Research Article
Deep Reinforcement Learning-Based Multi-node Collaborative Task Offloading Optimization in 6G Space-Air-Ground Integrated Networks
@INPROCEEDINGS{10.1007/978-3-031-63989-0_16, author={Wang Li and Xin Chen and Libo Jiao and Wangzhong Ning and Wenwu Zhu}, title={Deep Reinforcement Learning-Based Multi-node Collaborative Task Offloading Optimization in 6G Space-Air-Ground Integrated Networks}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking and Services. 20th EAI International Conference, MobiQuitous 2023, Melbourne, VIC, Australia, November 14--17, 2023, Proceedings, Part I}, proceedings_a={MOBIQUITOUS}, year={2024}, month={7}, keywords={6G Space-Air-Ground Networks Multi-access Edge Computing Task Offloading Deep reinforcement learning}, doi={10.1007/978-3-031-63989-0_16} }- Wang Li
Xin Chen
Libo Jiao
Wangzhong Ning
Wenwu Zhu
Year: 2024
Deep Reinforcement Learning-Based Multi-node Collaborative Task Offloading Optimization in 6G Space-Air-Ground Integrated Networks
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-031-63989-0_16
Abstract
With the explosion of communication data volume, 6G communication has gradually entered the vision of academia and industry. In addition, in order to support the execution of computationally intensive applications, the study of 6G space-air-ground integration network (SAGIN) is becoming more and more extensive, in which satellites, drones, and base stations can provide arithmetic support for mobile users (MUs) through multi-access edge computing (MEC). However, the variety of offloading modes, the mobility of nodes and the stochastic state of wireless networks make that selecting the optimal base station and allocating the appropriate computational resources become more challenging. In this paper, we first describe the offloading process and establish the SAGIN model. Then the Deep Reinforcement Learning-based Task Offloading Optimization Algorithm (DTOOA) is proposed to jointly optimize the problem of minimizing latency and energy consumption. The numerical results show that the DTOOA scheme significantly outperforms benchmark schemes and markedly improves the quality of experience (QoE) of MUs.
