TY - GEN
T1 - Intelligent Tracking of Network Dynamics for Cross-Technology Coexistence over Unlicensed Bands
AU - Hirzallah, Mohammed
AU - Krunz, Marwan
N1 - Funding Information:
This research was supported in part by NSF and by the Broadband Wireless Access & Applications Center (BWAC). Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the author(s) and do not necessarily reflect the views of NSF.
Funding Information:
This research was supported in part by NSF and by the Broadband Wireless Access and Applications Center (BWAC)
Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - 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.
AB - 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.
KW - 5G New Radio Unlicensed (NR-U)
KW - Cross-technology coexistence
KW - Feature selection and extraction
KW - IEEE 802.11
KW - Intelligent tracking
KW - LAA
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85083466293&partnerID=8YFLogxK
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U2 - 10.1109/ICNC47757.2020.9049660
DO - 10.1109/ICNC47757.2020.9049660
M3 - Conference contribution
AN - SCOPUS:85083466293
T3 - 2020 International Conference on Computing, Networking and Communications, ICNC 2020
SP - 698
EP - 703
BT - 2020 International Conference on Computing, Networking and Communications, ICNC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computing, Networking and Communications, ICNC 2020
Y2 - 17 February 2020 through 20 February 2020
ER -