad-hoc networks;' IEEE Trans. VehicuLar TechnoLogy, vol. 59, no. 4, pp. 1969-1979, May 2010. [31] Q. Zhao, L. Tong, A. Swami, and Y. Chen, "Decentralized cognitive mac for opportunistic spectrum access in ad hoc networks: A pomdp framework," IEEE Journal on SeLected Areas in Communications, vol. 25, no. 3, pp. 589-600,Apr. 2007. [32] S. N. Keshavarz, M. A. Kakhki, M. G. Omali et al., “A novel UWB pulse design method using particle swarm optimization algorithm,” Scientific Research and Essays, vol. 5, no. 20, pp. 3049-3058, Oct 18, 2010. [33] Z. Hao, C. Liu and Q. Zhou, "An Improved NLMS Algorithm for Interference Cancellation," 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, 2019, pp. 327-331, doi: 10.1109/IMCEC46724.2019.8984040. [34] Dai, Jing & Han, Zhonghua & Zhang, Feng. (2012). Research on the Interference Cancellation Based on Adaptive Algorithms. International Journal of Intelligent Engineering and Systems. 5. 18-25. 10.22266/ijies2012.1231.03. | ||
|
آمار تعداد مشاهده مقاله: 499 تعداد دریافت فایل اصل مقاله: 385 |
||