Large and Small Model Collaboration for Air Interface

TL;DR

This paper proposes a collaborative framework combining large AI models and small models for efficient, environment-specific adaptation in wireless communication tasks, notably CSI feedback, reducing training costs and improving performance.

Contribution

It introduces LASCO and E-LASCO frameworks that enable environment-specific adaptation by leveraging small models as plugins, avoiding costly fine-tuning of large models.

Findings

LASCO improves CSI feedback accuracy with less training data.

E-LASCO dynamically balances contributions of large and small models.

Framework achieves faster adaptation and reduced training costs.

Abstract

Large artificial intelligence models (LAMs) have shown strong capability in wireless communications, yet existing works mainly rely on their generalized knowledge across environments while overlooking the potential gains of environment-specific adaptation. Directly fine-tuning LAMs for adaptation is often impractical due to prohibitive training costs, low inference efficiency in multi-user scenarios, and the risk of catastrophic forgetting, in addition to the limited accessibility of model parameters. To address these limitations, we establish a collaborative framework for air interface. In this framework, unlike prior approaches that either depend solely on LAMs or require direct fine-tuning, LAMs are exploited as a universal channel knowledge base while small artificial intelligence models (SAMs) are employed as lightweight plugins to capture environment-specific knowledge, facilitating efficient environment-specific adaptation of LAMs. Subsequently, we instantiate this framework for CSI feedback tasks, and develop a large and small collaboration framework for CSI feedback, referred to as LASCO. LASCO operates by letting the base LAM produce an initial CSI reconstruction, learning the environment-induced reconstruction shift through a reference SAM and a proxy SAM, and transferring this shift back to the LAM. To further enhance adaptability, we introduce elastic-LASCO (E-LASCO), which augments LASCO with learnable collaboration coefficients that control the contribution of LAMs and SAMs across different environments. Numerical results demonstrate that LASCO and E-LASCO enables LAMs to achieve environment-specific performance gains with significantly reduced training costs, lower data collection requirements, and faster adaptation speed.