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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| Veröffentlicht in: | China communications Jg. 21; H. 8; S. 79 - 88 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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China Institute of Communications
01.08.2024
School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China |
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| ISSN: | 1673-5447 |
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| Abstract | 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. |
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| AbstractList | 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. 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 ac-cess(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 maxi-mize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multi-agent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and re-lationship information between agents from the sys-tem state as observations,outputs task offloading de-cision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the pro-posed method achieves a 1.52％～5.80％improvement compared with the benchmark algorithms in system service utility. |
| Author | Dan, Tao Yonghui, Hu Peng, Qi Zuodong, Jin |
| AuthorAffiliation | School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China |
| AuthorAffiliation_xml | – name: School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China |
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| Keywords | graph convolutional net-work task offloading edge computing reinforcement learning |
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| SubjectTerms | Costs edge computing Feature extraction graph convolutional network Interference NOMA Optimization reinforcement learning Resource management Servers task offloading |
| Title | Task offloading and resource allocation in NOMA-VEC: A multi-agent deep graph reinforcement learning algorithm |
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