Reduced-Overhead CSI Feedback for Massive MIMO through Selective Component Processing

Efficient and reliable transmission of CSI is crucial for maintaining optimal performance in wireless communication networks. However, the feedback overhead remains a significant challenge. In this paper, we propose a novel feedback compression scheme leveraging the Sobel operator to enhance the efficiency of CSI transmission as yet another novel computer vision based solution. Specifically, our proposed scheme differentiates the dynamic and quasi-static components in given CSI matrices and builds on top of the CsiNet to learn and compress the dynamic components in CSI matrices, thereby significantly reducing feedback overhead while maintaining high reconstruction quality. We perform extensive simulations to demonstrate the effectiveness of our proposed scheme. Evaluation results indicate that our algorithm achieves comparable compression performance to baseline algorithms while significantly reducing computational overhead.