A CSI Feedback Framework based on Transmitting the Important Values and Generating the Others

TL;DR

This paper presents ITUG, a novel CSI feedback framework that transmits only critical values and uses a Transformer-based generative model to reconstruct the rest, improving robustness and accuracy in dynamic wireless channels.

Contribution

The paper introduces a new CSI feedback framework combining importance-based value transmission with a Transformer-based generative model, enhancing robustness over existing autoencoder methods.

Findings

ITUG outperforms autoencoder models in reconstruction accuracy.

The importance scoring and adaptive encoding improve transmission efficiency.

The framework maintains high performance across diverse channel conditions.

Abstract

The application of deep learning (DL)-based channel state information (CSI) feedback frameworks in massive multiple-input multiple-output (MIMO) systems has significantly improved reconstruction accuracy. However, the limited generalization of widely adopted autoencoder-based networks for CSI feedback challenges consistent performance under dynamic wireless channel conditions and varying communication overhead constraints. To enhance the robustness of DL-based CSI feedback across diverse channel scenarios, we propose a novel framework, ITUG, where the user equipment (UE) transmits only a selected portion of critical values in the CSI matrix, while a generative model deployed at the BS reconstructs the remaining values. Specifically, we introduce a scoring algorithm to identify important values based on amplitude and contrast, an encoding algorithm to convert these values into a bit stream for transmission using adaptive bit length and a modified Huffman codebook, and a Transformer-based generative network named TPMVNet to recover the untransmitted values based on the received important values. Experimental results demonstrate that the ITUG framework, equipped with a single TPMVNet, achieves superior reconstruction performance compared to several high-performance autoencoder models across various channel conditions.