Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Fatima Hussain*, Syed Ali Hassan, Rasheed Hussain, Ekram Hossain

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

217 Citations (Scopus)

Abstract

Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with a huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML- and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML- and DL-based resource management techniques in wireless IoT networks and the techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

Original languageEnglish
Article number8951180
Pages (from-to)1251-1275
Number of pages25
JournalIEEE Communications Surveys and Tutorials
Volume22
Issue number2
DOIs
Publication statusPublished - 7 Jan 2020

Bibliographical note

Funding Information:
Manuscript received June 19, 2019; revised October 10, 2019 and November 28, 2019; accepted January 1, 2020. Date of publication January 7, 2020; date of current version May 28, 2020. This work was supported in part by the Discovery Grant from the Natural Sciences and Engineering Research Council of Canada, Canada. (Corresponding author: Ekram Hossain.) Fatima Hussain is with the API Operations and Delivery, Technology and Operations, Royal Bank of Canada, Toronto, ON M5J 0B8, Canada (e-mail: fatima.hussain@rbc.com).

Publisher Copyright:
© 1998-2012 IEEE.

Keywords

  • D2D
  • deep learning
  • HetNets
  • Internet-of-Things (IoT)
  • machine learning
  • MIMO
  • NOMA
  • resource allocation
  • resource management
  • wireless IoT

Fingerprint

Dive into the research topics of 'Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges'. Together they form a unique fingerprint.

Cite this