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We investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical application. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to ‘model’ the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design ....

Approximate Message Passing Amp , Rtificial Neural Networks , Bayes Methods , Bayesian Inference , Factor Graphs , Ardware Imperfections , Iq Imbalance , Message Passing , Imo Communication , Ultiple Input Multiple Output Mimo , Eural Networks Nns , Ower Amplifier Nonlinearity , Signal Detection ,