Radio Resource Management in Multi-numerology 5G New Radio featuring Network Slicing

Karim Boutiba, Miloud Bagaa, Adlen Ksentini

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


5G New Radio (NR) introduces several key features to support the new emerging vertical industry use-cases, mainly: (1) Different numerology that gives more flexibility in managing time slot duration, and hence satisfying different delay requirements; (2) Bandwidth part that permits dedicating parts of the bandwidth to ensure different data rate requirements. However, although 5G NR introduces several enhancements, it makes radio resource management, more precisely resource scheduling, more complex and challenging. In this paper, we address the challenge of radio resource management in 5G NR featuring network slicing. We introduce a novel scheduling solution based on Deep Reinforcement Learning (DRL) to allocate resources and numerology for UEs to satisfy their different requirements. We evaluated the solution for different network configurations and compared its performance with the maximum achievable throughput. Simulation results demonstrated the efficiency of the proposed algorithm to allocate resources and the ability to scale for larger bandwidths covering both Frequency Range 1 (FR1) and FR2, as well as serving a higher number of User Equipment (UE).

Original languageEnglish
Title of host publicationICC 2022 - IEEE International Conference on Communications
Number of pages6
ISBN (Electronic)978-1-5386-8347-7
Publication statusPublished - 2022
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Communications - Seoul, Korea, Republic of
Duration: 16 May 202220 May 2022

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607
ISSN (Electronic)1938-1883


ConferenceIEEE International Conference on Communications
Abbreviated titleICC
Country/TerritoryKorea, Republic of


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