Hierarchical Wireless Foundation Model for Multi-Task Optimization
Machine Learning
Summary
The authors created a new AI system called a wireless foundation model (WFM) to improve how wireless networks work across many different tasks and settings. Their model uses two parts: one to understand the wireless signals and another to make smart decisions for optimizing the network. They trained it with a mix of teaching methods to perform better than usual, and it can quickly adapt to new tasks without much extra work. Tests showed the model works well, is faster than traditional methods, and handles new environments and system changes reliably.
Authors
Yangjing Wang, Ouya Wang, Shenglong Zhou, Geoffrey Ye Li
Abstract
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 task-agnostic channel representations via self-supervised masked reconstruction while the FOD generates multi-task optimization decisions through differentiable output heads. Moreover, a hybrid supervised-to-unsupervised training strategy is employed to overcome the performance ceiling of purely supervised learning, and the modular architecture of the WFM enables efficient adaptation to unseen communication tasks with minimal parameter overhead. Simulation results show that the proposed WFM learns high-fidelity channel representations and achieves competitive multi-task optimization performance while substantially reducing optimization inference latency relative to numerical baselines. Furthermore, it exhibits robust generalization to unseen propagation environments, varying constraint parameters, and heterogeneous system configurations.