Intelligent Tracking of Network Dynamics for Cross-Technology Coexistence over Unlicensed Bands

Mohammed Hirzallah, Marwan Krunz

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

Abstract

Unlicensed bands offer great opportunities for numerous wireless technologies, including IEEE 802.11-based systems, 4G Licensed-Assisted-Access (LAA), and 5G New Radio Unlicensed (NR-U) networks. Achieving harmonious coexistence between these technologies requires real-time adaptation of their channel access, which can be facilitated by artificial intelligence (AI) and machine learning (ML) techniques. However, to leverage such techniques, we need to characterize the state of unlicensed wireless channel and the dynamics of the coexisting systems. In this paper, we introduce the concept of Sensing Fingerprint (SF) profile to characterize the state of coexisting networks and track their dynamics over unlicensed bands. We conduct extensive experiments to show the effectiveness of SF profile in tracking key network dynamics, including sensitivity thresholds of contending devices, their mobility, traffic loads, and other channel access parameters. AI-and ML-based controllers can utilize this tool to model the state of coexisting networks and track their dynamics.

Original languageEnglish (US)
Title of host publication2020 International Conference on Computing, Networking and Communications, ICNC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages698-703
Number of pages6
ISBN (Electronic)9781728149059
DOIs
StatePublished - Feb 2020
Event2020 International Conference on Computing, Networking and Communications, ICNC 2020 - Big Island, United States
Duration: Feb 17 2020Feb 20 2020

Publication series

Name2020 International Conference on Computing, Networking and Communications, ICNC 2020

Conference

Conference2020 International Conference on Computing, Networking and Communications, ICNC 2020
CountryUnited States
CityBig Island
Period2/17/202/20/20

    Fingerprint

Keywords

  • 5G New Radio Unlicensed (NR-U)
  • Cross-technology coexistence
  • Feature selection and extraction
  • IEEE 802.11
  • Intelligent tracking
  • LAA
  • Machine learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Control and Optimization

Cite this

Hirzallah, M., & Krunz, M. (2020). Intelligent Tracking of Network Dynamics for Cross-Technology Coexistence over Unlicensed Bands. In 2020 International Conference on Computing, Networking and Communications, ICNC 2020 (pp. 698-703). [9049660] (2020 International Conference on Computing, Networking and Communications, ICNC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNC47757.2020.9049660