CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback
This work addresses the challenge of computational complexity and accuracy degradation in CSI feedback for massive MIMO, which is incremental but offers specific gains for wireless communication systems.
The paper tackles the problem of reducing CSI feedback overhead in massive MIMO systems by proposing CLNet, a lightweight neural network that improves accuracy by 5.41% on average while reducing computational overhead by 24.1% compared to state-of-the-art methods.
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.