Joint task offloading and resource optimization in NOMA-based vehicular edge computing: A game-theoretic DRL approach
Vehicular edge computing (VEC) becomes a promising paradigm for the development of emerging intelligent transportation systems. Nevertheless, the limited resources and massive transmission demands bring great challenges on implementing vehicular applications with stringent deadline requirements. Thi...
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| Published in | Journal of systems architecture Vol. 134; p. 102780 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.01.2023
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| Abstract | Vehicular edge computing (VEC) becomes a promising paradigm for the development of emerging intelligent transportation systems. Nevertheless, the limited resources and massive transmission demands bring great challenges on implementing vehicular applications with stringent deadline requirements. This work presents a non-orthogonal multiple access (NOMA) based architecture in VEC, where heterogeneous edge nodes are cooperated for real-time task processing. We derive a vehicle-to-infrastructure (V2I) transmission model by considering both intra-edge and inter-edge interferences and formulate a cooperative resource optimization (CRO) problem by jointly optimizing the task offloading and resource allocation, aiming at maximizing the service ratio. Further, we decompose the CRO into two subproblems, namely, task offloading and resource allocation. In particular, the task offloading subproblem is modeled as an exact potential game (EPG), and a multi-agent distributed distributional deep deterministic policy gradient (MAD4PG) is proposed to achieve the Nash equilibrium. The resource allocation subproblem is divided into two independent convex optimization problems, and an optimal solution is proposed by using a gradient-based iterative method and KKT condition. Finally, we build the simulation model based on real-world vehicular trajectories and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solutions. |
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| AbstractList | Vehicular edge computing (VEC) becomes a promising paradigm for the development of emerging intelligent transportation systems. Nevertheless, the limited resources and massive transmission demands bring great challenges on implementing vehicular applications with stringent deadline requirements. This work presents a non-orthogonal multiple access (NOMA) based architecture in VEC, where heterogeneous edge nodes are cooperated for real-time task processing. We derive a vehicle-to-infrastructure (V2I) transmission model by considering both intra-edge and inter-edge interferences and formulate a cooperative resource optimization (CRO) problem by jointly optimizing the task offloading and resource allocation, aiming at maximizing the service ratio. Further, we decompose the CRO into two subproblems, namely, task offloading and resource allocation. In particular, the task offloading subproblem is modeled as an exact potential game (EPG), and a multi-agent distributed distributional deep deterministic policy gradient (MAD4PG) is proposed to achieve the Nash equilibrium. The resource allocation subproblem is divided into two independent convex optimization problems, and an optimal solution is proposed by using a gradient-based iterative method and KKT condition. Finally, we build the simulation model based on real-world vehicular trajectories and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solutions. |
| ArticleNumber | 102780 |
| Author | Ren, Hualing Guo, Songtao Xu, Xincao Jin, Feiyu Dai, Penglin Liu, Kai Zhan, Choujun |
| Author_xml | – sequence: 1 givenname: Xincao orcidid: 0000-0001-5057-5468 surname: Xu fullname: Xu, Xincao email: near@cqu.edu.cn organization: College of Computer Science, Chongqing University, Chongqing, China – sequence: 2 givenname: Kai surname: Liu fullname: Liu, Kai email: liukai0807@cqu.edu.cn organization: College of Computer Science, Chongqing University, Chongqing, China – sequence: 3 givenname: Penglin surname: Dai fullname: Dai, Penglin email: penglindai@swjtu.edu.cn organization: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China – sequence: 4 givenname: Feiyu surname: Jin fullname: Jin, Feiyu email: fyjin@cqu.edu.cn organization: College of Computer Science, Chongqing University, Chongqing, China – sequence: 5 givenname: Hualing surname: Ren fullname: Ren, Hualing email: renharlin@cqu.edu.cn organization: College of Computer Science, Chongqing University, Chongqing, China – sequence: 6 givenname: Choujun surname: Zhan fullname: Zhan, Choujun email: zchoujun2-c@my.cityu.edu.hk organization: School of Computing, South China Normal University, Guangzhou, China – sequence: 7 givenname: Songtao surname: Guo fullname: Guo, Songtao email: guosongtao@cqu.edu.cn organization: College of Computer Science, Chongqing University, Chongqing, China |
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| Keywords | Vehicular edge computing Deep reinforcement learning Real-time task offloading Heterogeneous resource allocation Exact potential game |
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| SubjectTerms | Deep reinforcement learning Exact potential game Heterogeneous resource allocation Real-time task offloading Vehicular edge computing |
| Title | Joint task offloading and resource optimization in NOMA-based vehicular edge computing: A game-theoretic DRL approach |
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