Learn to Rapidly and Robustly Optimize Hybrid Precoding

TL;DR

This paper introduces a deep learning-based method for rapid and robust hybrid precoding optimization in massive MIMO systems, significantly reducing optimization iterations while maintaining or improving sum-rate performance.

Contribution

It proposes an interpretable deep learning approach that learns iteration-dependent hyperparameters for hybrid precoding, enhancing speed and robustness under noisy channel conditions.

Findings

Over ten times fewer iterations needed compared to traditional methods.

Achieves similar or better sum-rate performance.

Demonstrates robustness to noisy channel state information.

Abstract

Hybrid precoding plays a key role in realizing massive multiple-input multiple-output (MIMO) transmitters with controllable cost. MIMO precoders are required to frequently adapt based on the variations in the channel conditions. In hybrid MIMO, here precoding is comprised of digital and analog beamforming, such an adaptation involves lengthy optimization and depends on accurate channel state information (CSI). This affects the spectral efficiency when the channel varies rapidly and when operating with noisy CSI. In this work we employ deep learning techniques to learn how to rapidly and robustly optimize hybrid precoders, while being fully interpretable. We leverage data to learn iteration-dependent hyperparameter settings of projected gradient sum-rate optimization with a predefined number of iterations. The algorithm maps channel realizations into hybrid precoding settings while preserving the interpretable flow of the optimizer and improving its convergence speed. To cope with noisy CSI, we learn to optimize the minimal achievable sum-rate among all tolerable errors, proposing a robust hybrid precoding based on the projected conceptual mirror prox minimax optimizer. Numerical results demonstrate that our approach allows using over ten times less iterations compared to that required by conventional optimization with shared hyperparameters, while achieving similar and even improved sum-rate performance.