TSNet-SAC：利用 Transformer 实现高效任务调度
TSNet-SAC: Leveraging Transformers for Efficient Task Scheduling
AI总结：
面向 6G MEC 实时多模态任务调度，提出基于 Transformer 的 TSNet-SAC，用启发式算法引导训练，并通过 SAC 与 Extender 提升鲁棒性、多尺度适应和可扩展性。
摘要：
在未来的 6G 移动边缘计算（MEC）中，自动驾驶系统需要具备处理具有强相互依赖关系的多模态数据的能力。然而，传统启发式算法由于需要多次迭代才能得出最优方案，无法满足实时调度需求。我们提出一种基于 Transformer 的新型 TSNet-SAC，它仅利用启发式算法来指导 TSNet 的训练。此外，引入滑动增强组件（Sliding Augment Component，SAC）以增强鲁棒性并解决算法缺陷。进一步地，设计 Extender 组件来处理多尺度训练数据并提供网络可扩展性，使 TSNet 能够适应不同的接入场景。仿真表明，TSNet-SAC 在准确性和鲁棒性方面优于现有网络，并相较启发式算法实现了更优的调度决策延迟。
英文摘要：
In future 6G Mobile Edge Computing (MEC), autopilot systems require the capability of processing multimodal data with strong interdependencies. However, traditional heuristic algorithms are inadequate for real-time scheduling due to their requirement for multiple iterations to derive the optimal scheme. We propose a novel TSNet-SAC based on Transformer, that utilizes heuristic algorithms solely to guide the training of TSNet. Additionally, a Sliding Augment Component (SAC) is introduced to enhance the robustness and resolve algorithm defects. Furthermore, the Extender component is designed to handle multi-scale training data and provide network scalability, enabling TSNet to adapt to different access scenarios. Simulation demonstrates that TSNet-SAC outperforms existing networks in accuracy and robustness, achieving superior scheduling-making latency compared to heuristic algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
