Learning for Semantic Knowledge Base-Guided Online Feature Transmission
  in Dynamic Channels
Xiangyu Gao Yaping Sun Dongyu Wei Xiaodong Xu Hao Chen Hao Yin Shuguang Cui
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.