Machine Learning-based Compensation Methods for Weight Matrices of SVD-MIMO

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.