Learnable Model-Driven Performance Prediction and Optimization for Robust Regularized Zero-Forcing Precoding in MIMO Communications

State-of-the-art schemes for the performance analysis and optimization of multiple-input multiple-output (MIMO) communications generally suffer from degradation or even become ineffective in highly dynamic and complex environments with unknown interference and uncertain channel state information (CSI). To address these challenges and enhance network self-optimization, we propose a learnable model-driven regularized zero-forcing precoding scheme, and design a light-weight neural network for refined prediction of sum rate and detection error, by leveraging coarse model-driven approximations. Then, we estimate the CSI uncertainty based on the learned predictor in an iterative manner and, in turn, optimize both the transmit regularization term and subsequent receive power scaling factors. To achieve a favorable trade-off between convergence speed and robustness, we further propose a deep-unfolded projected gradient descent algorithm for power scaling.