With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.

翻译：随着边缘计算的普及，边缘设备上的高效AI推理已成为自动驾驶、VR/AR等智能应用的关键。针对这一问题，我们通过优化移动设备与边缘服务器之间的特征传输，研究了高效远程目标识别的解决方案。我们提出了一种在线优化框架，以应对端到端通信系统中动态信道条件与设备移动性的挑战。该方法在现有技术基础上，利用语义知识库驱动多层次特征传输，并充分考虑传输过程中的时间因素与动态要素。为解决在线优化问题，我们设计了一种基于软演员-评论家的深度强化学习系统，通过精心设计的奖励函数实现实时决策，克服了NP-hard问题的优化难度，在满足延迟约束的同时最小化语义损失。数值结果表明，在不同系统配置下，该方法相比传统贪心策略具有更优性能。