Generalizing Deep Learning-Based CSI Feedback in Massive MIMO via ID-Photo-Inspired Preprocessing

TL;DR

UniversalNet is a universal CSI feedback framework inspired by ID-photos that standardizes input formats to improve deep learning model generalization across diverse massive MIMO environments, with minimal modifications.

Contribution

The paper introduces UniversalNet, a novel universal CSI feedback framework that enhances model generalizability through standardized input formatting and efficient eigenvector joint optimization.

Findings

UniversalNet improves CSI feedback accuracy in unseen environments.

The framework requires minimal preprocessing modifications.

Enhanced sparsity of the precoding matrix boosts compression efficiency.

Abstract

Deep learning (DL)-based channel state information (CSI) feedback has shown great potential in improving spectrum efficiency in massive MIMO systems. However, DL models optimized for specific environments often experience performance degradation in others due to model mismatch. To overcome this barrier in the practical deployment, we propose UniversalNet, an ID-photo-inspired universal CSI feedback framework that enhances model generalizability by standardizing the input format across diverse data distributions. Specifically, UniversalNet employs a standardized input format to mitigate the influence of environmental variability, coupled with a lightweight sparsity-aligning operation in the transformed sparse domain and marginal control bits for original format recovery. This enables seamless integration with existing CSI feedback models, requiring minimal modifications in preprocessing and postprocessing without updating neural network weights. Furthermore, we propose an efficient eigenvector joint optimization method to enhance the sparsity of the precoding matrix by projecting the channel correlation into the eigenspace, thus improving the implicit CSI compression efficiency. Test results demonstrate that UniversalNet effectively improves generalization performance and ensures precise CSI feedback, even in scenarios with limited training diversity and previously unseen CSI environments.