Resource Optimization for Semantic-Aware Networks with Task Offloading

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 multi-agent reinforcement learning algorithm (MAPPO) is proposed to coordinate the resource management for wireless communications and computation in a distributed and low computational complexity manner. Simulation results verify that the proposed MAPPO algorithm outperforms other reinforcement learning algorithms and fixed schemes in terms of task execution speed and the overall system QoE.