篇名 |
Data-driven Diversity Antenna Selection for MIMO Communication using Machine Learning
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並列篇名 | Data-driven Diversity Antenna Selection for MIMO Communication using Machine Learning |
作者 | Chien-Hsiang Wu、Chin-Feng Lai |
英文摘要 | 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.
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起訖頁 | 001-009 |
關鍵詞 | Antenna selection、Diversity antenna pattern、Data-driven、Error vector magnitude |
刊名 | 網際網路技術學刊 |
期數 | 202201 (23:1期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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