Open AccessDeep learning based EVM correction for RF receiver of vector signal analyser
Author(s) -
Jiang Zhengbo,
Liu Jingxin,
Ye Kairen,
Hong Wei
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.7691
Subject(s) - analyser , signal (programming language) , signal generator , metric (unit) , computer science , generator (circuit theory) , artificial neural network , electronic engineering , artificial intelligence , support vector machine , pattern recognition (psychology) , engineering , telecommunications , power (physics) , physics , optics , chip , operations management , quantum mechanics , programming language
This Letter presents a novel deep learning approach for optimising the receiver performance with respect to the error vector magnitude (EVM) metric, which was verified and evaluated by applying it to a self‐developed proprietary vector signal analyser (VSA). A four‐layer neural network was built and trained to estimate and correct the systematic error of the VSA receiver by using a calibrated commercially available vector signal generator as the training source. Experimental results show that the EVM performance of the self‐developed VSA is improved and approaches that of a state‐of‐the‐art VSA.