ICWLM: A Multi-Task Wireless Large Model via In-Context Learning

TL;DR

ICWLM introduces a multi-task wireless foundation model utilizing in-context learning, enabling adaptive, generalizable physical layer solutions directly trained on large wireless datasets, reducing retraining needs and improving network performance.

Contribution

The paper presents ICWLM, a novel wireless-native foundation model trained from scratch with in-context learning for multi-task physical layer problems, enhancing adaptability and generalization in wireless networks.

Findings

ICWLM achieves competitive performance on multiple physical layer tasks.

ICWLM demonstrates strong generalization to unseen system configurations.

ICWLM reduces the need for extensive retraining in dynamic wireless environments.

Abstract

The rapid evolution of wireless communication technologies, particularly massive multiple-input multiple-output (mMIMO) and millimeter-wave (mmWave), introduces significant network complexity and computational demands. Significant research efforts have been made to improve physical layer performance by resorting to deep learning (DL) methods, which, however, are usually task-specific and struggle with data scarcity and generalization. To address these challenges, we propose a novel In-Context Wireless Large Model (ICWLM), a wireless-native foundation model designed for simultaneous multi-task learning at the physical layer. Unlike conventional methods that adapt wireless data to pre-trained large language models (LLMs), ICWLM is trained directly on large-scale, mixed wireless datasets from scratch. It jointly solves multiple classical physical layer problems, including multi-user precoding (sum-rate maximization and max-min SINR) and channel prediction. A key innovation of ICWLM is its utilization of in-context learning (ICL), enabling the model to adapt to varying system configurations and channel conditions with minimal demonstration pairs, eliminating the need for extensive retraining. Extensive simulation results demonstrate that ICWLM achieves competitive performance compared to task-specific methods while exhibiting remarkable generalization capabilities to unseen system configurations. This work offers a promising paradigm for developing unified and adaptive AI models for future wireless networks, potentially reducing deployment complexity and enhancing intelligent resource management.