Search

Meeting the diverse delay requirements of emerging wireless applications is one of the most critical goals for the design of ultra-dense networks. Although the delay of point-to-point communications has been well investigated using classical queueing theory, the delay of multipoint- to-multipoint communications, such as in ultra-dense networks, has not been explored in depth. The main technical difficulty lies in the interacting queues problem, in which the service rate is coupled with the statuses of other queues. In this article, we elaborate on the main challenges in the delay analysis in ultra-dense networks. Several promising approaches, such as introducing the dominant system and the simplified system, to bypass these difficulties are proposed and summarized to provide useful guidance. [ABSTRACT FROM PUBLISHER]

Optimizing the cellular network?s cell locations is one of the most fundamental problems of network design. The general objective is to provide the desired QoS with the minimum system cost. In order to meet a growing appetite for mobile data services, heterogeneous networks have been proposed as a cost- and energyefficient method of improving local spectral efficiency. While unarticulated cell deployments can lead to localized improvements, there is a significant risk posed to network-wide performance due to the additional interference. The first part of the article focuses on state-of-the-art modelling and radio planning methods based on stochastic geometry and Monte Carlo simulations, and the emerging automatic deployment prediction technique for low-power nodes, or LPNs, in heterogeneous networks. The technique advises an LPN where it should be deployed, given certain knowledge of the network. The second part of the article focuses on algorithms that utilize interference and physical environment knowledge to assist LPN deployment. The proposed techniques can not only improve network performance, but also reduce radio planning complexity, capital expenditure, and energy consumption of the cellular network. The theoretical work is supported by numerical results from system-level simulations that employ real cellular network data and physical environments. [ABSTRACT FROM PUBLISHER]