Wilkinson Power Divider for High Power Amplifier Employed for Wireless Communications
Keywords:
Wilkinson Power Dividers, High Power Amplifier, Field Effect Transistor, Low-Pass Filter, MATLAB Software, Wireless Communications
Abstract
High power amplifier based on the Wilkinson energy has been constructed and fabricated in this study. The 45-50W primary speaker unit is manufactured using horizontally distributed metal oxide semiconductor (LDMOS) materials and ferromagnetic materials. Field Effect Transistor (FET) Transistor PTFA260451E. Wilkinson Energy Complex has employed to merge dual entered energies to achieve 90 watts of power. The suggested power amplifier was investigated, planned and advanced utilizig (MATLAB) software traditional Wilkinson power dividers (WPDs) might produce acceptable response near to suggested center frequency Anyhow, such WPDs might further displayed low out of range response whereas it needs adding significant procedures. In favor of enhancing the preset performance state, a modification has been made Proposed utilizig microstrip WPD which shows significantly improved stopping range with large separation. A low-pass filter (LPF) model is employed in each sections of the harmonic damping splitter to save power. The suggested structure is constructed using microelectromechanical system oscillators (MEMS) against the film's sonic resonator (FBAR) with a center frequency of 2.65-3 GHz with two sections and 50 Ohm impedance for 90 watts amplification power.
Downloads
Download data is not yet available.
References
1. Feiri, M., Petit, J., and Kargl, F., “The Impact of Security on Cooperative Awareness in VANET,” in IEEEVehicular Networking Conference, VNC, 2014.
2. Asuquo, P., Cruickshank, H., Morley, J., Anyigor Ogah, C.P., Lei, A., Hathal, W., and Bao, S., “Security and Privacy in Location-Based Services for Vehicular and Mobile Communications: An Overview, Challenges and Countermeasures,” in IEEE Internet of Things, 2018.
3. Mansour, M. B., Salama, C., Mohamed, H. K., and Hammad, S. A., “CARCLOUD: A Secure Architecture for Vehicular Cloud Computing,” in 14th Embedded Security in Cars Europe Conference, ESCAR, Germany, Nov. 2016.
4. Intisar Salem Hamed Al-Mandhari , "A Machine Learning Based Investigation of Cloud Service Attacks", A Doctoral Thesis Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Lough borough University, April 2019. Copyright 2019 Intisar Salem Hamed Al-Mandhari.
5. Rasheed, A., Gillani, S., Ajmal, S., and Qayyum, A., “Vehicular Ad Hoc Network (VANET): A Survey, Challenges, and Applications,” in Vehicular Ad-Hoc Networks for Smart Cities,pp. 39-51, Springer, Singapore, 2017. [2] Jaydip Kumar, "Cloud Computing Security Issues and Its Challenges": International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-1S4, June 2019.
6. Wang, S., and Yao, N., “A RSU-aided distributed trust framework for pseudonym-enabled privacy preservation in VANETs,” in Wireless Networks, pp.1-17, 2018.
7. Guo, N., Ma, L., and Gao, T., “Independent Mix Zone for Location Privacy in Vehicular Networks,” in IEEE Access, 2018.
8. Amro, B., “Protecting Privacy in VANETs Using Mix Zones With Virtual Pseudonym Change,”arXiv preprint arXiv:1801.10294, 2018.
9. M. A. Javed, E. Ben Hamida, A. Al-fuqaha, and B. Bhargava, “Adaptive Security for Intelligent Transport System Applications,” IEEE Intell. Transp. Syst. Mag., vol. 10, no. April, pp. 110–120, 2018.
10. K. Khanna, B. K. Panigrahi, and A. Joshi, “AI-based approach to identify compromised meters in data integrity attacks on smart grid,” 2018.
11. N. Nissim, A. Cohen, and Y. Elovici, “ALDOCX: Detection of Unknown Malicious Microsoft Office Documents Using Designated Active Learning Methods Based on New Structural Feature Extraction Methodology,” IEEE Trans. Inf. Forensics Secur., vol. 12, no. 3, pp. 631–646, 2017.
12. H. Sedjelmaci and S. M. Senouci, “An Accurate Security Game for Low-Resource IoT Devices,” vol. 66, no. 10, pp. 9381–9393, 2017.
13. Y. Du, J. Wang, and Q. Li, “An android malware detection approach using community structures of weighted function call graphs,” IEEE Access, vol. 5, pp. 17478–17486, 2017.
14. M. H. Kamarudin, C. Maple, T. Watson, and N. S. Safa, “A LogitBoost-Based Algorithm for Detecting Known and Unknown Web Attacks,” IEEE Access, vol. 5, pp. 26190–26200, 2017.
15. V. T. Alaparthy and S. D. Morgera, “A Multi-Level Intrusion Detection System for Wireless Sensor Networks Based on Immune Theory,” IEEE Access, vol. 6, pp. 47364–47373, 2018.
16. M. H. Ali, B. Abbas, D. Al, A. Ismail, and M. F. Zolkipli, “A New Intrusion Detection System Based on Fast Learning Network and Particle Swarm Optimization,” IEEE Access, vol. 6, pp. 20255–20261, 2018.
17. P. Feng, J. Ma, C. Sun, and Y. Ma, “A Novel Dynamic Android Malware Detection System With Ensemble Learning,” IEEE Access, vol. 6, pp. 30996–31011, 2018.
18. D. Hu, L. Wang, W. Jiang, and S. Zheng, “A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks,” IEEE Access, vol. 6, pp. 38303–38314, 2018.
19. Y. Gao, Y. U. Liu, Y. Jin, J. Chen, and H. Wu, “A Novel Semi-Supervised Learning Approach for Network Intrusion Detection on Cloud-Based Robotic System,” IEEE Access, vol. 6, pp. 50927–50938, 2018. [15] G. Tyler, “Information Assurance Tools Report Intrusion Detection Systems,” Information Assurance Technology Analysis Center (IATAC), September 2009.
20. C. Yin, Y. Zhu, J. Fei, and X. He, “A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks,” vol. 5, 2017.
21. N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A Deep Learning Approach to Network Intrusion Detection,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 2, no. 1, pp. 41–50, 2018.
22. H. Peng, Z. Sun, X. Zhao, S. Tan, and Z. Sun, “A Detection Method for Anomaly Flow in Software Defined Network,” IEEE Access, vol. 6, pp. 27809–27817, 2018.
23. C. Wang, Z. Zhao, L. Gong, L. Zhu, Z. Liu, and X. Cheng, “A Distributed Anomaly Detection System for In-Vehicle Network Using HTM,” IEEE Access, vol. 6, pp. 9091–9098, 2018.
24. Y. Han, T. Alpcan, J. Chan, C. Leckie, and B. I. P. Rubinstein, “A Game Theoretical Approach to Defend Against Co-Resident Attacks in Cloud Computing : Preventing Co-Residence Using Semi-Supervised Learning,” IEEE Trans. Inf. Forensics Secur., vol. 11, no. 3, pp. 556–570, 2016.
25. L. Dritsoula, P. Loiseau, and J. Musacchio, “A Game-Theoretic Analysis of Adversarial Classification,” vol. 12, no. 12, pp. 3094–3109, 2017. [17] B. Sevak, “Security against Side Channel Attack in Cloud Computing,” Int. J. Eng. Adv. Technol., vol. 2, no. 2, pp. 183–186, 2012.
26. Y Z An, Z F Zaaba, et. al., "Reviews on Security Issues and Challenges in Cloud Computing", International Engineering Research and Innovation Symposium (IRIS) IOP Publishing IOP Conf. Series: Materials Science and Engineering 160 (2016) 012106 doi:10.1088/1757-899X/160/1/012106.
27. Ahmed Khalid Salih, A survey of Cloud Computing Security challenges and solutions", see discussions, stats, and author profiles for this,publicationat:https://www.researchgate.net/publication/311075805Article, January 2016
28. Akashdeep Bhardwaj, " Security Algorithms for Cloud Computing", International Conference on Computational Modeling and Security (CMS 2016).
29. Lubna Luxmi Dhirani, et. al., "Tenant - Vendor and Third-Party Agreements for the Cloud: Considerations for Security Provision Article in International Journal of Software Engineering and its Applications • December 2016 DOI: 10.14257/ijseia.2016.10.12.37.
30. Zeinab Lashkaripour," SECURITY IMPLICATIONS AND REQUIREMENTS - CLOUD ENVIRONMENT", Conference Article, July 2016.
2. Asuquo, P., Cruickshank, H., Morley, J., Anyigor Ogah, C.P., Lei, A., Hathal, W., and Bao, S., “Security and Privacy in Location-Based Services for Vehicular and Mobile Communications: An Overview, Challenges and Countermeasures,” in IEEE Internet of Things, 2018.
3. Mansour, M. B., Salama, C., Mohamed, H. K., and Hammad, S. A., “CARCLOUD: A Secure Architecture for Vehicular Cloud Computing,” in 14th Embedded Security in Cars Europe Conference, ESCAR, Germany, Nov. 2016.
4. Intisar Salem Hamed Al-Mandhari , "A Machine Learning Based Investigation of Cloud Service Attacks", A Doctoral Thesis Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Lough borough University, April 2019. Copyright 2019 Intisar Salem Hamed Al-Mandhari.
5. Rasheed, A., Gillani, S., Ajmal, S., and Qayyum, A., “Vehicular Ad Hoc Network (VANET): A Survey, Challenges, and Applications,” in Vehicular Ad-Hoc Networks for Smart Cities,pp. 39-51, Springer, Singapore, 2017. [2] Jaydip Kumar, "Cloud Computing Security Issues and Its Challenges": International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-1S4, June 2019.
6. Wang, S., and Yao, N., “A RSU-aided distributed trust framework for pseudonym-enabled privacy preservation in VANETs,” in Wireless Networks, pp.1-17, 2018.
7. Guo, N., Ma, L., and Gao, T., “Independent Mix Zone for Location Privacy in Vehicular Networks,” in IEEE Access, 2018.
8. Amro, B., “Protecting Privacy in VANETs Using Mix Zones With Virtual Pseudonym Change,”arXiv preprint arXiv:1801.10294, 2018.
9. M. A. Javed, E. Ben Hamida, A. Al-fuqaha, and B. Bhargava, “Adaptive Security for Intelligent Transport System Applications,” IEEE Intell. Transp. Syst. Mag., vol. 10, no. April, pp. 110–120, 2018.
10. K. Khanna, B. K. Panigrahi, and A. Joshi, “AI-based approach to identify compromised meters in data integrity attacks on smart grid,” 2018.
11. N. Nissim, A. Cohen, and Y. Elovici, “ALDOCX: Detection of Unknown Malicious Microsoft Office Documents Using Designated Active Learning Methods Based on New Structural Feature Extraction Methodology,” IEEE Trans. Inf. Forensics Secur., vol. 12, no. 3, pp. 631–646, 2017.
12. H. Sedjelmaci and S. M. Senouci, “An Accurate Security Game for Low-Resource IoT Devices,” vol. 66, no. 10, pp. 9381–9393, 2017.
13. Y. Du, J. Wang, and Q. Li, “An android malware detection approach using community structures of weighted function call graphs,” IEEE Access, vol. 5, pp. 17478–17486, 2017.
14. M. H. Kamarudin, C. Maple, T. Watson, and N. S. Safa, “A LogitBoost-Based Algorithm for Detecting Known and Unknown Web Attacks,” IEEE Access, vol. 5, pp. 26190–26200, 2017.
15. V. T. Alaparthy and S. D. Morgera, “A Multi-Level Intrusion Detection System for Wireless Sensor Networks Based on Immune Theory,” IEEE Access, vol. 6, pp. 47364–47373, 2018.
16. M. H. Ali, B. Abbas, D. Al, A. Ismail, and M. F. Zolkipli, “A New Intrusion Detection System Based on Fast Learning Network and Particle Swarm Optimization,” IEEE Access, vol. 6, pp. 20255–20261, 2018.
17. P. Feng, J. Ma, C. Sun, and Y. Ma, “A Novel Dynamic Android Malware Detection System With Ensemble Learning,” IEEE Access, vol. 6, pp. 30996–31011, 2018.
18. D. Hu, L. Wang, W. Jiang, and S. Zheng, “A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks,” IEEE Access, vol. 6, pp. 38303–38314, 2018.
19. Y. Gao, Y. U. Liu, Y. Jin, J. Chen, and H. Wu, “A Novel Semi-Supervised Learning Approach for Network Intrusion Detection on Cloud-Based Robotic System,” IEEE Access, vol. 6, pp. 50927–50938, 2018. [15] G. Tyler, “Information Assurance Tools Report Intrusion Detection Systems,” Information Assurance Technology Analysis Center (IATAC), September 2009.
20. C. Yin, Y. Zhu, J. Fei, and X. He, “A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks,” vol. 5, 2017.
21. N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A Deep Learning Approach to Network Intrusion Detection,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 2, no. 1, pp. 41–50, 2018.
22. H. Peng, Z. Sun, X. Zhao, S. Tan, and Z. Sun, “A Detection Method for Anomaly Flow in Software Defined Network,” IEEE Access, vol. 6, pp. 27809–27817, 2018.
23. C. Wang, Z. Zhao, L. Gong, L. Zhu, Z. Liu, and X. Cheng, “A Distributed Anomaly Detection System for In-Vehicle Network Using HTM,” IEEE Access, vol. 6, pp. 9091–9098, 2018.
24. Y. Han, T. Alpcan, J. Chan, C. Leckie, and B. I. P. Rubinstein, “A Game Theoretical Approach to Defend Against Co-Resident Attacks in Cloud Computing : Preventing Co-Residence Using Semi-Supervised Learning,” IEEE Trans. Inf. Forensics Secur., vol. 11, no. 3, pp. 556–570, 2016.
25. L. Dritsoula, P. Loiseau, and J. Musacchio, “A Game-Theoretic Analysis of Adversarial Classification,” vol. 12, no. 12, pp. 3094–3109, 2017. [17] B. Sevak, “Security against Side Channel Attack in Cloud Computing,” Int. J. Eng. Adv. Technol., vol. 2, no. 2, pp. 183–186, 2012.
26. Y Z An, Z F Zaaba, et. al., "Reviews on Security Issues and Challenges in Cloud Computing", International Engineering Research and Innovation Symposium (IRIS) IOP Publishing IOP Conf. Series: Materials Science and Engineering 160 (2016) 012106 doi:10.1088/1757-899X/160/1/012106.
27. Ahmed Khalid Salih, A survey of Cloud Computing Security challenges and solutions", see discussions, stats, and author profiles for this,publicationat:https://www.researchgate.net/publication/311075805Article, January 2016
28. Akashdeep Bhardwaj, " Security Algorithms for Cloud Computing", International Conference on Computational Modeling and Security (CMS 2016).
29. Lubna Luxmi Dhirani, et. al., "Tenant - Vendor and Third-Party Agreements for the Cloud: Considerations for Security Provision Article in International Journal of Software Engineering and its Applications • December 2016 DOI: 10.14257/ijseia.2016.10.12.37.
30. Zeinab Lashkaripour," SECURITY IMPLICATIONS AND REQUIREMENTS - CLOUD ENVIRONMENT", Conference Article, July 2016.
Published
2023-12-04
How to Cite
Suham A. Albderi. (2023). Wilkinson Power Divider for High Power Amplifier Employed for Wireless Communications. Central Asian Journal of Theoretical and Applied Science, 4(12), 26-38. Retrieved from https://cajotas.casjournal.org/index.php/CAJOTAS/article/view/1363
Section
Articles
