SemCSINet: A Semantic-Aware CSI Feedback Network in Massive MIMO Systems

TL;DR

SemCSINet introduces a semantic-aware Transformer framework that integrates CQI into CSI feedback, significantly improving accuracy and robustness in noisy, low-compression scenarios for massive MIMO systems.

Contribution

The paper presents SemCSINet, a novel Transformer-based CSI feedback network that incorporates semantic CQI information and a joint coding scheme for enhanced performance.

Findings

Outperforms conventional CSI feedback methods in low SNR conditions.

Achieves higher reconstruction accuracy with low compression ratios.

Demonstrates robustness against noisy feedback channels.

Abstract

Massive multiple-input multiple-output (MIMO) technology is a key enabler of modern wireless communication systems, which demand accurate downlink channel state information (CSI) for optimal performance. Although deep learning (DL) has shown great potential in improving CSI feedback, most existing approaches fail to exploit the semantic relationship between CSI and other related channel metrics. In this paper, we propose SemCSINet, a semantic-aware Transformer-based framework that incorporates Channel Quality Indicator (CQI) into the CSI feedback process. By embedding CQI information and leveraging a joint coding-modulation (JCM) scheme, SemCSINet enables efficient, digital-friendly CSI feedback under noisy feedback channels. Experimental results on DeepMIMO datasets show that SemCSINet significantly outperforms conventional methods, particularly in scenarios with low signal-to-noise ratio (SNR) and low compression ratios (CRs), highlighting the effectiveness of semantic embedding in enhancing CSI reconstruction accuracy and system robustness.