  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.
