Reinforcement learning-based radio access network slicing for a 5G system with support for cellular V2X
RLbasedRANslicing_AuthorsDraft.pdf (888,5Kb) (Restricted access) Request copy
Què és aquest botó?
Aquest botó permet demanar una còpia d'un document restringit a l'autor. Es mostra quan:
- Disposem del correu electrònic de l'autor
- El document té una mida inferior a 20 Mb
- Es tracta d'un document d'accés restringit per decisió de l'autor o d'un document d'accés restringit per política de l'editorial
Document typeConference report
Rights accessRestricted access - publisher's policy
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectSOFTWARIZACION Y OPTIMIZACION AUTOMATICA DE REDES DE ACCESO RADIO 5G MULTI-TENANT (AEI-TEC2017-82651-R)
5G mobile systems are expected to host a variety of services and applications such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Therefore, the major challenge in designing the 5G networks is how to support different types of users and applications with different quality-of-service requirements under a single physical network infrastructure. Recently, Radio Access Network (RAN) slicing has been introduced as a promising solution to address these challenges. In this direction, our paper investigates the RAN slicing problem when providing two generic services of 5G, namely eMBB and Cellular Vehicle-to-everything (V2X). We propose an efficient RAN slicing scheme based on offline reinforcement learning that allocates radio resources to different slices while accounting for their utility requirements and the dynamic changes in the traffic load in order to maximize efficiency of the resource utilization. A simulation-based analysis is presented to assess the performance of the proposed solution.
CitationAlbonda, H.; Perez-Romero, J. Reinforcement learning-based radio access network slicing for a 5G system with support for cellular V2X. A: International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications. "Cognitive Radio-Oriented Wireless Networks: 14th EAI International Conference, CrownCom 2019: Poznan, Poland: June 11-12, 2019: proceedings". Berlín: Springer, 2019, p. 262-276.