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

• URL: http://arxiv.org/abs/2608.17760v1
• Date: Tue, 18 Aug 2026 13:26:34 GMT
• Title: Learnware for CSI Feedback: Scene-specific Small Models Can Do Big
• Abstract summary: Intelligent channel state information (CSI) feedback is essential for realizing the high capacity and spectral efficiency goals of future 6G systems.<n>Existing deep learning solutions face a trade-off between model generalization and scenario-specific performance.<n>We introduce a model repository-based deployment framework in which a central AI data center maintains a catalog of scene-specific CSI models.
• Score: 118.52196588174786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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