Delay Minimization for NOMA-mmW Scheme-Based MEC Offloading

Shi, Jia; Zhou, Yifan; Li, Zan; Zhao, Zhongling; Chu, Zheng; Xiao, Pei

doi:10.1109/jiot.2022.3206432

Cited by 24 publications

(3 citation statements)

References 37 publications

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“…Finally, new architectures have also been considered to minimize latency. The authors of [79] proposed to define new mmWave-based architectures to improve NOMA-MEC offloading environments. The advantage of NOMA scheme- based mmWave is that it significantly improves the accessing efficiency for crowded networks by allowing users to share time and spectrum resources in the same spatial layer with NOMA.…”

Section: B Delay Minimizationmentioning

confidence: 99%

Non-Orthogonal Multiple Access for Offloading in Multi-Access Edge Computing: A Survey

Dulout,

Mendiboure,

Pousset

et al. 2023

IEEE Access

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Multi-Access Edge Computing (MEC) architectures now seem to represent the future of data processing architectures. Indeed, they have the potential to optimize the use of the backhaul network, guarantee the implementation of real-time applications and offer services adapted to the user's context. To further improve the performance of MEC architectures, it may be worth combining them with a new technology at the physical layer: Non Orthogonal Multiple Access (NOMA). During the task offloading process, this could enhance energy efficiency, maximize the number of users benefiting from MEC services and further reduce latency. That is why the article focuses on the use of NOMA for task offloading in MEC architectures and offers a comprehensive study on the subject. First, we define a taxonomy to ensure a systematic review of existing work. We then analyze and compare existing works, classifying them according to their purpose. Based on this, we then discuss the benefits and limitations of existing work, highlighting some good practices. Finally, we identify future research directions in the field of NOMAassisted MEC architectures.

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Section: B Delay Minimizationmentioning

confidence: 99%

Non-Orthogonal Multiple Access for Offloading in Multi-Access Edge Computing: A Survey

Dulout,

Mendiboure,

Pousset

et al. 2023

IEEE Access

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“…In order to minimize the average delay of task offloading, a joint bandwidth allocation, user device scheduling, and transmission power allocation problem with considering the directional millimeter-wave link was formulated. By utilizing the alternative optimization method, the joint bandwidth and transmit power algorithm was proposed [18].…”

Section: Related Workmentioning

confidence: 99%

Joint Virtual Machine Selection and Computation Resource Allocation in Mobile Edge Computing

Yang,

Meng,

et al. 2023

Journal of Sensors

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Mobile edge computing (MEC) is considered as an effective technology to enhance the storage and computation capability of smart power sensors (SPSs) in smart grid networks. The MEC server is composed of multiple virtual machines (VMs) with powerful computation capability, and each VM can process multiple tasks independently, which cannot be ignored during the task computation period. In this work, we aim to minimize the energy consumption of SPSs subject to the task offloading delay by jointly optimizing the VM selection and computation resource allocation. Considering the formulated problem is nonconvex, we first utilize the linearization method to transform it into a convex optimization problem. And then, by using the branch and bound method, we propose the joint VM selection and computation resource allocation (JVMSRA) algorithm. Considering the complexity of the JVMSRA algorithm is high, we decompose the primal problem into two subproblems and solve them by utilizing the ant colony method and CVX package, respectively. Based on the solutions of the two subproblems, the resource allocation-based ant colony (RAAC) algorithm is proposed. Simulation results show that the proposed RAAC algorithm and JVMSRA algorithm decrease by 6% and 8.8% on average compared with the benchmark algorithm, respectively, when the computation resources of each VM increase from 1 to 3 GHz.

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“…In recent years, due to the rapid proliferation of compute-intensive or time-sensitive applications in various fields, MEC (Mobile Edge Computing) has being widely adopted in the Internet of Things (IoT) [1] and Virtual Reality (VR) [2]. Users in MEC can offload the complex and huge computing tasks to nearby edge servers for achieving the lower delay [3] and calculation cost [4]. However, due to the spatial restriction of the MEC server's deployment, it is not flexible enough to provide services to users.…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization between Task Offloading and Resource Allocation in Mobile Edge Computing with multiple UAVs

Chen¹,

Gao

Zou³

et al. 2023

Preprint

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Energy consumption and completion time are two hot issues in UAV (Unmanned Aerial Vehicles) assisted edge computing.The current research is mainly focused on the interaction between UAVs and edge servers, while the interplay between UAVs is rarely considered.In this paper, in the considered scenario with multiple UAVs and servers, the UAVs may possess idle resources, and thus function as temporary servers to provide task offloading services for other UAVs.We begin by formulating a multi-objective joint optimization problem, which aims to balance the energy consumption and time delay in order to maximize the benefits of the system. Then we use a multi-objective genetic algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II) to iteratively find the optimal offloading strategy.Through numerical simulation, the results obtained from the numerical simulation demonstrate the superiority of the proposed method in achieving the Pareto optimal frontier that balances energy consumption and completion time.

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Delay Minimization for NOMA-mmW Scheme-Based MEC Offloading

Cited by 24 publications

References 37 publications

Non-Orthogonal Multiple Access for Offloading in Multi-Access Edge Computing: A Survey

Non-Orthogonal Multiple Access for Offloading in Multi-Access Edge Computing: A Survey

Joint Virtual Machine Selection and Computation Resource Allocation in Mobile Edge Computing

Joint Optimization between Task Offloading and Resource Allocation in Mobile Edge Computing with multiple UAVs

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