Spectrum Sharing in Multi-Tenant 5G Cellular Networks: Modeling and Planning

IEEE Access

Obada Al-Khatib¹  Wibowo Hardjawana²  Branka Vucetic² 

[1] Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai, UAE;School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia;

关键词: Network virtualization;    pursuit learning;    queuing systems;    spectrum sharing;   

DOI  :  10.1109/ACCESS.2018.2886447

来源: DOAJ

【 摘 要 】

A multi-tenant cellular network is a paradigm where the physical infrastructure of the network is leased by various big industries, e.g., power utilities and transportation. Hence, a major challenge in a multi-tenant cellular network is the efficient allocation of the physical spectrum to various tenants with broadly distinct quality-of-service (QoS) requirements and communications traffic characteristics. In this paper, we approach this issue by presenting a versatile spectrum sharing scheme, which may be deployed to model any spectrum sharing strategy between various tenants in a multi-tenant cellular network. The proposed spectrum sharing scheme is based upon a queuing system that considers the various communications traffic characteristics of the tenants. In addition, by using the developed queuing system, mathematical expressions for the blocking probability and spectrum utilization are derived. We then propose an optimal spectrum planning scheme, referred to as reservation-based sharing (RBS) policy that maximizes the spectrum utilization by allocating the spectrum resources to various tenants according to their traffic loads. The computational complexity of the optimal RBS policy is reduced by developing a learning automata technique, referred to as pursuit learning-based RBS policy. By using real traffic parameters for various tenants, the results show that the simulation and analytical results match well, ensuring the accuracy of the proposed analytical model. Moreover, the results indicate that the proposed pursuit learning-based RBS policy firmly matches the optimal solution and delivers a higher spectrum utilization that increases linearly with the number of tenants.

【 授权许可】

Unknown