6G WavesFM: A Foundation Model for Sensing, Communication, and Localization

In the past decade, breakthroughs in deep learning (DL) have sparked interest in AI-native 6G networks. While extensive research has focused on developing DL models for wireless network functions, most efforts have focused on highly specialized models that have limited ability to perform effectively in real-world scenarios and generalize to out-of-distribution data. Furthermore, they also lack the ability to perform multiple tasks. The notion of Wireless Foundation Models (WFM) has recently emerged as a promising solution to this challenge. This paper introduces WavesFM, a novel WFM, capable of supporting communication, sensing, and localization tasks. The model processes image-like wireless modalities, such as spectrograms, channel state information (CSI), and in-phase and quadrature (IQ) signals arranged as orthogonal frequency-division multiplexing (OFDM) resource grids. We demonstrate the strong generalization capabilities of WavesFM through extensive experiments on four downstream tasks: Fifth Generation New Radio (5G NR) positioning; multiple-input multiple-output OFDM (MIMO-OFDM) channel estimation; human activity sensing; and radio-frequency (RF) signal classification. Compared to supervised baselines trained individually, our approach achieves superior performance while sharing 80% of its parameters across tasks. Furthermore, we show that pretraining on domain-relevant data not only boosts performance but also accelerates convergence, reducing training time by up to 5×. Additionally, we incorporate Low-Rank Adaptation (LoRA) fine-tuning, which enables full parameter sharing across tasks, significantly reducing memory overhead without compromising performance. These results demonstrate that our unified WFM can support diverse tasks and deliver significant gains in both performance and efficiency, highlighting the transformative potential of WFMs to drive AI-native paradigms in future sixth-generation (6G) networks.