AI Enabled Resource Allocation in Future Mobile Networks

Umer Rehman Mughal, Manzoor Ahmed Khan, Azam Beg, Ghulam Qadir Mughal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

The recent past has advocated immense flexibility in the control and elasticity in the resources of the mobile networks. The emerging application domains including autonomous driving, eHealth, smart grid, etc. position the need for the right communication stretch at a pivotal level. It goes without saying that the network operators will experience the dynamic demands like never before owing to an extremely dynamic device layer i.e., IoT. An obvious consequence of this is the uncertainty in the demand estimation and capacity planning of the communication infrastructure. This paper studies the concept of dynamic demand estimation using AI approaches. We start with learning over the mobility and activity patterns of a single user and evaluate the performance of different machine learning (ML) approaches, for example, classification, regression, and clustering. We then move on to more realistic settings, where the learning is carried out for population and autonomous driving. To do so, we use the data-set from Ernst-Reuter-Platz collected as part a Berlin City project that made the data openly available.

Original languageEnglish
Title of host publicationProceedings of IEEE/IFIP Network Operations and Management Symposium 2020
Subtitle of host publicationManagement in the Age of Softwarization and Artificial Intelligence, NOMS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728149738
DOIs
Publication statusPublished - Apr 2020
Event2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020 - Budapest, Hungary
Duration: Apr 20 2020Apr 24 2020

Publication series

NameProceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020

Conference

Conference2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020
Country/TerritoryHungary
CityBudapest
Period4/20/204/24/20

Keywords

  • Artificial Intelligence
  • Autonomous Driving
  • Mobility and Activity Management and Machine Learning.

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Information Systems and Management
  • Health Informatics
  • Artificial Intelligence

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