Recent advancements in foundation models (FMs) have attracted increasing attention in the wireless communication domain. Leveraging the powerful multi-task learning capability, FMs hold the promise of unifying multiple tasks of wireless communication with a single framework. Nevertheless, existing wireless FMs face limitations in the uniformity to address multiple tasks with diverse inputs/outputs across different communication scenarios. In this paper, we propose a MUlti-taSk Environment-aware FM (MUSE-FM) with a unified architecture to handle multiple tasks in wireless communications, while effectively incorporating scenario information. Specifically, to achieve task uniformity, we propose a unified prompt-guided data encoder-decoder pair to handle data with heterogeneous formats and distributions across different tasks. Besides, we integrate the environmental context as a multi-modal input, which serves as prior knowledge of environment and channel distributions and facilitates cross-scenario feature extraction. Simulation results illustrate that the proposed MUSE-FM outperforms existing methods for various tasks, and its prompt-guided encoder-decoder pair facilitates few-shot adaptation to new task configurations. Moreover, the incorporation of environment information improves the ability to adapt to different scenarios.

翻译：近年来，基础模型在无线通信领域的发展日益受到关注。凭借其强大的多任务学习能力，基础模型有望通过单一框架统一处理无线通信中的多项任务。然而，现有的无线基础模型在处理不同通信场景下输入/输出形式各异的多种任务时，其统一性仍存在局限。本文提出一种具有统一架构的多任务环境感知基础模型，能够在有效融入场景信息的同时，处理无线通信中的多种任务。具体而言，为实现任务统一性，我们设计了一种基于提示引导的统一数据编码器-解码器对，以处理不同任务间格式与分布异构的数据。此外，我们将环境上下文作为多模态输入进行融合，其作为环境与信道分布的先验知识，有助于跨场景特征提取。仿真结果表明，所提出的MUSE-FM在多项任务上优于现有方法，且其基于提示引导的编码器-解码器对能够有效支持对新任务配置的少样本适应。此外，环境信息的融入提升了模型适应不同场景的能力。