TCLNet: A Hybrid Transformer-CNN Framework Leveraging Language Models as Lossless Compressors for CSI Feedback

TL;DR

TCLNet introduces a hybrid Transformer-CNN framework combined with language models to improve CSI feedback compression in massive MIMO systems, achieving higher accuracy and efficiency by capturing local and global features.

Contribution

The paper presents a novel hybrid Transformer-CNN architecture for lossy CSI compression and utilizes large language models as zero-shot lossless compressors, enhancing overall compression performance.

Findings

Outperforms existing methods in reconstruction accuracy.

Achieves up to 5 dB performance gain in diverse scenarios.

Leverages LLMs as zero-shot lossless compressors with prompt design.

Abstract

In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) plays a crucial role in achieving high spectrum and energy efficiency. However, the CSI feedback overhead becomes a major bottleneck as the number of antennas increases. Although existing deep learning-based CSI compression methods have shown great potential, they still face limitations in capturing both local and global features of CSI, thereby limiting achievable compression efficiency. To address these issues, we propose TCLNet, a unified CSI compression framework that integrates a hybrid Transformer-CNN architecture for lossy compression with a hybrid language model (LM) and factorized model (FM) design for lossless compression. The lossy module jointly exploits local features and global context, while the lossless module adaptively switches between context-aware coding and parallel coding to optimize the rate-distortion-complexity (RDC) trade-off. Extensive experiments on both real-world and simulated datasets demonstrate that the proposed TCLNet outperforms existing approaches in terms of reconstruction accuracy and transmission efficiency, achieving up to a 5 dB performance gain across diverse scenarios. Moreover, we show that large language models (LLMs) can be leveraged as zero-shot CSI lossless compressors via carefully designed prompts.