Large-scale Spatial Distribution Identification of Base Stations in Cellular Networks

The performance of cellular system significantly depends on its network topology, where the spatial deployment of base stations (BSs) plays a key role in the downlink scenario. Moreover, cellular networks are undergoing a heterogeneous evolution, which introduces unplanned deployment of smaller BSs, thus complicating the performance evaluation even further. In this paper, based on large amount of real BS locations data, we present a comprehensive analysis on the spatial modeling of cellular network structure. Unlike the related works, we divide the BSs into different subsets according to geographical factor (e.g. urban or rural) and functional type (e.g. macrocells or microcells), and perform detailed spatial analysis to each subset. After examining the accuracy of Poisson point process (PPP) in BS locations modeling, we take into account the Gibbs point processes as well as Neyman-Scott point processes and compare their accuracy in view of large-scale modeling test. Finally, we declare the inaccuracy of the PPP model, and reveal the general clustering nature of BSs deployment, which distinctly violates the traditional assumption. This paper carries out a first large-scale identification regarding available literatures, and provides more realistic and more general results to contribute to the performance analysis for the forthcoming heterogeneous cellular networks

An energy saving small cell sleeping mechanism with cell range expansion in heterogeneous networks

In recent years, the explosion of wireless data traffic has resulted in a trend of large scale dense deployment of small cells, with which the rising cost of energy has attracted a lot of research interest. In this paper, we present a novel sleeping mechanism for small cells to decrease the energy consumption of heterogeneous networks. Specifically, in the cell-edge area of a macrocell, the small cells will be put into sleep where possible and their service areas will be covered by the range-expanded small cells nearby and the macrocell; in areas close to the macrocell, the user equipments associated with a sleeping small cell will be handed over to the macrocell. Furthermore, we use enhanced inter-cell interference coordination techniques to support the range expanded small cells to avoid QoS degradation. Using a stochastic geometry-based network model, we provide the numerical analysis of the proposed approach, and the results indicate that the proposed sleeping mechanism can significantly reduce the power consumption of the network compared with the existing sleeping methods while guaranteeing the QoS requirement

Energy efficiency based on relay station deployment and sleep mode activation of eNBs for 4G LTE-A network

The energy efficiency is considered as a major issue due to large power consumption of eNBs in heterogeneous cellular networks. In this paper, a novel relay station (RS) deployment scheme and base station (BS) sleep mode algorithm is proposed to minimize the power consumption of eNBs. Initially, the RSs are deployed to cover the entire area of a cell. It is due to the purpose of providing service when the BS is in sleep mode. Then the network traffic of each cell is measured with Erlang B and C probability measure. If the network traffic is low, then the BS is decided to put into sleep mode which reduces the power consumption. For that, the corresponding RS are selected to handover the active mobile users (MUs). Then, the network traffic is estimated for each RS and the RS without MU becomes sleep mode in order to reduce power consumption further. The proposed model of cellular network reduces the power consumption by applying sleep mode algorithm for both BS and RS based on the measured network traffic. The power consumed by the entire network is measured and compared with the network without sleep mode. The evaluation results show the efficiency of our proposed work