Sparse linear regression for optimizing design parameters of double t-shaped monopole antennas

Yashika Sharma, Junqiang Wu, Hao Xin, Hao Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper we propose using sparse linear regression for antenna design optimization. The new method provides an automatic, efficient, and reliable framework to identify optimal design parameters for a reference dual band double T-shaped monopole antenna in order to achieve the best performance in terms of the fractional bandwidth of its two bands.

Original languageEnglish (US)
Title of host publication2017 IEEE Antennas and Propagation Society International Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages347-348
Number of pages2
Volume2017-January
ISBN (Electronic)9781538632840
DOIs
StatePublished - Oct 18 2017
Event2017 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2017 - San Diego, United States
Duration: Jul 9 2017Jul 14 2017

Other

Other2017 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2017
CountryUnited States
CitySan Diego
Period7/9/177/14/17

Keywords

  • Antenna optimization
  • Lasso
  • Linear regression
  • Machine learning

ASJC Scopus subject areas

  • Radiation
  • Computer Networks and Communications
  • Instrumentation

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