Resource Optimization for Semantic-Aware Networks with Task Offloading

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.

arXiv commentSubmitted to TWC, in major revision

References in corpus (6)

• High-Dimensional Continuous Control Using Generalized Advantage Estimation
• Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing
• Resource allocation for text semantic communications
• Semantic Communications: Principles and Challenges
• Meta Federated Reinforcement Learning for Distributed Resource Allocation
• A Unified Multi-Task Semantic Communication System for Multimodal Data

Cited by in corpus (1)

• Computational Offloading in Semantic-Aware Cloud-Edge-End Collaborative Networks