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.

提出了一种基于 Transformer 网络的 TSNet-SAC 方案，用于 6G 移动边缘计算机自动驾驶系统中的实时调度和多模数据处理，通过引入幻数算法来指导 TSNet 的训练，以及增强鲁棒性和解决算法缺陷的滑动增强组件（SAC），并设计了 Extender 组件来处理多尺度训练数据和提供网络可扩展性，从而在准确性和鲁棒性上优于现有网络，并实现了与启发式算法相比更优秀的调度决策延迟。