Abstract
To effectively increase the capacity in 5G wireless networks requires more spectrum and denser network deployments. However, due to the increasing network density, the coordination of network and spectrum management becomes a challenging task both within a single operator’s network and among multiple operators’ networks. In this article, we develop new radio resource management (RRM) algorithms for adapting the frequency spectrum and the density of active access nodes in 5G ultra-dense networks (UDNs) to the traffic load and the user density in different geographical areas of the network. To this end, we formulate a network optimization problem where the allocation of spectrum bandwidth and the density of active access nodes are optimized to minimize a joint cost function, and we exploit Lagrange duality techniques to develop provably optimal network-scheduling algorithms. In particular, we develop density algorithms for two application scenarios. The first scenario solves the resource management problem for an operator of an ultra-dense network with exclusive access to a pool of frequency resources, while the second scenario applies to the management of the network density of collocated UDNs that belong to multiple operators sharing the same frequency spectrum. Simulation results demonstrate how effectively the algorithms can adapt the allocation of the spectrum allocation and the density of active access nodes over space and time.
Highlights
One of the main envisaged means to increase a cellular network’s capacity significantly beyond 4G’s capabilities, is by densification of the radio access networks (RAN) resources [1]
In this study we extend our prior work [4,14] to a rigorous optimization framework based on Lagrange duality techniques and devise provably optimal RRM algorithms for two application scenarios: (a) the resource management problem in an ultra-dense network operated by a single network operator; and (b) the resource management problem for multiple operators of co-located ultra-dense networks (UDNs) sharing a common pool of frequency resources
In the prior article [4] it has been reasoned that UDNs require new flexible RRM algorithms that jointly optimize the allocation of radio resources for a massive number of access nodes (ANs) per area, such as frequency bandwidth and transmission power budget
Summary
One of the main envisaged means to increase a cellular network’s capacity significantly beyond 4G’s capabilities (up to the projected and widely echoed 1000-fold increase in one decade), is by densification of the radio access networks (RAN) resources [1]. Assuming that the central processing units are connected to each other through a backhaul and/or backbone supporting fast coordination among them [10,11], optimal energy- and spectrum-efficient tradeoff among multiple operators can be performed [7,12] Leveraging these concepts, this article presents novel RRM algorithms for adapting and optimizing the allocation of spectrum bandwidth and the density of active ANs in ultra-dense 5G networks. The fundamentals of this research work are based on new analytical models (cf [2,13] and Section 2.2) characterizing the achievable spectral efficiency of a RAN as a function of the density of ANs, the density of active users, and the large-scale fading Through these models the average user data rate offered by a network becomes a function of the network density and the frequency bandwidth, thereby enabling us to jointly optimize both variables to support a certain user traffic demand. We exploit these models to define traffic demand constraints that captures the coupling between average traffic demand, network density and bandwidth in UDNs
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
