A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models
arXiv:2606.10277v1 Announce Type: new Abstract: Though wireless foundation models (WFMs) have shown strong potential in learning universal channel representations, their adaptation to various downstream tasks remains constrained by existing paradigms. Fine-tuning strategies introduces substantial computational and storage overhead, while frozen feature extraction leads to sub-optimal performance across diverse downstream tasks. To address this issue, we propose a unified adaptive feature composition framework for multitask generalization in WFMs, where the key component is the Routing Adapter
The rapid development and application of wireless foundation models necessitate efficient adaptation strategies to overcome the limitations of current fine-tuning and frozen feature extraction methods.
This framework offers a path to more computationally efficient and performant multi-task generalization for wireless AI, enhancing the utility and scalability of complex communication systems.
The proposed 'Routing Adapter' framework allows WFMs to adapt to diverse tasks without extensive computational overhead, enabling more flexible and robust AI deployment in wireless networks.
- · Telecommunication companies
- · AI model developers
- · Edge computing providers
- · Wireless network operators
- · Providers of inefficient fine-tuning solutions
- · Developers reliant on ad-hoc adaptation methods
Wireless foundation models become more versatile and efficient in adapting to various communication tasks.
This efficiency gain could accelerate the deployment of advanced AI applications in 5G and 6G networks, enabling new services.
Improved multi-task generalization for WFMs might lead to more resilient and intelligent autonomous communication systems, reducing human intervention.
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Read at arXiv cs.LG