This paper addresses the problem of dual-technology scheduling in hybrid Internet-of-Things (IoT) networks that integrate Optical Wireless Communication (OWC) with Radio Frequency (RF). We first present an optimization formulation that jointly maximizes throughput and minimizes delivery-based Age of Information (AoI) between access points and IoT nodes under energy and link availability constraints. However, solving such NP-hard problems at scale is computationally intractable and typically assumes full channel observability, which is impractical in real deployments. To address this challenge, we propose the Dual-Graph Embedding with Transformer (DGET) framework, a supervised multi-task learning architecture that combines a two-stage Graph Neural Network (GNN) with a Transformer encoder. The first stage employs a transductive GNN to encode the known graph topology together with initial node and link states, such as energy levels, available links, and queued transmissions. The second stage introduces an inductive GNN for temporal refinement, enabling the model to generalize these embeddings to evolving network states while capturing variations in energy and queue dynamics over time through a consistency loss. The resulting embeddings are then processed by a Transformer-based classifier that models cross-link dependencies using multi-head self-attention. Simulation results show that hybrid RF-OWC networks outperform standalone RF systems by supporting higher traffic loads and reducing AoI by up to 20% while maintaining comparable energy consumption. Compared with optimization-based methods, the proposed DGET framework achieves near-optimal scheduling with over 90% classification accuracy, lower computational complexity, and improved robustness under partial channel observability.

翻译：本文研究了集成光无线通信与射频通信的混合物联网网络中的双技术调度问题。我们首先提出一个优化模型，该模型在能量和链路可用性约束下，联合最大化接入点与物联网节点之间的吞吐量并最小化基于信息传递的信息年龄。然而，大规模求解此类NP难问题在计算上是不可行的，并且通常假设完全的信道可观测性，这在实际部署中并不现实。为应对这一挑战，我们提出了双图嵌入Transformer框架，这是一种结合两阶段图神经网络与Transformer编码器的监督式多任务学习架构。第一阶段采用转导式图神经网络，对已知的图拓扑结构以及初始节点与链路状态进行编码，例如能量水平、可用链路和排队传输。第二阶段引入归纳式图神经网络进行时序精化，使模型能够将这些嵌入泛化到演变的网络状态，同时通过一致性损失捕获能量与队列动态随时间的变化。生成的嵌入随后由基于Transformer的分类器处理，该分类器利用多头自注意力对跨链路依赖性进行建模。仿真结果表明，混合射频-光无线网络在保持可比能量消耗的同时，通过支持更高的流量负载并将信息年龄降低高达20%，优于独立的射频系统。与基于优化的方法相比，所提出的双图嵌入Transformer框架实现了接近最优的调度，分类准确率超过90%，计算复杂度更低，并在部分信道可观测性下具有更强的鲁棒性。