Data-driven Diversity Antenna Selection for MIMO Communication using Machine Learning

Chien Hsiang Wu, Chin Feng Lai

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

With the popularity of wireless application environments, smart antenna technology has completely changed the communication system. In order to improve the quality of wireless transmission, smart antennas have been widely used in wireless devices. Wireless signal modeling and prediction machine learning gradually replaced the traditional smart antenna selection method in the antenna selection solution. This article utilizes mobile devices to adjust the diversity antenna pattern for test verification in a MIMO wireless communication environment. The proposed method manipulates signal parameters through error vector magnitude (EVM) and adds data-driven training data. The results show that the SVM and NN methods proposed in this paper are 10.5% and 14% higher than the traditional EVM calculation methods, respectively. Thereby, realize precise antenna adjustment of mobile devices and improving wireless transmission quality.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalJournal of Internet Technology
Volume23
Issue number1
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Data-driven Diversity Antenna Selection for MIMO Communication using Machine Learning'. Together they form a unique fingerprint.

Cite this