Data-Augmentation-Aided Detection for MIMO Systems Under Hardware Impairments

This paper studies a data detection problem for multiple-input multiple-output (MIMO) communication systems with hardware impairments. To facilitate maximum likelihood (ML) data detection without knowledge of nonlinear and unknown hardware impairments, we develop a novel likelihood function estimation method based on data augmentation and boosting. In our method, we generate multiple augmented datasets by injecting noise with various distributions into seed data consisting of online received signals. We then estimate the likelihood function (LF) using each augmented dataset based on the expectationmaximization algorithm. We linearly combine the multiple LF estimates obtained from these datasets based on their reliability levels. Simulation results demonstrate that the ML detection combined with our LF estimation method outperforms existing methods, while also highlighting the effectiveness of our data augmentation approach.