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Unleashing the full potential of massive MIMO in FDD mode by reducing theoverhead of CSI feedback has recently garnered attention. Numerous deeplearning for massive MIMO CSI feedback approaches have demonstrated theirefficiency and potential. However, most existing methods improve accuracy atthe cost of computational complexity and the accuracy decreases significantlyas the CSI compression rate increases. This paper presents a novel neuralnetwork CLNet tailored for CSI feedback problem based on the intrinsicproperties of CSI. CLNet proposes a forge complex-valued input layer to processsignals and utilizes attention mechanism to enhance the performance of thenetwork. The experiment result shows that CLNet outperforms thestate-of-the-art method by average accuracy improvement of 5.41\% in bothoutdoor and indoor scenarios with average 24.1\% less computational overhead.Codes for deep learning-based CSI feedback CLNet are available at GitHub.

CLNet: Complex Input Lightweight Neural Network Designed for Massive MIMO CSI Feedback