Harmonious Cross-Technology Coexistence With Heterogeneous Traffic in Unlicensed Bands: Analysis and Approximations
Author(s) -
Mohammed Hirzallah,
Marwan Krunz,
Yong Xiao
Publication year - 2019
Publication title -
ieee transactions on cognitive communications and networking
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 25
eISSN - 2372-2045
pISSN - 2332-7731
DOI - 10.1109/tccn.2019.2934108
Subject(s) - computer science , computer network , quality of service , spectrum management , cellular network , throughput , latency (audio) , mobile broadband , channel (broadcasting) , wireless , telecommunications , cognitive radio
The dramatic growth in demand for mobile data has prompted mobile network operators (MNOs) to explore spectrum sharing in unlicensed bands. MNOs have been allowed recently to operate their LTE services over the 5 GHz Unlicensed National Information Infrastructure (U-NII) bands, currently occupied by Wi-Fi. The unlicensed LTE operation has been standardized by 3GPP under the name Licensed Assisted Access (LAA). Unlicensed 5G New radio (NR) operation over the U-NII bands, a.k.a., NR-Unlicensed (NR-U), is also being explored. To support applications with diverse Quality-of-Service (QoS) requirements, LAA, NR-U, and Wi-Fi technologies offer multiple priority classes with different contention parameters for accessing an unlicensed channel. How these different classes affect the interplay between coexisting MNOs and Wi-Fi systems is still a relatively under-explored topic. In this paper, we develop a simple yet efficient framework for fair coexistence between LTE MNOs and Wi-Fi systems, each with multiple priority classes. We derive approximated closed-form solutions for the probability of successful transmission (PST), average contention delay, and average throughput when coexisting LAA and Wi-Fi devices serve traffic with different priority classes. LTE and Wi-Fi operators can fit our solutions to estimate the performance of both systems in offline or online fashions, and use them to further optimize their system throughput and latency. Our results reveal that PSTs computed with our approximate models are within 5% difference with those obtained via close-to-real simulation under dense network deployments and high traffic loads.
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