Wi-Fi 7 introduces the restricted target wake time (RTWT) mechanism, which is vital for Industrial IoT (IIoT) applications requiring periodic, reliable, and low-latency communication. RTWT enables deterministic channel access by assigning scheduled transmission slots to stations (STAs), minimizing contention and interference. However, determining efficient RTWT slot assignments remains challenging in dense networks, where conventional interference graph-based models lack flexibility and scalability. To overcome this, we propose a scalable interference graph learning (IGL) framework that learns optimal interference graph representations for graph coloring-based RTWT scheduling. The IGL leverages an evolution strategy (ES) to train a neural network (NN) using a single network-wide reward, avoiding costly edge-wise feedback. Furthermore, a deep hashing function (DHF) groups interfering STAs, limiting training and inference to relevant subsets and greatly reducing complexity. Simulation results demonstrate that the proposed IGL improves slot efficiency by up to 25\%, reduces packet losses by up to 30\% in dynamic environments. Thanks to DHF, it also reduces the training and inference time of IGL by 4 and 8 times, respectively, and the online slot assignment time by 3 times in large networks.

翻译：Wi-Fi 7引入了受限目标唤醒时间（RTWT）机制，该机制对于需要周期性、可靠且低延迟通信的工业物联网（IIoT）应用至关重要。RTWT通过为站点（STA）分配预定的传输时隙来实现确定性信道接入，从而最小化竞争与干扰。然而，在密集网络中，确定高效的RTWT时隙分配仍然具有挑战性，因为传统的基于干扰图的模型缺乏灵活性和可扩展性。为克服此问题，我们提出了一种可扩展的干扰图学习（IGL）框架，该框架学习最优的干扰图表征，用于基于图着色的RTWT调度。IGL利用进化策略（ES）训练一个神经网络（NN），仅使用单一的网络范围奖励，避免了代价高昂的逐边反馈。此外，一个深度哈希函数（DHF）对相互干扰的STA进行分组，将训练和推理限制在相关子集内，从而极大地降低了复杂度。仿真结果表明，所提出的IGL在动态环境中将时隙效率提升高达25%，将数据包丢失减少高达30%。得益于DHF，它还将IGL的训练和推理时间分别减少了4倍和8倍，并在大型网络中将在线时隙分配时间减少了3倍。