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.

边缘计算下的 AI 推理在边缘设备上高效进行已成为智能应用（如自动驾驶和虚拟现实 / 增强现实）所必需的。我们针对移动设备和边缘服务器之间特征传输的优化问题，提出了一种在线优化框架。通过利用语义知识库来驱动多级特征传输，考虑传输过程中的时间因素和动态元素，我们的方法基于现有方法构建，解决了动态信道条件和设备移动的挑战。为了解决在线优化问题，我们设计了一种基于软演员 - 评论家的深度强化学习系统，并设计了一个精心设计的奖励函数来进行实时决策，克服了 NP-hard 问题的优化困难，并在尊重延迟限制的同时实现了语义损失的最小化。数值结果显示，相较于各种系统设置下的传统贪婪方法，我们的方法具有优势。