MUSE-FM: Multi-task Environment-aware Foundation Model for Wireless Communications

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.