Goal-Oriented Learning at the Edge: Graph Neural Networks Over-the-Air for Blockage Prediction

2026-03-13

AI Summary

This work addresses the challenge of balancing task accuracy and wireless resource efficiency in 6G edge intelligence scenarios, particularly for millimeter-wave line-of-sight blockage prediction, where conventional communication mechanisms suffer from scalability limitations and latency bottlenecks. The authors propose a task-oriented communication-learning framework that uniquely integrates over-the-air computation (AirComp) with spatio-temporal graph neural networks, leveraging the wireless channel itself as an analog aggregation layer to enable low-latency, high-semantic feature fusion. While preserving the expressive power of digital messaging, the approach significantly enhances scalability and incorporates lightweight transfer learning to mitigate domain shift. Experimental results demonstrate substantial reductions in communication overhead for mmWave blockage prediction, achieving performance on par with or superior to digital baselines, along with strong inductive generalization capabilities.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Sixth-generation (6G) wireless networks evolve from connecting devices to connecting intelligence. The focus turns to Goal-Oriented Communications, where the effectiveness of communication is assessed through task-level objectives over traditional throughput-centric metrics. As communication intertwines with learning at the edge, distributed inference over wireless networks faces a critical trade-off between task accuracy and efficient radio resource use. Traditional communication schemes (e.g., OFDMA) are not designed for this trade-off, often facing challenges related to scalability and latency. Therefore, we propose a novel goal-oriented framework that integrates over-the-air computation with spatio-temporal graph learning. Leveraging the wireless channel as an analog aggregation layer, the proposed framework enables low-latency message passing while efficiently aggregating semantically relevant features from distributed nodes. Theoretical analysis confirms that our analog architecture converges to the expressive power of digital message passing, while offering decisive scalability advantages. We assess the framework in proactive line-of-sight blockage prediction for millimeter-wave networks. Through high-fidelity ray-tracing simulations, the framework exhibits strong inductive generalization to unseen networks and adapts to domain shifts via lightweight transfer learning, matching or even outperforming digital baselines with significantly reduced communication overhead.

Problem

• Goal-Oriented Communications
• Edge Intelligence
• Blockage Prediction
• Over-the-Air Computation
• 6G Networks

Innovation

• Goal-Oriented Communications
• Over-the-Air Computation
• Graph Neural Networks
• Edge Intelligence
• Blockage Prediction