A deep learning-based approach to lightweight CSI feedback

Abstract

Some deep learning-based CSI feedback models have high computational and storage requirements, which limit their feedback efficiency on mobile devices, making them challenging to deploy on a large scale. Therefore, to address the poor feasibility of existing deep learning-based CSI feedback methods in practical deployment on user devices, a lightweight CSI feedback network suitable for mobile terminals is proposed to reduce the demand for computational and storage resources. This network enables efficient feedback on mobile devices. It leverages the design concept of a multi-resolution network to enhance feedback performance while reducing the number of parameters and computational load of the feedback network. Additionally, it employs dynamic convolution to effectively capture the contextual information of CSI. Through simulation comparison, it is found that compared with other lightweight CSI feedback networks based on deep learning, the feedback accuracy in each scenario is improved by 8.57% on average.