Variational Bayesian method improves signal detection in multiuser MIMO systems
Variational Bayesian Data Detection for Multiuser MIMO Systems Corrupted by Phase Noises
Information Theory
Summary
Signal transmission in multiuser MIMO systems is often affected by phase noise, which comes from imperfections in electronic components and causes errors in communication. Existing methods to handle phase noise either simplify the problem too much or become too slow as systems grow larger. The authors propose a new approach using variational Bayes, a statistical method that better estimates phase noise and detects data simultaneously with manageable computing effort. Their method outperforms traditional techniques, especially when phase noise is strong or the system uses many antennas.
Phase noiseMIMO systemsUplink communicationVariational BayesData detectionLocal oscillatorsSymbol error rateSignal processingWireless communicationStatistical inference
Authors
Toan-Van Nguyen, Duy H. N. Nguyen
Abstract
Phase noise (PN), arising from imperfect local oscillators, introduces multiplicative distortions that degrade the performance of communication systems. In uplink multiuser multiple-input multiple-output (MIMO) systems, this impairment is further compounded by the presence of independent oscillators at each transmit and receive antenna, each contributing an uncorrelated noise component. Existing PN compensation algorithms at the receiver either rely on linearization approximations that lose accuracy under severe PN conditions, or incur computational complexity that scales prohibitively with the number of antennas. To address these limitations, we propose a variational Bayes (VB) framework for joint PN estimation and data detection in uplink MIMO systems. We develop VB-based detectors that treat noise statistics as latent variables, and reformulate the inference problem by absorbing the transmitter PN into the transmitted signal, treating the resulting composite variable as the inference target. Under von Mises priors, this reformulation yields exact closed-form conjugate posterior updates, from which we derive an improved detector achieving superior performance at low complexity. Simulation results demonstrate that the proposed VB algorithm achieves lower symbol error rates than the Self-Interference Whitening (SIW) algorithm and conventional phase-noise-unaware detectors across a wide range of channel conditions, modulation orders, and PN severities, while remaining computationally scalable to large MIMO deployments.