پدافند الکترونیکی و سایبری

پدافند الکترونیکی و سایبری

طیف سنجی و ارسال داده به طور همزمان با در نظر گرفتن حذف ناقص سیگنال در شبکه های رادیوشناختگر

نوع مقاله : مقاله پژوهشی

نویسندگان
1 استادیار ،گروه مهندسی برق، دانشکده مهندسی برق و کامپیوتر، دانشگاه بیرجند، بیرجند، ایران
2 مربی، گروه مهندسی برق ، دانشکده مهندسی برق و کامپیوتر، دانشگاه بیرجند، بیرجند ، ایران
3 پژوهشگر،دانشگاه صنعتی مالک اشتر، اصفهان، ایران
چکیده
امروزه، هوشمند‌سازی جنگ الکترونیک با استفاده از فناوری رادیوشناختگر می‌تواند بسیار مفید باشد. از نتایج کاربرد این فناوری، توانایی درک محیط اطراف شامل تشخیص خودکار سیگنال‌های دوست از دشمن، شناسایی تهدیدات جمینگ و عملیات انتقال به فرکانس‌های مختلف به منظور جلوگیری از حمله جمینگ می باشد. در سیستم‌های رادیوشناختگر رایج، کاربر ثانویه قبل از شروع گفتگو، با استفاده از الگوهای طیف سنجی مناسب، حضور یا عدم حضور سیگنال کاربر اولیه را تشخیص می دهد. با این حال، از چالش های این روش، کاهش گذردهی کاربر ثانویه است، زیرا هیچ ارسال داده‌ای در بازه زمانی طیف سنجی انجام نمی شود. در این مقاله، با در نظر گرفتن همکاری بین فرستنده و گیرنده ثانویه، شبکه ای شامل طیف سنجی توأم با ارسال اطلاعات مدنظر قرار گرفته است. ابتدا، سیگنال فرستنده‌ ثانویه توسط گیرنده‌ ثانویه کدگشایی می‌شود. سپس، آن را از مجموع سیگنال دریافتی کم می‌کند و در نهایت تشخیص حضور یا عدم حضور کاربر اولیه، از سیگنال باقیمانده انجام می‌شود. برخلاف روش‏های متداول، در این مقاله، حذف ناقص سیگنال و تأثیر خطاهای کدگشایی بر قابلیت اطمینان طیف سنجی ارزیابی و عبارت‌های تحلیلی برای احتمال هشدار خطا حاصل می شود. نتایج شبیه سازی نشان می دهد که ایده‌ی پیشنهادی باعث بهبود بهره وری طیفی سیستم رادیوشناختگر می شود. مقایسه روش پیشنهادی با روش متداول طیف سنجی و همچنین روش های تقسیم آنتنی، تقسیم توانی و تقسیم آنتنی توأم با برداشت انرژی، نشان می دهد که در زمان های طیف سنجی مختلف، گذردهی بیشتری نسبت به سایر روش های ذکر شده حاصل می گردد؛ لذا حداکثر گذردهی 2b/s/Hz بطور مستقل از زمان طیف سنجی قابل دستیابی است.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Joint Sensing and Transmission Strategy Considering Imperfect Signal Cancellation in Cognitive Radio Communications

نویسندگان English

Javad Zeraatkar Moghaddam 1
Mohammad Soruri 2
Mohammad Sadeghian 3
1 assistant professorDepartment of Electrical and Computer Engineering, University of Birjand, Birjand.Iran
2 Instructor.Department of Electrical and Computer Engineering- University of Birjand- Birjand- Iran
3 researcher.Malek-Ashtar University of Technology- Isfahan- Iran
چکیده English

Recently, the smartening of electronic warfare using cognitive radio technology can be very useful. Among the results of the application of this technology, the ability to understand the surrounding environment includes the automatic detection of friendly signals from the enemy, identification of jamming threats and transmission operations to different frequencies in order to prevent jamming attacks. In common cognitive radio systems, the secondary user detects the presence or absence of the primary user's signal by using suitable spectrometric patterns before starting the conversation. However, one of the challenges of this method is the reduction of the secondary user throughput, because no data is sent in the spectrum sensing time frame. In this paper, the idea of joint spectrum sensing and information transmission is studied, which is achieved with the cooperation of the secondary transmitter and receiver. First, the secondary transmitter signal is decoded by the secondary receiver. Then, it is subtracted from the total received signal and finally the presence or absence of the primary user is detected from the remaining signal. Contrary to common methods, in this work, Imperfect Decoding of the signal and the effect of decoding errors on the spectrum sensing reliability is evaluated. Then, analytical expressions for the probability of false alarm are obtained. The simulation results show that the proposed method improves the spectral efficiency of the cognitive radio system. Comparing the proposed method with the conventional sensing method, as well as with the antenna splitting, power splitting, and joint antenna splitting and energy harvesting methods, shows that at different sensing times, the proposed method achieves higher throughput than the other mentioned methods, and a maximum throughput of 2 b/s/Hz can be achieved which is independent of the sensing time.

کلیدواژه‌ها English

Cognitive radio
Spectrum Sensing
Imperfect Decoding
Throughput
[1]    J. Z. Ahmadabadi and K. Ebrahimi, “Investigating the Impact of Environmental Data Changes on the Lifetime of Wireless Sensor Network,” J ELECTRONICAL & CYBER DEFENCE, vol. 12, no. 3, 2024.  DOR:20.1001.1.23224347 .1403.12.3.2.4, https://ecdj.ihu.ac.ir. (in Persian)
[2]    A. R. Nadinejad and M. Alaei, “A Heuristic Data Diffusion and Gathering Scheme Using Virtual Line for Wireless Sensor Networks with Mobile Sink,” J ELECTRONICAL & CYBER DEFENCE, vol. 9, no.2, 2021. https://ecdj.ihu.ac.ir. (in Persian)
[3]    M. Alaei and F. Yazdanpanah, “A Method for Energy and Delay Aware Routing in Wireless Multimedia Sensor Networks,” J ELECTRONICAL & CYBER DEFENCE, vol.12, no. 47, 2024. https://ecdj.ihu.ac.ir. (in Persian)
[4]    H. Zhou and J. Li, “Sleep Scheduling for Enhancing the Lifetime of Three-Dimensional Heterogeneous Wireless Sensor Networks,” In book: Computer Supported Cooperative Work and Social Computing, pp.365-375, 2023. https://doi.org/ 10.1007/978-981-99-2356-4_29.
[5]    M. Alaei and F. Yazdanpanah, “ZOGLO: A Scheme of Zoning and Data Gathering for Lifetime Optimization in Wireless Sensor Networks,” Journal of Soft Computing and Information Technology (JSCIT), vol. 7, Issue 2, pp. 71-80, 2019. (in Persian)
[6]    A. Seyyedabbasi, G. Dogan, and F. Kiani, “HEEL: A New Clustering Method To Improve Wireless Sensor Network Lifetime,” IET WIRELESS SENSOR SYS, vol. 10, no. 3, pp. 130-136, 2020. https://doi.org/10.1049/ietwss.2019.0153.
[7]    S. Jadhav and S. Jadhav, “KPSO: K-Mean and PSO Based Clustering Algorithm for Wireless Sensor Network,” 6th IEEE International Conference On Computing, Communica-tion, Control and Automation (ICCUBEA), 2020. https://doi.org/10.1109/ICCUBEA54992.2022.10011024.
[8]    A. Joseph, R. Asaletha, V. J. Manoj, and R. Nishanth, “Enhancing the Network Lifetime of a Wireless Sensor Network using Modified K-Means Firefly Optimization,” J PHYS, vol. 2466, no. 012019, 2023. https://doi.org/10.1088/ 1742- 6596/2466/1/012019.
[9]    F. Elfouly, A. Khedr, M. H. Sharif, E. Alreshidi, K. Yadav, H. Kusetogullari, and R. Ramadan, “ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks,” SENSORS,  vol. 22, no. 8950, pp. 1-17, 2022. https://doi.org/10.3390/s22228950.
[10]  M. Alaei and F. Yazdanpanah, “EELCM: An Energy Efficient Load-Based Clustering Method for Wireless Mobile Sensor Networks,” MOBILE NETW APPL, vol. 24, no. 5, pp.1486-1498, 2019. https://doi.org/10.1007/s11036-019-01270 -2.
[11]  B. Sarangi and B. Tripathy, “Outlier Detection Technique for Wireless Sensor Network Using GAN with Autoencoder to Increase the Network Lifetime,” INT J COMP NETW INF SEC, vol. 15, pp. 26-38, 2023. https://doi.org/10.4018/ IJBDCN.286705.
[12]  S. Z. Majidian and M. Shirmohammadi, “Clustering and Routing in Wireless Sensor Networks Using Multi-Objective Cuckoo Search and Game Theory,” J ELECTRONICAL & CYBER DEFENCE, vol. 10, no. 3, 2023. DOR:20.1001 .1.23224347.1401.10.3.2.0, https://ecdj.ihu.ac.ir.  (in Persian)
[13]  D. S. Sultana, D. Bordoloi, C. Singh, D. Srivastava, N. Thiyagarajan, and N. Chinthamu, “A Comparative Approach on Enhancing Lifetime of Wireless Sensor Networks,” 5th IEEE International Conference on Contemporary Computing and Informatics, 2023. https://doi.org/10.47750/pnr.2022.13 .S07.189.
[14]  R. Subha and A. Haldorai, “Improved EPOA Clustering Protocol for Lifetime Longevity in Wireless Sensor Network,” SENSORS,  vol. 3. no. 100199, 2022. https://doi.org/10.1016/j.sintl.2022.100199.
[15]  D. Agrawal, S. Pandey, P. Gupta, and M. K. Goyal, “Optimization of Cluster Heads Through Harmony Search Algorithm in Wireless Sensor Networks,” J INTELL FUZZY SYST, vol. 39, no. 6, pp. 8587-8597, 2020, https://doi.org/ 10.3233/ JIFS-189175.
[16]  L. Jawad and A. Idrees, “Integrated Encoding and Scheduling Protocol for Improving the Lifetime in Wireless Sensor Networks,” INT J COMPUT APPL T, vol. 69, no. 334, pp. 334-343, 2022. https://doi.org/ 10.1504/IJCAT. 2022.10054564.
[17]  V. Narayan  and A. K. Daniel, “Energy Efficient Protocol for Lifetime Prediction of Wireless Sensor Network using Multivariate Polynomial Regression Model,” J SCI IND RES INDIA, vol. 81, no. 12, pp. 1297-1309, 2022. https://doi.org /0.56042/jsir.v81i12.54908.
[18]  K. Debasis, L. Sharma, V. Bohat, and R. Bhadoria, “An Energy-Efficient Clustering Algorithm for Maximizing Lifetime of Wireless Sensor Networks using Machine Learning,” MOBILE NETW APPL, vol.28, no.2, pp. 1-15, 2023. https://doi.org/ 10.1007/s11036-023-02109-7.
[19]  S. Phommasan, Widyawan, and I. W. Mustika “Cluster Selection Technique with Fuzzy Logic-Based Wireless Sensor Network to Increase the Lifetime of Networks,” 5th International Conference on Research of Information Technology and Intelligent Systems (ISRITI), 2022. https://doi.org/ 10.1109/ISRITI56927.2022.10052871.
[20]  A. Taha, H. Abouroumia, S. Mohamed, and L. Amar, “Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm,” FUTURE INTERNET, vol. 14. no. 365, pp. 1-17, 2022. https://doi.org/ 10.3390/fi14120365.
[21]  C. D. Tran, N. Tam, N. Quy, and H. Binh, “An Energy-Efficient Scheme for Maximizing Data Aggregation Tree Lifetime in Wireless Sensor Network,” J AMB INTEL HUM COMP, vol. 14, 2023. https://doi.org/s12652-023-04621-w.
[22]  A. J. Yuste-Delgado, J. C. Cuevas-Martinez, and A. Trivino-Cabrera, “A Distributed Clustering Algorithm Guided by the Base Station to Extend the Lifetime of Wireless Sensor Networks,” SENSORS, vol. 20, no. 8, ID: 2312, pp. 1-18, 2020. https://doi.org/10.3390/s20082312.
[23]  A. M. Jubair, R. Hassan, A. Aman, H. Sallehudin, Z. Al-Mekhlafi, B. Mohammed, and M. Alsaffar, “Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols,” APPL SCI, vol. 11, no. 23, 11448, pp. 1-30, 2021. https://doi.org/10.3390/app112311448.
[24]  M. Mirzasadeghi and H. Bakhshi, “A New Method for Clustering Wireless Sensor Networks to Improve the Energy Consumption,” J COMMUN ENG, vol. 5, no. 2, pp. 136-149, 2016. https://doi.org/10.22070/jce.2017.1614.
[25]  M. Khan, J. Khan, K. Mahmood, I. Bari, H. Ali, N. Jan, and R. Ghoniem, “Algorithm for Increasing Network Lifetime in Wireless Sensor Networks Using Jumping and Mobile Sensor Nodes,” ELECTRONICS, vol. 11, no.2913, pp. 1-15, 2022. https://doi.org/ 10.3390/electronics11182913.
[26]  R. Medeiros, J. M. Villanueva, and E. Macedo, “Lifetime Increase for Wireless Sensor Networks Using Cellular Learning Automata,” WIRELESS PERS COMMUN, vol. 123, pp. 3413–3432, 2022. https://doi.org/10.1007/s11277-021-09295-1.
[27]  R. Marappan, P. Vardhini, G. Kaur, S. Murugesan, M. Kathiravan, N. Bharathiraja, and R. Venkatesan, “Efficient Evolutionary Modeling in Solving Maximization of Lifetime of Wireless Sensor Healthcare Networks,” SOFT COMPUT, vol. 27, Issue. 16, PP. 11853-11867, 2023. https://doi.org/ 10.1007 /s00500-023-08623-w.
[28]  M. Alaei, P. Sabbagh, and F. Yazdanpanah, “A QoS-aware Congestion Control Mechanism for Wireless Multimedia Sensor Networks,” WIREL NETW, vol. 25, no. 3, pp. 4173-4192, 2019. https://doi.org/10.1007/s11276-018-1738-8.
[29]  S. Misra and R. Kumar, “An Analytical Study of LEACH and PEGASIS Protocol in Wireless Sensor Networks,” International Conference on Innovations in information Embedded and Communication Systems (ICIIECS), 2017, https://doi.org/10.1109/ICIIECS.2017.8276118.
[30]  R. Chang and C. Kuo, “An Energy Efficient Routing Mechanism for Wireless Sensor Networks,” 20th International Conference on Advanced Information Networking and Applications (AINA’06), 2006. https://doi. org/10.1109/AINA.2006.86.
[31]  W. B. Heinzelman, A.P. Chandrakasan, and H. Balakrish-nan, “An Application-specific Protocol Architecture for Wireless Microsensor Networks,” IEEE T WIREL COMMUN, vol. 1, Issue 4, 2002. https://doi.org/10.1109/ TWC.2002.804190.

  • تاریخ دریافت 22 مهر 1404
  • تاریخ بازنگری 29 آذر 1404
  • تاریخ پذیرش 12 دی 1404
  • تاریخ انتشار 01 دی 1404