  State-of-the-art schemes for performance analysis and optimization of
multiple-input multiple-output systems generally experience degradation or even
become invalid in dynamic complex scenarios with unknown interference and
channel state information (CSI) uncertainty. To adapt to the challenging
settings and better accomplish these network auto-tuning tasks, we propose a
generic learnable model-driven framework in this paper. To explain how the
proposed framework works, we consider regularized zero-forcing precoding as a
usage instance and design a light-weight neural network for refined prediction
of sum rate and detection error based on coarse model-driven approximations.
Then, we estimate the CSI uncertainty on the learned predictor in an iterative
manner and, on this basis, optimize the transmit regularization term and
subsequent receive power scaling factors. A deep unfolded projected gradient
descent based algorithm is proposed for power scaling, which achieves favorable
trade-off between convergence rate and robustness.
