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 a comprehensive suite of fundamental PHY actions, demonstrating strong generalization to unseen scenarios.
From: Haotian Zhang [view email]
[v1]
Wed, 14 Jan 2026 05:26:25 UTC (1,722 KB)
[v2]
Fri, 30 Jan 2026 05:11:48 UTC (1,810 KB)
[v3]
Sat, 25 Jul 2026 08:01:29 UTC (8,911 KB)
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