Learnware for CSI Feedback: Scene-specific Small Models Can Do Big

2026-08-18

AI Summary

为解决CSI反馈中模型泛化与特定场景性能之间的权衡问题，提出基于模型库和Learnware框架的方法，通过语义和统计规格匹配预训练模型，提高效率和隐私保护。

Abstract

Intelligent channel state information (CSI) feedback is essential for realizing the high capacity and spectral efficiency goals of future 6G systems, yet existing deep learning solutions face a trade-off between model generalization and scenario-specific performance. Large neural networks generalize well but incur high computational and tuning costs, while small models excel in particular environments but require repetitive costly end-to-end training for each base station (BS). To address these challenges, we introduce a model repository-based deployment framework in which a centralized AI data center maintains a catalog of scene-specific CSI models. The repository is enhanced with a Learnware-based framework, where each model is associated with a specification including semantic part (network architecture parameters) and statistical part (codeboo-fingerprint embeddings of training-data distributions). A BS submits only its local statistical specifications to retrieve the most relevant pre-trained model, enhancing data privacy by avoiding raw CSI transmission and drastically reducing retrieval latency and communication overhead. We further develop a data-driven search strategy that matches codebook fingerprints to model performance, achieving over 90% selection accuracy. In simulations, our scheme yields 18.8% and 57.7% performance improvements over the General Model in LOS and NLOS scenarios, respectively while reducing local fine-tuning by up to 1000 samples and 100 epochs. This Learnware-based approach minimizes redundant training, maximizes model reuse, and supports rapid,privacy-enhancing deployment of CSI feedback models.

Problem

• CSI feedback
• model generalization
• scenario-specific performance
• deep learning solutions
• computational cost

Innovation

• Learnware-based framework
• scene-specific CSI models
• data privacy enhancement
• reduced retrieval latency
• model reuse