TR2019-139

Variational Bayesian Symbol Detection for Massive MIMO Systems with Symbol-Dependent Transmit Impairments



In this paper, we propose a variational Bayesian inference approach for a low-complexity symbol detection for massive MIMO systems with symbol-dependent transmit-side impairments. This study is motivated by observations that realworld communication transceivers are often affected by the hardware impairments, such as non-linearities of power amplifiers, I/Q imbalance, phase drifts due to non-ideal oscillators, and carrier frequency offsets. Particularly, symbol-dependent perturbations are fully accounted into the designed hierarchical signal model as unknown model parameters. The developed variational Bayesian symbol detector is able to learn the unknown perturbations in an iterative fashion. Numerical evaluation confirms the effectiveness of the proposed approach.