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

TL;DR

This paper introduces CLNet, a lightweight neural network with complex-valued input processing and attention mechanisms, significantly improving CSI feedback accuracy and reducing computational overhead in massive MIMO systems.

Contribution

CLNet is a novel neural network tailored for CSI feedback, leveraging complex-valued inputs and attention to outperform existing methods with lower computational costs.

Findings

Outperforms state-of-the-art by 5.41% accuracy

Reduces computational overhead by 24.1%

Effective in both indoor and outdoor scenarios

Abstract

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.