Multi-Helper NOMA for Cooperative Mobile Edge Computing

Yilmaz, Saadet Simay; Özbek, Berna

doi:10.1109/tits.2021.3116421

Cited by 22 publications

(23 citation statements)

References 36 publications

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“…In our previous work in [28], a multi-helper cooperative MEC system based on NOMA has been examined to maximize the total offloading data under latency and power constraints. Motivated by the above background, this work presents overall delay minimization problem in the Massive MIMO-NOMA based cooperative MEC system for the scenario as shown in Fig.…”

Section: ) Secure Mec Systemsmentioning

confidence: 99%

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Delay Minimization for Massive MIMO Based Cooperative Mobile Edge Computing System With Secure Offloading

Yilmaz

Özbek

Mumtaz³

2023

IEEE Open J. Veh. Technol.

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Mobile edge computing (MEC) has been envisioned as a promising technology for enhancing the computational capacities of mobile devices by enabling task offloading. In this paper, we present a novel framework for a cooperative MEC system by employing Massive Multiple-Input Multiple-Output (MIMO) and non-orthogonal multiple access (NOMA) technologies, including security aspects. Specifically, in the proposed cooperative MEC system, there is no strong direct transmission link between the cell-edge user and the MEC server; consequently, the user sends their tasks to the MEC server through the helpers at the cell-centers. In the proposed framework, we minimize the overall delay, including secure offloading under the constraints of computing capability and transmit power. The proposed algorithm minimizes the overall delay in downlink and uplink transmission while satisfying security constraints to solve the formulated problem. The simulation results show that Massive MIMO based NOMA improves the performance of the secure MEC system by employing more than one helper.INDEX TERMS Secure offloading, delay minimization, massive multiple input multiple output (MIMO), mobile edge computing (MEC), non-orthogonal multiple access (NOMA)

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Section: ) Secure Mec Systemsmentioning

confidence: 99%

“…Then, we can extend the downlink power allocation factors to the helpers as in [28]. Assume that , represents each helper's index, which provides cooperation between the user and the MEC server.…”

Section: A Problem Solutionmentioning

confidence: 99%

Delay Minimization for Massive MIMO Based Cooperative Mobile Edge Computing System With Secure Offloading

Yilmaz

Özbek

Mumtaz³

2023

IEEE Open J. Veh. Technol.

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“…In the above research works, the user is mostly assumed to cooperate with single helper at the same time. In practical design, multiple helpers can simultaneously share their own computation and communication resources to help the user [20,[25][26][27]. In the D2D MEC system, [25] jointly optimized helpers' selection and the communication and computation resource allocation for minimizing the energy consumption.…”

Section: Introductionmentioning

confidence: 99%

“…In the D2D MEC system, [25] jointly optimized helpers' selection and the communication and computation resource allocation for minimizing the energy consumption. In [26], a multihelper MEC with NOMA-based cooperative edge computing has been presented to maximize the total offloading data subject to the latency constraints. However, the system model in [25,26] ignores results downloading, which may be not applicable for practical design.…”

Section: Introductionmentioning

confidence: 99%

“…In [26], a multihelper MEC with NOMA-based cooperative edge computing has been presented to maximize the total offloading data subject to the latency constraints. However, the system model in [25,26] ignores results downloading, which may be not applicable for practical design. Thus, [20,27] focus on the joint task offloading and results downloading.…”

Section: Introductionmentioning

confidence: 99%

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Joint Task Partition and Resource Allocation for Multiuser Cooperative Mobile Edge Computing

Xie

Wang

Liu

2022

Wireless Communications and Mobile Computing

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Exploiting the idle computation resources distributed at wireless devices (WDs) can enhance the mobile edge computing (MEC) computation performance. This paper studies a multiuser cooperative computing system consisting of one local user and multiple helpers, in which the user solicits multiple nearby WDs acting as helpers for cooperative computing. We design an efficient orthogonal frequency-division multiple access- (OFDMA-) aided three-phase transmission protocol, under which the user’s computation-intensive tasks can be executed in parallel by local computing and offloading. Under this setup, we study the energy consumption minimization problem by optimizing the user’s task partition, jointly with the communication and computation resources allocation for task offloading and results downloading, subject to the user’s computation latency constraint. For the nonconvex problem, we first transform the original problem into a convex one and then use the Lagrange duality method to obtain the globally optimal solution. Compared with other benchmark schemes, numerical results validate the effectiveness of the proposed joint task partition and resource allocation (JTPRA) scheme.

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Non‐orthogonal multiple access‐based task processing and energy optimization in vehicular edge computing networks

Shi,

Li,

Zhao

et al. 2024

Concurrency and Computation

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SummaryVehicular edge computing (VEC) is envisioned as a promising approach to process explosive vehicle tasks, where vehicles can choose to upload tasks to nearby edge nodes for processing. However, since the communication between vehicles and edge nodes is via wireless network, which means the channel condition is complex. Moreover, in reality, the arrival time of each vehicle task is stochastic, so efficient communication methods should be designed for VEC. As one of the key communication technologies in 5G, non‐orthogonal multiple access (NOMA) can effectively increase the number of simultaneous transmission tasks and enhance transmission performance. In this article, we design a NOMA‐based task allocation scheme to improve the VEC system. We first establish the mathematical model and divide the allocation of tasks into two processes: the transmission process and the computation process. In the transmission process, we adopt the NOMA technique to upload the tasks in batches. In the computation process, we use a high response‐ratio strategy to determine the computation order. Then we define the optimization objective as maximizing task completion rate and minimizing task energy consumption, which is an integer nonlinear problem with lots of integer variables and cannot be solved directly. Through further analysis, we design a heuristics algorithm which we name as the AECO (average energy consumption optimization) algorithm. By using the AECO, we obtain the optimal allocation strategy by constantly adjusting the optimal variables. Simulation results demonstrate that our algorithm has a significant number of advantages.

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Multi-Helper NOMA for Cooperative Mobile Edge Computing

Cited by 22 publications

References 36 publications

Delay Minimization for Massive MIMO Based Cooperative Mobile Edge Computing System With Secure Offloading

Delay Minimization for Massive MIMO Based Cooperative Mobile Edge Computing System With Secure Offloading

Joint Task Partition and Resource Allocation for Multiuser Cooperative Mobile Edge Computing

Non‐orthogonal multiple access‐based task processing and energy optimization in vehicular edge computing networks

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