Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computation efficiency maximization problems are formulated in wireless-powered MEC networks...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 19; no. 5; pp. 3170 - 3184
Main Authors: Zhou, Fuhui, Hu, Rose Qingyang
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
binary computation offloading
computation efficiency
Computation offloading
Computational efficiency
Computer simulation
Computing time
Edge computing
Efficiency
Energy consumption
Energy harvesting
Iterative algorithms
Iterative methods
Maximization
Mobile computing
Mobile-edge computing
NOMA
Optimization
partial computation offloading
Resource allocation
Resource management
Servers
Task analysis
Time Division Multiple Access
Wireless communication
Wireless networks
wireless power transfer
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary:Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computation efficiency maximization problems are formulated in wireless-powered MEC networks under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated for offloading. The energy harvesting time, the local computing frequency, and the offloading time and power are jointly optimized to maximize the computation efficiency under the max-min fairness criterion. Two iterative algorithms and two alternative optimization algorithms are respectively proposed to address the non-convex problems formulated in this paper. Simulation results show that the proposed resource allocation schemes outperform the benchmark schemes in terms of user fairness. Moreover, a tradeoff is elucidated between the achievable computation efficiency and the total number of computed bits. Furthermore, simulation results demonstrate that the partial computation offloading mode outperforms the binary computation offloading mode and NOMA outperforms TDMA in terms of computation efficiency.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2020.2970920