A Variational Bayesian Inference-Inspired Unrolled Deep Network for MIMO Detection

TL;DR

This paper introduces VBINet, a variational Bayesian inference-inspired unrolled deep network for MIMO detection, which is efficient, adaptable to different channel types, and automatically learns noise variance, outperforming existing methods.

Contribution

The paper proposes a novel variational Bayesian inference-based unrolled deep network for MIMO detection that automatically learns noise variance and works efficiently with limited training data.

Findings

Achieves competitive performance on i.i.d. Gaussian and 3GPP MIMO channels.

Automatically learns noise variance, improving robustness.

Requires only a few parameters, enabling efficient training.

Abstract

The great success of deep learning (DL) has inspired researchers to develop more accurate and efficient symbol detectors for multi-input multi-output (MIMO) systems. Existing DL-based MIMO detectors, however, suffer several drawbacks. To address these issues, in this paper, we develop a model-driven DL detector based on variational Bayesian inference. Specifically, the proposed unrolled DL architecture is inspired by an inverse-free variational Bayesian learning framework which circumvents matrix inversion via maximizing a relaxed evidence lower bound. Two networks are respectively developed for independent and identically distributed (i.i.d.) Gaussian channels and arbitrarily correlated channels. The proposed networks, referred to as VBINet, have only a few learnable parameters and thus can be efficiently trained with a moderate amount of training samples. The proposed VBINet-based detectors can work in both offline and online training modes. An important advantage of our proposed networks over state-of-the-art MIMO detection networks such as OAMPNet and MMNet is that the VBINet can automatically learn the noise variance from data, thus yielding a significant performance improvement over the OAMPNet and MMNet in the presence of noise variance uncertainty. Simulation results show that the proposed VBINet-based detectors achieve competitive performance for both i.i.d. Gaussian and realistic 3GPP MIMO channels.