Joint Offloading and Computation Energy Efficiency Maximization in a Mobile Edge Computing System

Sun, Haijian; Zhou, Fuhui; Hu, Rose Qingyang

doi:10.1109/tvt.2019.2893094

Cited by 152 publications

(126 citation statements)

References 12 publications

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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]. 12…”

Section: ) Fairness Considerationmentioning

confidence: 99%

Mobile Edge Computing in Unmanned Aerial Vehicle Networks

Zhou

et al. 2020

IEEE Wireless Commun.

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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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Section: ) Fairness Considerationmentioning

confidence: 99%

Mobile Edge Computing in Unmanned Aerial Vehicle Networks

Zhou

et al. 2020

IEEE Wireless Commun.

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211

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“…The energy consumed in local computing is modeled as E local k = ε k f 3 k T , where ε k denotes the coefficient depend on chip architecture at the user k [6]. The CE is the ratio of the computation bits both in local computing and offloading process to the consumed energy during the entire block T [10]. According to the definition of CE in [10] and [11], the CE of the kth user is expressed as…”

Section: System Modelmentioning

confidence: 99%

“…The CE is the ratio of the computation bits both in local computing and offloading process to the consumed energy during the entire block T [10]. According to the definition of CE in [10] and [11], the CE of the kth user is expressed as…”

Section: System Modelmentioning

confidence: 99%

“…In this letter, the computation efficiency (CE) is considered, which is a new metric that can consider both energy consumption and the computed bits. It aims to optimize the computation bits per Joule energy and describe the tradeoff between the energy consumption and the achievable computed bits [10]. In [10], the CE maximization problem under the partial offloading mode was studied in TDMA-based MEC systems, and a computation-efficient resource allocation scheme was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the above mentioned facts, in our work, we design computation-efficient resource allocation strategies to achieve the maximum weighted sum computation efficiency in the OFDMA-based MEC system under both partial and binary offloading modes. Although we have investigated CE maximization in MEC systems with TDMA in [10], the CE maximization problems and its solutions in the OFDMAbased multiple-user MEC system are very different from those in MEC systems with TDMA. The subchannel allocation in OFDMA systems results in more complicated mixed-integer programming problems.…”

Section: Introductionmentioning

confidence: 99%

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Computation Efficiency Maximization in OFDMA-Based Mobile Edge Computing Networks

Wang

Zhou

et al. 2020

IEEE Commun. Lett.

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Computation-efficient resource allocation strategies are of crucial importance in mobile edge computing networks. However, few works have focused on this issue. In this letter, weighted sum computation efficiency (CE) maximization problems are formulated in a mobile edge computing (MEC) network with orthogonal frequency division multiple access (OFDMA). Both partial offloading mode and binary offloading mode are considered. The closed-form expressions for the optimal subchannel and power allocation schemes are derived. In order to address the intractable non-convex weighted sum-of ratio problems, an efficiently iterative algorithm is proposed. Simulation results demonstrate that the CE achieved by our proposed resource allocation scheme is better than that obtained by the benchmark schemes.

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5G and Beyond Wireless Communication Networks

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Joint Offloading and Computation Energy Efficiency Maximization in a Mobile Edge Computing System

Cited by 152 publications

References 12 publications

Mobile Edge Computing in Unmanned Aerial Vehicle Networks

Mobile Edge Computing in Unmanned Aerial Vehicle Networks

Computation Efficiency Maximization in OFDMA-Based Mobile Edge Computing Networks

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