Task offloading and resource allocation in NOMA-VEC: A multi-agent deep graph reinforcement learning algorithm

Vehicular edge computing (VEC) is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle (IoV). Non-orthogonal multiple access (NOMA) has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost. It...

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Vydáno v:China communications Ročník 21; číslo 8; s. 79 - 88
Hlavní autoři: Yonghui, Hu, Zuodong, Jin, Peng, Qi, Dan, Tao
Médium: Journal Article
Jazyk:angličtina
Vydáno: China Institute of Communications 01.08.2024
School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China
Témata:
Costs
edge computing
Feature extraction
graph convolutional network
Interference
NOMA
Optimization
reinforcement learning
Resource management
Servers
task offloading
graph convolutional net-work
task offloading
edge computing
reinforcement learning
ISSN:1673-5447
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Shrnutí:Vehicular edge computing (VEC) is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle (IoV). Non-orthogonal multiple access (NOMA) has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost. It is an encouraging progress combining VEC and NOMA. In this paper, we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system. To solve the optimization problem, we propose a multiagent deep graph reinforcement learning algorithm. The algorithm extracts the topological features and relationship information between agents from the system state as observations, outputs task offloading decision and resource allocation simultaneously with local policy network, which is updated by a local learner. Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80% improvement compared with the benchmark algorithms in system service utility.
ISSN:1673-5447
DOI:10.23919/JCC.fa.2024-0021.202408