WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model

TL;DR

WiFo-M$^2$ is a foundation model that leverages environment sensing to enhance wireless PHY actions, offering universal improvements and strong generalization across different scenarios.

Contribution

This work introduces WiFo-M$^2$, a foundation model with a novel contrastive pre-training strategy for environment sensing in wireless networks, enabling plug-and-play integration and broad applicability.

Findings

Improves diverse PHY actions performance

Demonstrates strong generalization to unseen scenarios

Uses contrastive pre-training for robust feature extraction

Abstract

The emerging convergence of next-generation wireless networks and agentic artificial intelligence (AI) is inspiring a new vision: embodied intelligent network entities utilize environmental sensing to refine their physical-layer (PHY) actions. Despite a growing body of preliminary work, prevailing small and task-specific AI models require extensive manual design of data pre-processing, network architecture, and fine-tuning, leaving them tightly coupled to particular PHY actions, system configurations, and deployment scenarios. To address this, we propose a paradigm shift with WiFo-M$^2$, a foundation model that enables environment sensing to be easily integrated into PHY actions, delivering universal performance gains. To extract generalizable out-of-band (OOB) channel-aware features from environment sensing, we introduce ContraSoM, a contrastive pre-training strategy. Once pre-trained, WiFo-M$^2$ infers future OOB channel-aware features from historical sensory data and strengthens feature robustness via modality-specific data augmentation. Experiments show that WiFo-M$^2$ improves the performance of diverse PHY actions and demonstrates strong generalization to unseen scenarios.