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Radio Resource Allocation for D2D-Enabled Massive Machine Communication in the 5G Era

  • Huitao Yang
  • , Boon Chong Seet
  • , Syed Faraz Hasan
  • , Peter Han Joo Chong
  • , Min Young Chung

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

Abstract

Device-to-Device (D2D) and Massive Machine Communication (MMC) are believed to be the cornerstones of future 5th generation (5G) cellular technologies. As a method to increase spectrum utilization, extend cellular coverage, and offload backhaul traffic, D2D has been recently incorporated into Release 12 of 3rd Generation Partnership Project (3GPP) Long Term Evolution Advanced (LTE-A) specifications. Devices in physical proximity can thus discover each other and communicate via a direct path using licensed LTE spectrums. Leveraging on the ubiquity of cellular coverage and harnessing LTE-A D2D for networks with massive number of machine-type communications, such as large-scale sensor networks and vehicular networks, introduces a paradigm shift and opens up new opportunities for proximity-based services. In this paper, we propose a novel radio resource allocation method for D2D discovery in clustered MMC networks. With a large number of nodes, the proposed method can still maintain the signaling overhead at a reasonable level while achieving high discovery rate. Experimental results show that the proposed approach significantly outperforms the existing 3GPP random resource allocation mechanism.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
EditorsKevin I-Kai Wang, Qun Jin, Md Zakirul Alam Bhuiyan, Qingchen Zhang, Ching-Hsien Hsu
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages55-60
Number of pages6
ISBN (Electronic)9781509040650
DOIs
Publication statusPublished - 11 Oct 2016
Externally publishedYes
Event14th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 14th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2016, 2nd IEEE International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016 - Auckland, New Zealand
Duration: 8 Aug 201610 Aug 2016

Publication series

NameProceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016

Conference

Conference14th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 14th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2016, 2nd IEEE International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
Country/TerritoryNew Zealand
CityAuckland
Period8/08/1610/08/16

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