Hierarchical Wireless Foundation Model for Multi-Task Optimization
The increasing complexity of next-generation wireless networks has driven the integration of artificial intelligence (AI) into wireless communications. However, most existing studies focus on developing task-specific deep learning techniques for single scenarios, which limits their ability to generalize across diverse tasks, channel conditions, and system configurations. To address this generalization bottleneck, we propose a hierarchical wireless foundation model (WFM) for multi-task optimization. The proposed WFM couples an upstream foundation channel encoder (FCE) with a downstream foundation optimization decoder (FOD) via geometry-aware cross-attention. Specifically, the FCE extracts ...
| Authors | Yangjing Wang, Ouya Wang, Shenglong Zhou et al. |
|---|---|
| Primary category | eess.SP |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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