Energy efficient multi-user task offloading through active RIS with hybrid TDMA-NOMA transmission
In this paper, we address the challenge of minimizing system energy consumption for task offloading within non-line-of-sight (NLoS) mobile edge computing (MEC) environments. Our approach integrates an active reconfigurable intelligent surface (RIS) and employs a hybrid transmission scheme combining...
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| Published in | Journal of network and computer applications Vol. 232; p. 104005 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.12.2024
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| Abstract | In this paper, we address the challenge of minimizing system energy consumption for task offloading within non-line-of-sight (NLoS) mobile edge computing (MEC) environments. Our approach integrates an active reconfigurable intelligent surface (RIS) and employs a hybrid transmission scheme combining time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) to enhance the quality of service (QoS) for user task offloading. The formulation of this problem as a non-convex optimization issue presents significant challenges due to its inherent complexity. To overcome this, we introduce an innovative method termed element refinement-based differential evolution (ERBDE). Initially, through rigorous theoretical analysis, we optimally determine the allocation of local computation resources, computation resources at the base station (BS), and transmit power of users, while maintaining fixed values for the offloading ratio, amplification factor, phase of the reflecting element, and the transmission period. Subsequently, we employ the differential evolution (DE) algorithm to iteratively fine-tune the offloading ratio, amplification factor, phase of the reflecting element, and transmission period towards near-optimal configurations. Our simulation results demonstrate that the implementation of active RIS-supported task offloading utilizing the hybrid TDMA-NOMA scheme results in an average system energy consumption reduction of 80.3%. |
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| AbstractList | In this paper, we address the challenge of minimizing system energy consumption for task offloading within non-line-of-sight (NLoS) mobile edge computing (MEC) environments. Our approach integrates an active reconfigurable intelligent surface (RIS) and employs a hybrid transmission scheme combining time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) to enhance the quality of service (QoS) for user task offloading. The formulation of this problem as a non-convex optimization issue presents significant challenges due to its inherent complexity. To overcome this, we introduce an innovative method termed element refinement-based differential evolution (ERBDE). Initially, through rigorous theoretical analysis, we optimally determine the allocation of local computation resources, computation resources at the base station (BS), and transmit power of users, while maintaining fixed values for the offloading ratio, amplification factor, phase of the reflecting element, and the transmission period. Subsequently, we employ the differential evolution (DE) algorithm to iteratively fine-tune the offloading ratio, amplification factor, phase of the reflecting element, and transmission period towards near-optimal configurations. Our simulation results demonstrate that the implementation of active RIS-supported task offloading utilizing the hybrid TDMA-NOMA scheme results in an average system energy consumption reduction of 80.3%. |
| ArticleNumber | 104005 |
| Author | Hong, Xuemin Lu, Baoshan Fang, Junli Liu, Junxiu |
| Author_xml | – sequence: 1 givenname: Baoshan surname: Lu fullname: Lu, Baoshan email: baoshanlu@gxnu.edu.cn organization: Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, 541001, China – sequence: 2 givenname: Junli orcidid: 0009-0007-4256-2553 surname: Fang fullname: Fang, Junli email: junlifang@stu.xmu.edu.cn organization: Department of Informatics and Communication Engineering, Xiamen University, Xiamen, Fujian, 361005, China – sequence: 3 givenname: Junxiu surname: Liu fullname: Liu, Junxiu email: j.liu@ieee.org organization: Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, 541001, China – sequence: 4 givenname: Xuemin surname: Hong fullname: Hong, Xuemin email: xuemin.hong@xmu.edu.cn organization: Department of Informatics and Communication Engineering, Xiamen University, Xiamen, Fujian, 361005, China |
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| Keywords | Active reconfigurable intelligent surface Hybrid time division multiple access non-orthogonal multiple access Energy consumption minimization Non-line-of-sight Task offloading |
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| SubjectTerms | Active reconfigurable intelligent surface Energy consumption minimization Hybrid time division multiple access non-orthogonal multiple access Non-line-of-sight Task offloading |
| Title | Energy efficient multi-user task offloading through active RIS with hybrid TDMA-NOMA transmission |
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