Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks

Zhou, Fuhui; Hu, Rose Qingyang

doi:10.1109/twc.2020.2970920

Cited by 214 publications

(140 citation statements)

References 39 publications

(135 reference statements)

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“…Reference [14] has designed a computational offload strategy to achieve the purpose of minimizing the energy overhead during the computational offload; reference [15] considers the time overhead of computing tasks during transmission and calculation to minimize the system delay. Purpose: computation efficiency maximization problems in [16] are formulated in wireless-powered MEC networks under both partial and binary computation offloading modes. e research in [17] mainly studies the dynamic migration of users during the calculation offload process.…”

Section: Related Researchmentioning

confidence: 99%

Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing

Ding

Zhang

2021

Mathematical Problems in Engineering

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Due to the limited computing resources of the mobile edge computing (MEC) server, a massive Internet of things device computing unloading strategy using game theory in mobile edge computing is proposed. First of all, in order to make full use of the massive local Internet of things equipment resources, a new MEC system computing an unloading system model based on device-to-device (D2D) communication is designed and modeled, including communication model, task model, and computing model. Then, by using the utility function, the parameters are substituted into it, and the optimization problem with the goal of maximizing the number of CPU cycles and minimizing the energy consumption is constructed with the unloading strategy and power as constraints. Finally, the game theory is used to solve the problem of computing offload. Based on the proposed beneficial task offload theory, combined with the mobile user device computing offload task amount, transmission rate, idle device performance, and other factors, the computing offload scheme suitable for their own situation is selected. The simulation results show that the proposed scheme has better convergence characteristics, and, compared with other schemes, the proposed scheme significantly improves the amount of data transmission and reduces the energy consumption of the task.

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Section: Related Researchmentioning

confidence: 99%

Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing

Ding

Zhang

2021

Mathematical Problems in Engineering

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“…The authors in [13,29] extended the energy consumption minimization problems into multi-user MEC networks with TDMA and orthogonal frequency-division multiple access, respectively. Energy-efficient TDMA and non-orthogonal multiple access (NOMA) using partial offloading and binary offloading are studied in the wireless-powered MEC networks [30].…”

Section: Related Workmentioning

confidence: 99%

Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System

Liu

et al. 2020

Future Internet

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With the rapid popularity of mobile devices (MDs), mobile edge computing (MEC) networks and wireless power transmission (WPT) will receive more attention. Naturally, by integrating these two technologies, the inherent energy consumption during task execution can be effectively reduced, and the collected energy can be provided to charge the MD. In this article, our research focuses on extending the battery time of MDs by maximizing the harvested energy and minimizing the consumed energy in the MEC–WPT system, which is formulated as a residual energy maximization problem and also a non-convex optimization problem. On the basis of study on maximizing the residual energy under multi-users and multi-time blocks, we propose an effective jointly optimization method (i.e., jointly optimize the energy harvesting time, task-offloading time, task-offloading size and the MDs’ CPU frequency), which combines the convex optimization method and the augmented Lagrangian to solve the residual energy maximum problem. We leverage Time Division Multiple Access (TMDA) mode to coordinate computation offloading. Simulation results show that our scheme has better performance than the benchmark schemes on maximizing residual energy. In particular, our proposed scheme is outstanding in the failure rate of multiple MDs and can adapt to the task size to minimize the failure rate.

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“…where the second term in (14) represents the power consumption of the BS for downlink transmission and δ BS ≥ 1 accounts for the inefficiency of the BS power amplifier. Moreover, w k ≥ 1, ∀k, are weights that allow the prioritization of the users' power consumption compared to the BS's power consumption.…”

Section: Total System Power Consumptionmentioning

confidence: 99%

“…Existing resource allocation algorithms for MEC systems, such as [13]- [16], are based on Shannon's capacity formula. In particular, the authors of [13], [15] studied energy-efficient resource allocation for MEC, while computation rate maximization was targeted in [14]. However, if the resource allocation design for URLLC MEC systems is based on Shannon's capacity formula, the reliability of the offloading and downloading processes cannot be guaranteed because of the imposed delay constraints.…”

mentioning

confidence: 99%

Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems

Ghanem

Jamali

Sun

et al. 2019

2019 IEEE International Conference on Communications Workshops (ICC Workshops)

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In this paper, we study resource allocation algorithm design for secure multi-user downlink ultra-reliable low latency communication (URLLC). To enhance physical layer security (PLS), the base station (BS) is equipped with multiple antennas and artificial noise (AN) is injected by the BS to impair the eavesdroppers' channels. To meet the stringent delay requirements in secure URLLC systems, short packet transmission (SPT) is adopted and taken into consideration for resource allocation design. The resource allocation algorithm design is formulated as an optimization problem for minimization of the total transmit power, while guaranteeing quality-of-service (QoS) constraints regarding the URLLC users' number of transmitted bits, packet error probability, information leakage, and delay. Due to the nonconvexity of the optimization problem, finding a global solution entails a high computational complexity. Thus, we propose a low-complexity algorithm based successive convex approximation (SCA) to find a sub-optimal solution. Our simulation results show that the proposed resource allocation algorithm design ensures the secrecy of the URLLC users' transmissions, and yields significant power savings compared to a baseline scheme.

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Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks

Cited by 214 publications

References 39 publications

Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing

Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing

Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System

Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems

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