University of Oulu

M. Bagaa, T. Taleb, A. Chelli and H. Hellaoui, "Constraint Hubs Deployment for Efficient Machine-Type Communications," in IEEE Transactions on Wireless Communications, vol. 17, no. 12, pp. 7936-7951, Dec. 2018, doi: 10.1109/TWC.2018.2873293

Constraint hubs deployment for efficient machine-type communications

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Author: Bagaa, Miloud1; Taleb, Tarik1,2,3
Organizations: 1Department of Communications and Networking, School of Electrical Engineering, Aalto University, FI-00076 Aalto, Finland
2Faculty of Information Technology and Electrical Engineering, Oulu University, 90014 Oulu, Finland
3Department of Computer and Information Security, Sejong University, Seoul 05006, South Korea
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020060540781
Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2020-06-05
Description:

Abstract

Massive Internet of Things (mIoT) is an important use case of 5G. The main challenge for mIoT is the huge amount of uplink traffic as it dramatically overloads the radio access network (RAN). To mitigate this shortcoming, a new RAN technology has been suggested, where small cells are used for interconnecting different devices to the network. The use of small cells will alleviate congestion at the RAN, reduce the end-to-end (E2E) delay, and increase the link capacity for communications. In this paper, we devise three solutions for deploying and interconnecting small cells that would handle mIoT traffic. A realistic physical model is considered in these solutions. The physical model is based on a composite fading channel that captures path loss, fast fading, shadowing, and interference to derive the signal-to-interference-plus-noise ratio. The three solutions consider two conflicting objectives, namely the cost and the E2E delay for deploying and backhauling small cells. The first solution minimizes the cost while the second reduces the E2E delay. The third solution uses bargaining game theory for reducing both the cost and the E2E delay. The proposed solutions are evaluated through simulations. The obtained results demonstrate the efficiency of each solution in achieving its design goals.

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Series: IEEE transactions on wireless communications
ISSN: 1536-1276
ISSN-E: 1558-2248
ISSN-L: 1536-1276
Volume: 17
Issue: 12
Pages: 7936 - 7951
DOI: 10.1109/TWC.2018.2873293
OADOI: https://oadoi.org/10.1109/TWC.2018.2873293
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work was supported in part by 6Genesis and AoF CSN Projects under Grant 318927 and Grant 311654 and in part by the Aalto 5G meets Industrial Internet (5G@II) Project.
Academy of Finland Grant Number: 318927
311654
Detailed Information: 318927 (Academy of Finland Funding decision)
311654 (Academy of Finland Funding decision)
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