Fair Energy-Efficient Scheduling in Wireless Powered Full-Duplex Mobile-Edge Computing Systems

Mao, Sun; Leng, Supeng; Yang, Kun; Huang, Xiaotao; Zhao, Quanxin

doi:10.1109/glocom.2017.8254208

Cited by 61 publications

(37 citation statements)

References 10 publications

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“…In [14], the energy minimization problem was extended into a multi-input single-out wireless powered MEC system, and the offloading time, the offloading bits, the CPU frequency and the energy beamforming were jointly optimized. Unlike [14], energy efficiency was defined and maximized in a full-duplex wireless powered MEC system by jointly optimizing the transmission power, offloaded bits, computation energy consumption, time slots for computation offloading and energy transfer [15]. In contrast to the work in [13]- [15], the computation bits were maximized in a wireless powered MEC system under the binary computation offloading mode [16].…”

Section: A Related Work and Motivationmentioning

confidence: 99%

Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems

Zhou

et al. 2018

IEEE J. Select. Areas Commun.

744

432

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Mobile edge computing (MEC) and wireless power transfer (WPT) are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are respectively proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperforms other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.

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Section: A Related Work and Motivationmentioning

confidence: 99%

Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems

Zhou

et al. 2018

IEEE J. Select. Areas Commun.

744

432

View full text Add to dashboard Cite

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“…We compare the total energy consumption performance of the proposed algorithm (labelled as 'Proposed NOMA FD') with the algorithm for a half-duplex (HD) MEC system with NOMA [28] (labelled as 'NOMA HD'), and the algorithm for a FD-MEC system with OMA [4] (labelled as 'OMA FD'). The total energy consumption versus time slot duration is depicted in Fig.…”

Section: Numerical Resultsmentioning

confidence: 99%

Resource Allocation in Full-Duplex Mobile-Edge Computation Systems with NOMA and Energy Harvesting

Yang

Hou

Shikh-Bahaei

2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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This paper considers a full-duplex (FD) mobile-edge computing (MEC) system with non-orthogonal multiple access (NOMA) and energy harvesting (EH), where one group of users simultaneously offload task data to the base station (BS) via NOMA and the BS simultaneously receive data and broadcast energy to other group of users with FD. We aim at minimizing the total energy consumption of the system via power control, time scheduling and computation capacity allocation. To solve this nonconvex problem, we first transform it into an equivalent problem with less variables. The equivalent problem is shown to be convex in each vector with the other two vectors fixed, which allows us to design an iterative algorithm with low complexity. Simulation results show that the proposed algorithm achieves better performance than the conventional methods.

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“…Fairness should be considered when multiple users compete the resources [13]. Five fairness criteria are normally used, namely, max-min fairness, proportional fairness, harmonic fairness, α fairness, and β fairness [15].…”

Section: ) Fairness Considerationmentioning

confidence: 99%

Mobile Edge Computing in Unmanned Aerial Vehicle Networks

Zhou

et al. 2020

IEEE Wireless Commun.

211

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Unmanned aerial vehicle (UAV)-enabled communication networks are promising in the fifth and beyond wireless communication systems. In this paper, we shed light on three UAV-enabled mobile edge computing (MEC) architectures. Those architectures have been receiving ever increasing research attention for improving computation performance and decreasing execution latency by integrating UAV into MEC networks. We present a comprehensive survey for the state-of-the-art research in this domain. Important implementation issues are clarified. Moreover, in order to provide an enlightening guidance for future research directions, key challenges and open issues are discussed.

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Fair Energy-Efficient Scheduling in Wireless Powered Full-Duplex Mobile-Edge Computing Systems

Cited by 61 publications

References 10 publications

Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems

Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems

Resource Allocation in Full-Duplex Mobile-Edge Computation Systems with NOMA and Energy Harvesting

Mobile Edge Computing in Unmanned Aerial Vehicle Networks

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