Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-18T14:50:03.540Z Has data issue: false hasContentIssue false
  • English
  • Français

Variable Z0 applied to the optimal design of multi-stub matching network and a meander monopole

Published online by Cambridge University Press:  13 December 2013

Nihad Dib ,
Ashraf Sharaqa  and
Richard A. Formato
Nihad Dib*
Affiliation:
Electrical Engineering Department, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan
Ashraf Sharaqa
Affiliation:
Communication and Security Projects Division, WorleyParsons Arabia Ltd., P. O. Box 31699, Al-Khobar 31952, Saudi Arabia
Richard A. Formato
Affiliation:
Consulting Engineer and Patent Attorney, P.O. Box 1714, Harwich, MA 02645, USA
*
Corresponding author: N. Dib Email: nihad@just.edu.jo
Get access Rights & Permissions [Opens in a new window]

Abstract

Variable Z0, a new concept in antenna design and optimization, is applied to two optimization problems: a multi-stub matching network (MSMN) using biogeography-based optimization (BBO), and an ultra wideband meander monopole antenna (MMA) using central force optimization (CFO). BBO is a newly-proposed stochastic global search and optimization evolutionary algorithm (EA) used to determine MSMN stub lengths and locations for optimum (minimum) reflection coefficient. CFO is a deterministic EA used to optimize the MMA's impedance bandwidth (IBW) while maintaining good average gain without considering the radiation pattern in detail. Two cases are investigated for both problems: (a) fixed characteristic impedance Z0, and (b) variable characteristic impedance. In the first case, Z0 is a fixed user-specified parameter (the traditional methodology), whereas in the second, it is a true variable quantity whose value is determined by the optimization methodology, which is a new technology. Variable Z0 is a fundamentally different design approach in optimization problems. BBO's fixed Z0 results for MSMN are compared to published data computed using Nelder–Mead optimization with BBO exhibiting better performance. BBO's results are improved even more using Variable Z0 technology. A similar performance improvement is seen for Variable Z0 applied to the CFO-optimized MMA.

Keywords

Variable Z0Matching networksMonopole antennaOptimizationBiogeography-based optimizationCentral force optimization
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 6 , Issue 5: MEMSWAVE Symposium 2013 , October 2014 , pp. 505 - 514
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1]Pozar, D.: Microwave Engineering, 3rd ed., Wiley, New York, 2005.Google Scholar
[2]Ulker, S.: Particle swarm optimization application to microwave circuits. Microw. Opt. Technol. Lett., 50 (5) (2008), 1333–1336.CrossRefGoogle Scholar
[3]Deniz, E.; Ulker, S.: Clonal selection algorithm application to simple microwave matching network. Microw. Opt. Technol. Lett., 53 (5) (2011), 991–993.CrossRefGoogle Scholar
[4]Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput., 12 (6) (2008), 702–713.CrossRefGoogle Scholar
[5]Simon, D.; Ergezer, M.; Du, D.; Rarick, R.: Markov models for biogeography-based optimization. IEEE Trans. Syst. Man Cybern. B: Cybern., 41 (1) (2011), 299–306.CrossRefGoogle ScholarPubMed
[6]Simon, D.; Rarick, R.; Ergezer, M.; Du, D.: Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms. Inf. Sci., 181 (7) (2011), 1224–1248.CrossRefGoogle Scholar
[7]Roy, P.; Ghoshal, S.; Thakur, S.: Biogeography based optimization for multi-constraint optimal power flow with emission and non-smooth cost function. Expert Syst. Appl., 37 (12) (2010), 8221–8228.CrossRefGoogle Scholar
[8]Bhattacharya, A.; Chattopadhyay, P.: Hybrid differential evolution with biogeography based optimization for solution of economic load dispatch. IEEE Trans. Power Syst., 25 (4) (2010), 1955–1964.CrossRefGoogle Scholar
[9]Singh, U.; Singla, H.; Kamal, T.: Design of Yagi–Uda antenna using biogeography based optimization. IEEE Trans. Antennas Propag., 58 (10) (2010), 3375–3379.CrossRefGoogle Scholar
[10]Sharaqa, A.; Dib, N.: Design of linear and circular antenna arrays using biogeography based optimization, in 2011 IEEE Jordan Conf. on Applied Electrical Engineering and Computing Technologies (AEECT), Jordan, 2011.CrossRefGoogle Scholar
[11]Singh, U.; Kumar, H.; Kamal, T.: Linear array synthesis using biogeography based optimization. Progr. Electromagn. Res. M, 11 (2010), 25–36.CrossRefGoogle Scholar
[12]Dib, N.; Sharaqa, A.: On the optimal design of non-uniform circular antenna arrays. J. Appl. Electromagn., 14 (1) (2012), 42–59.Google Scholar
[13]Singh, U.; Kamal, T.: Design of non-uniform circular antenna arrays using biogeography-based optimization. IET Microw. Antennas Propag., 5 (11) (2011), 1365–1370.CrossRefGoogle Scholar
[14]Lohokare, M.; Pattnaik, S.; Devi, S.; Panigrahi, B.; Bakwad, K.; Joshi, J.: Modified BBO and calculation of resonant frequency of circular microstrip antenna, in World Congress on Nature & Biologically Inspired Computing, Coimbatore, India, 2009, 487–492.CrossRefGoogle Scholar
[15]Lohokare, M.; Pattnaik, S.; Devi, S.; Bakwad, K.; Joshi, J.: Parameter calculation of rectangular microstrip antenna using biogeography-based optimization, in Applied Electromagnetics Conf., Kolkata, India, 2009.CrossRefGoogle Scholar
[16]Gong, W.; Cai, Z.; Ling, C.: DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput., 15 (4) (2011), 645–665.CrossRefGoogle Scholar
[17]Kundra, H.; Sood, M.: Cross-country path finding using hybrid approach of PSO and BBO. Int. J. Comput. Appl., 7 (6) (2010), 15–19.Google Scholar
[18]Regoli, C.: Impedance matching by using a multi-stub system, in Proceedings of the 7th WSEAS Int. Conf. on Simulation, Modeling and Optimization, Beijing, China, 2007, 15–17.Google Scholar
[19]Moreno, E.; Vazquez, J.; Baneira-Gomez, M.: Matching in transmission lines with N stubs: analytic development and computational treatment. Int. J. Electr. Eng.. Educ., 36 (1999), 154–162.CrossRefGoogle Scholar
[20]Formato, R.: Improving bandwidth of Yagi–Uda arrays. Wirel. Eng. Technol., 3 (2012), 18–24.CrossRefGoogle Scholar
[21]Biogeography-based optimization (BBO). [Online]. Available: http://academic.csuohio.edu/simond/bbo/Google Scholar
[22]Formato, R.: Central force optimization: a new metaheuristic with applications in applied electromagnetics. Progr. Electromagn. Res., 77 (2007), 425–491.CrossRefGoogle Scholar
[23]Formato, R.: Improved CFO algorithm for antenna optimization. Progr. Electromagn. Res. B, 19 (2010), 405–425.CrossRefGoogle Scholar
[24]Qubati, G.; Formato, R.; Dib, N.: Antenna benchmark performance and array synthesis using central force optimization. IET Microw., Antennas Propag., 4 (5) (2010), 583–592.CrossRefGoogle Scholar
[25]Formato, R.: Central force optimization applied to the PBM suite of antenna benchmarks. February, 2010: http://arxiv.org/abs/1003.0221.Google Scholar
[26]Formato, R.: New techniques for increasing antenna bandwidth with impedance loading. Progress in Electromagnetics Research B, 29 (2011), 269–288.CrossRefGoogle Scholar
[27]Formato, R.: High-performance indoor VHF-UHF antennas: technology update report. National Association of Broadcasters (NAB), FASTROAD (Flexible Advanced Services for Television and Radio On All Devices), Technology Advocacy Program, 15 May 2010: http://www.nabfastroad.org/NABHighperformanceIndoorTVantennaRpt.pdfGoogle Scholar
[28]Formato, R.: Parameter-free deterministic global search with simplified central force optimization, in Advanced Intelligent Computing Theories and Applications (ICIC2010), Lecture Notes in Computer Science, Vol. 6215, Springer-Verlag, Berlin, Heidelberg, 2010, 309–318.Google Scholar
[29]Formato, R.: UWB array design using Variable Z0 technology and central force optimization ver. 2. August, 2011: http://arXiv.org/abs/1108.0901.Google Scholar
[30]Formato, R.: A novel methodology for antenna design and optimization: variable Z0 (ver. 2). July 2011: http://arXiv.org/abs/1107.1437.Google Scholar
[31]Formato, R.: Variable Z0 antenna device design system and method. US Patent Application Publication No. US 2012/0331436 A1, December 27, 2012 (http://www.uspto.gov).Google Scholar
[32]Formato, R.: Issues in antenna optimization – a monopole case study. Applied Computational Electromagnetics Society (ACES) Journal, in press.Google Scholar
[33]Burke, G.: Numerical Electromagnetics Code – NEC-4.2 Method of Moments, Part I: User's Manual, LLNL-SM-490875, Lawrence Livermore National Laboratory, Livermore, California, USA, July 15, 2011.Google Scholar
[34]Burke, G.: Numerical Electromagnetics Code – NEC-4, Method of Moments, Part I: User's Manual and Part II: Program Description – Theory, UCRL-MA-109338, Lawrence Livermore National Laboratory, Livermore, California, USA, January 1992.Google Scholar
[35]Burke, G.; Poggio, A.: Numerical Electromagnetics Code (NEC) – Method of Moments, Parts I, II and III, UCID-19934, Lawrence Livermore National Laboratory, Livermore, California, USA, January 1981.Google Scholar
[36]4NEC2 antenna modeling freeware, Arie Voors: http://home.ict.nl/~arivoors/.Google Scholar
[37]Altshuler, E.: Design of a vehicular antenna for GPS/iridium using a genetic algorithm. IEEE Trans. Antennas Propag., 48 (2000), 968–972.CrossRefGoogle Scholar