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.

翻译：第六代（6G）无线网络正从连接设备向连接智能演进。其焦点转向目标导向通信，即通信有效性通过任务层目标而非传统的以吞吐量为核心的指标来评估。随着通信与边缘学习的深度融合，无线网络上的分布式推理面临任务精度与无线资源使用效率之间的关键权衡。传统通信方案（如OFDMA）并非为此权衡而设计，常面临可扩展性和延迟方面的挑战。为此，我们提出一种新颖的目标导向框架，将空中计算与时空图学习相融合。该框架利用无线信道作为模拟聚合层，在高效聚合分布式节点语义相关特征的同时，实现低延迟的消息传递。理论分析证实，我们的模拟架构在表达能力上收敛于数字消息传递，同时提供显著的可扩展性优势。我们在毫米波网络的主动视距遮挡预测场景中评估该框架。通过高保真射线追踪仿真，该框架展现出对未见网络的强大归纳泛化能力，并能通过轻量级迁移学习适应域偏移，其性能匹配甚至超越数字基线，同时通信开销显著降低。