The major issue in smart grid (SG) communications networks is the latency management of a vast amount of SG traffic in the access network that connects power substations to a large number of SG monitoring devices. There are three possible deployments for the SG access network considered by power industries: 1) a public access network with a mix of SG and human-to-human (H2H) traffic; 2) a private access network exclusively assigned to SG communications; and 3) a mix of private and public access networks, referred to as hybrid access networks. The SG communications traffic is classified as fixed-scheduling (FS) and event-driven (ED). The FS and ED traffic, generated by SG devices, occur on a periodic basis and as a response to electricity supply conditions, respectively. In this paper, we develop traffic models for public, private, and hybrid SG access networks based on queuing theory. By using these models, we derive an expression for the mean queuing delay for each traffic class in each network. We then propose an optimization problem to find the optimal partitioning of the SG traffic in a hybrid access network. The analytical results obtained from the proposed models agree very well with the simulation results.
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 multitenant 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. INDEX TERMS Network virtualization, pursuit learning, queuing systems, spectrum sharing. OBADA AL-KHATIB (M'17) received the B.Sc. degree (Hons.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.