<title>Abstract</title> To enhance the performance of massive MIMO systems, efficient downlink CSI compression and feedback are crucial in FDD mode. Deep learning (DL)-based methods surpass traditional compressed sensing but often rely on CNNs designed for image processing, neglecting essential channel and spatial information. This paper proposes WTSCNet, a novel CSI feedback network integrating wavelet transform convolution and attention mechanisms to balance network complexity and feature extraction. The encoder…