Joint subcarrier and power allocation for OFDMA based mobile edge computing system

Li, Mengyuan; Yang, Shuo; Zhang, Zhenduo; Ren, Jinke; Yu, Guanding

doi:10.1109/pimrc.2017.8292207

Cited by 28 publications

(14 citation statements)

References 6 publications

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“…In a single MEC server scenario, some works focused on decreasing the energy consumption with the constraints of computation resources and delay, the tasks of multiple users are offloaded to an edge server, and the joint optimization problem of offloading decision and resource allocation was studied in binary offloading case [16]- [18]. In [19], the joint sub-channel and power allocation problem in the MEC system based on Orthogonal Frequency Division Multiple Access (OFDMA) was investigated to minimize the delay of each mobile device. Chen et al [20] studied the multi-user offloading problem in a multichannel wireless environment and regarded the distributed offloading decision problem as a multi-user potential game, and proved the existence of Nash equilibrium.…”

Section: Related Workmentioning

confidence: 99%

Joint Task Offloading and Resource Allocation for Multi-Task Multi-Server NOMA-MEC Networks

Xue

2021

IEEE Access

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By offloading computationally intensive tasks of smart end devices to edge servers deployed at the edge of the network, mobile edge computing (MEC) has become a promising technology to provide computing services for Internet of Things (IoT) devices. In order to further improve the access capability of MEC and increase the spectrum utilization efficiency, in this paper, Non-Orthogonal Multiple Access (NOMA) technology is introduced into MEC systems and we study the computing offloading problem of multi-user, multi-task and multi-server through joint optimization of task offloading and resource allocation, we intend to maximize the system's processing capability as an optimization goal. To solve the proposed mixed integer nonlinear programming (MINLP) problem, the objective optimization problem is firstly decoupled into two sub-problems of resource allocation and task allocation. Secondly the resource allocation problem is further decomposed into computation resource optimization and communication resource allocation. For the communication resource allocation, it first fixed power allocation, then the subchannel allocation problem is regarded as a many-to-one matching problem between sub-channels and users. In addition, we propose a low-complexity sub-optimal matching algorithm for sub-channel allocation to maximize the offloading efficiency. Based on our proposed sub-channel allocation scheme, the transmission power allocation is regarded as a convex optimization problem, which is tackled by Lagrangian multiplier method. Finally, under the condition of resource allocation, the tasks of all end devices (EDs) are allocated. Experimental numerical results show that the proposed scheme can effectively decrease latency and energy consumption of networks, improve system processing capability, and further improve MEC system performance.INDEX TERMS Mobile edge computing, multi-task multi-server, non-orthogonal multiple access, processing capability, resource allocation, task offloading

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Section: Related Workmentioning

confidence: 99%

Joint Task Offloading and Resource Allocation for Multi-Task Multi-Server NOMA-MEC Networks

Xue

2021

IEEE Access

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“…In [26], maximizing the throughput of the uplink in the OFDMA-SWIPT system is regarded as the research goal, and the cases of frequency division multiplexing (FDD) and time division multiplexing (TDD) are analyzed separately. M Li et al proposed an OFDMA-based MEC system and developed a heuristic algorithm that calculates sub-carrier allocation and power allocation separately [27].…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient computation offloading and resource allocation in SWIPT-based MEC Networks

Xuefei

2024

IEEE Access

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“…Optimal communications and computation resource allocation for MEC systems based on OFDMA has received significant interest [1,2,3,4]. Note that [2,3,4] assume that computation results are negligible and hence do not consider the transmission of computation results.…”

Section: Introductionmentioning

confidence: 99%

Optimal scalable MEC systems based on TDD-OFDMA

et al. 2021

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This letter investigates the optimal tradeoff between user experience and communications and computation resource consumptions in designing efficient mobile edge computing (MEC) systems which are based on orthogonal frequency division multiple access (OFDMA) and operating in time division duplexing (TDD) mode. First, we establish a communication model for offloading scalable tasks in TDD-OFDMA systems. Then, we formulate the system utility maximization problem with respect to the service level selection and resource allocation. Finally, we propose two algorithms to obtain a globally optimal solution and a low-complexity suboptimal solution, respectively, using convex optimization and difference of convex (DC) programming.

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Joint subcarrier and power allocation for OFDMA based mobile edge computing system

Cited by 28 publications

References 6 publications

Joint Task Offloading and Resource Allocation for Multi-Task Multi-Server NOMA-MEC Networks

Joint Task Offloading and Resource Allocation for Multi-Task Multi-Server NOMA-MEC Networks

Energy-efficient computation offloading and resource allocation in SWIPT-based MEC Networks

Optimal scalable MEC systems based on TDD-OFDMA

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