Energy-Efficient Resource Allocation and User Scheduling for Collaborative Mobile Clouds With Hybrid Receivers

In this paper, we study the resource allocation and user scheduling algorithm for minimizing the energy cost of data transmission in the context of orthogonal frequency-division multiple-access (OFDMA) collaborative mobile clouds (CMCs) with simultaneous wireless information and power transfer recei...

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Bibliographic Details
Published inIEEE transactions on vehicular technology Vol. 65; no. 12; pp. 9834 - 9846
Main Authors Zheng Chang, Jie Gong, Ristaniemi, Tapani, Zhisheng Niu
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Cloud computing
Collaboration
Collaborative mobile clouds (CMCs)
Computational geometry
content distribution
Convexity
Data transmission
Energy consumption
Energy costs
Energy distribution
Energy efficiency
green communications
Mobile communication
Optimization
Optimization techniques
power allocation
Power transfer
Receivers
Resource allocation
Resource management
Resource scheduling
Scheduling
subchannel allocation
user cooperation
user scheduling
Wireless communication
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Summary:In this paper, we study the resource allocation and user scheduling algorithm for minimizing the energy cost of data transmission in the context of orthogonal frequency-division multiple-access (OFDMA) collaborative mobile clouds (CMCs) with simultaneous wireless information and power transfer receivers. The CMC, which consists of several collaborating mobile terminals, offers one potential solution for downlink content distribution and for energy consumption (EC) reduction. Previous work on the design of the CMC system mainly focused on cloud formulation or energy efficiency (EE) investigation, whereas how to allocate the radio resource and schedule user transmission has not gotten much attention. With the objective of minimizing system EC, an optimization problem that jointly considers subchannel assignment, power allocation, and user scheduling has been presented. We propose different algorithms to address the formulated problem based on the convex optimization technique. Simulation results demonstrate that the proposed user scheduling and resource allocation algorithms can achieve significant EE performance.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2016.2525821