  Unleashing the full potential of massive MIMO in FDD mode by reducing the
overhead of CSI feedback has recently garnered attention. Numerous deep
learning for massive MIMO CSI feedback approaches have demonstrated their
efficiency and potential. However, most existing methods improve accuracy at
the cost of computational complexity and the accuracy decreases significantly
as the CSI compression rate increases. This paper presents a novel neural
network CLNet tailored for CSI feedback problem based on the intrinsic
properties of CSI. CLNet proposes a forge complex-valued input layer to process
signals and utilizes attention mechanism to enhance the performance of the
network. The experiment result shows that CLNet outperforms the
state-of-the-art method by average accuracy improvement of 5.41\% in both
outdoor and indoor scenarios with average 24.1\% less computational overhead.
Codes for deep learning-based CSI feedback CLNet are available at GitHub.
