  The efficacy of massive multiple-input multiple-output (MIMO) techniques
heavily relies on the accuracy of channel state information (CSI) in frequency
division duplexing (FDD) systems. Many works focus on CSI compression and
quantization methods to enhance CSI reconstruction accuracy with lower feedback
overhead. In this letter, we propose CsiConformer, a novel CSI feedback network
that combines convolutional operations and self-attention mechanisms to improve
CSI feedback accuracy. Additionally, a new quantization module is developed to
improve encoding efficiency. Experiment results show that CsiConformer
outperforms previous state-of-the-art networks, achieving an average accuracy
improvement of 17.67\% with lower computational overhead.
