{"title":"Machine Learning-based Compensation Methods for Weight Matrices of SVD-MIMO","authors":"Kiminobu Makino, T. Nakagawa, N. Iai","doi":"10.1587/transcom.2023ebp3033","DOIUrl":null,"url":null,"abstract":"SUMMARY Thispaperproposesandevaluatesmachinelearning(ML)- basedcompensationmethods forthetransmit(Tx) weightmatricesofactual singular value decomposition (SVD)-multiple-input and multiple-output (MIMO) transmissions. These methods train ML models and compensate theTxweightmatricesbyusingalargeamountoftrainingdatacreatedfromstatisticaldistributions.Moreover,thispaperproposessimplifiedchannel metricsbasedonthechannelqualityofactualSVD-MIMOtransmissionstoevaluatecompensationperformance.Theoptimalparametersaredeter-mined from many ML parameters by using the metrics, and the metrics for this determination are evaluated. Finally, a comprehensive computer simulation shows that the optimal parameters improve performance by up to 7.0dB compared with the conventional method.","PeriodicalId":50385,"journal":{"name":"IEICE Transactions on Communications","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEICE Transactions on Communications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1587/transcom.2023ebp3033","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
SUMMARY Thispaperproposesandevaluatesmachinelearning(ML)- basedcompensationmethods forthetransmit(Tx) weightmatricesofactual singular value decomposition (SVD)-multiple-input and multiple-output (MIMO) transmissions. These methods train ML models and compensate theTxweightmatricesbyusingalargeamountoftrainingdatacreatedfromstatisticaldistributions.Moreover,thispaperproposessimplifiedchannel metricsbasedonthechannelqualityofactualSVD-MIMOtransmissionstoevaluatecompensationperformance.Theoptimalparametersaredeter-mined from many ML parameters by using the metrics, and the metrics for this determination are evaluated. Finally, a comprehensive computer simulation shows that the optimal parameters improve performance by up to 7.0dB compared with the conventional method.
期刊介绍：
The IEICE Transactions on Communications is an all-electronic journal published occasionally by the Institute of Electronics, Information and Communication Engineers (IEICE) and edited by the Communications Society in IEICE. The IEICE Transactions on Communications publishes original, peer-reviewed papers that embrace the entire field of communications, including:
- Fundamental Theories for Communications
- Energy in Electronics Communications
- Transmission Systems and Transmission Equipment for Communications
- Optical Fiber for Communications
- Fiber-Optic Transmission for Communications
- Network System
- Network
- Internet
- Network Management/Operation
- Antennas and Propagation
- Electromagnetic Compatibility (EMC)
- Wireless Communication Technologies
- Terrestrial Wireless Communication/Broadcasting Technologies
- Satellite Communications
- Sensing
- Navigation, Guidance and Control Systems
- Space Utilization Systems for Communications
- Multimedia Systems for Communication
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