Resource Optimization for Semantic-Aware Networks with Task Offloading
Zelin Ji, Zhijin Qin, Xiaoming Tao, Han Zhu

TL;DR
This paper introduces a semantic-aware multi-modal task offloading system for edge computing, utilizing reinforcement learning to optimize resource management, thereby reducing latency and improving overall quality of experience.
Contribution
It proposes a novel semantic-aware offloading framework combined with a MAPPO reinforcement learning algorithm for efficient resource coordination.
Findings
MAPPO outperforms other RL algorithms in task speed
System achieves higher QoE with semantic-aware offloading
Reduces latency in wireless communication for edge tasks
Abstract
The limited capabilities of user equipment restrict the local implementation of computation-intensive applications. Edge computing, especially the edge intelligence system, enables local users to offload the computation tasks to the edge servers to reduce the computational energy consumption of user equipment and accelerate fast task execution. However, the limited bandwidth of upstream channels may increase the task transmission latency and affect the computation offloading performance. To overcome the challenge arising from scarce wireless communication resources, we propose a semantic-aware multi-modal task offloading system that facilitates the extraction and offloading of semantic task information to edge servers. To cope with the different tasks with multi-modal data, a unified quality of experience (QoE) criterion is designed. Furthermore, a proximal policy optimization-based…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Visual Attention and Saliency Detection
