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

Chien Hsiang Wu, Chin Feng Lai

研究成果: Article同行評審

摘要

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.

原文English
頁(從 - 到)1-9
頁數9
期刊Journal of Internet Technology
23
發行號1
出版狀態Published - 2022

All Science Journal Classification (ASJC) codes

  • 軟體
  • 電腦網路與通信

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