TSNet-SAC: Leveraging Transformers for Efficient Task Scheduling
This addresses efficient task scheduling for autopilot systems in 6G MEC, representing an incremental improvement over heuristic algorithms.
The paper tackles the problem of real-time task scheduling in 6G Mobile Edge Computing for autopilot systems, proposing TSNet-SAC based on Transformers, which outperforms existing methods in accuracy and robustness with reduced scheduling latency.
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.