arXiv:2605.31279v1 Announce Type: cross Abstract: To make cross-band channel prediction practical for AI-native RAN, algorithms must generalize across diverse environments and support real-time inference. Existing approaches achieve one but not both. To bridge this gap, we introduce GUIDE, a physics-guided deep unfolding framework that embeds wireless channel physics into differentiable layers. Without retraining in unseen environments, GUIDE achieves 2.75x beamforming gain than the deep learning-based baseline FIRE with only a slight increase in inference time, and 1.39x beamforming gain than
The increasing sophistication of AI and the pressing need for efficient 5G/6G network utilization make practical, real-time channel prediction a critical bottleneck. This research addresses a key requirement for AI-native RAN development.
This breakthrough improves the efficiency and performance of AI-native radio access networks, potentially accelerating the deployment and capabilities of AI-driven wireless communication systems. It offers a solution that balances generalization with real-time demands, crucial for distributed intelligence.
Current limitations in real-time cross-band channel prediction are being addressed, enabling AI-RAN systems to adapt more effectively to diverse environments without constant retraining. This will enhance network performance and responsiveness.
- · Telecommunications infrastructure providers
- · AI hardware manufacturers
- · 5G/6G network operators
- · AI research institutions
- · Legacy network optimization techniques
- · Companies relying on less efficient AI-RAN approaches
Improved beamforming gain and network efficiency in AI-native RAN environments.
Faster and more reliable AI-driven wireless communication leading to enhanced connectivity for various applications.
Accelerated development and adoption of advanced AI-powered autonomous systems and IoT by removing communication bottlenecks.
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Read at arXiv cs.AI