Design Self-Supervised Pre-Training Strategies for Wireless Foundation Models Across Diverse Downstream Tasks

Develop self-supervised pre-training objectives and procedures—such as next-sample prediction, masking, and denoising—for a physical-layer wireless foundation model that can effectively support multiple downstream wireless tasks with heterogeneous data representations, enabling efficient fine-tuning with minimal additional data across varied telecom applications.

Background

The white paper proposes building a wireless physical-layer foundation model using self-supervised training to reduce reliance on large labeled datasets and to facilitate adaptation to new tasks. However, telecom downstream tasks vary widely in data representation and objectives, making pre-training design challenging.

While self-supervised tasks like next-sample prediction, masking, and denoising are suggested, the paper explicitly acknowledges that determining effective pre-training strategies that generalize across multiple wireless tasks with different data formats remains unresolved and requires targeted research.