A1 Journal article (refereed)
Energy Efficient Resource Allocation and User Scheduling for Collaborative Mobile Clouds with Hybrid Receivers (2016)

Chang, Z., Gong, J., Ristaniemi, T., & Niu, Z. (2016). Energy Efficient Resource Allocation and User Scheduling for Collaborative Mobile Clouds with Hybrid Receivers. IEEE transactions on vehicular technology, 65(12), 9834-9846. https://doi.org/10.1109/TVT.2016.2525821

JYU authors or editors

Publication details

All authors or editors: Chang, Zheng; Gong, Jie; Ristaniemi, Tapani; Niu, Zhisheng

Journal or series: IEEE transactions on vehicular technology

ISSN: 0018-9545

eISSN: 1939-9359

Publication year: 2016

Volume: 65

Issue number: 12

Pages range: 9834-9846

Publisher: Institute of Electrical and Electronics Engineers

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/TVT.2016.2525821

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/52572


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.

Keywords: resource allocation

Free keywords: content distribution; green communications; subchannel allocation; power allocation; user scheduling; collaborative mobile clouds; user cooperation

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2016

JUFO rating: 2

Last updated on 2021-02-08 at 10:26