Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels

• URL: http://arxiv.org/abs/2311.18316v1
• Date: Thu, 30 Nov 2023 07:35:56 GMT
• Title: Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels
• Authors: Xiangyu Gao, Yaping Sun, Dongyu Wei, Xiaodong Xu, Hao Chen, Hao Yin, Shuguang Cui
• Abstract summary: 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. 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.
• Score: 41.59960455142914
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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