Data Traffic Analysis and Small Cell Deployment in Cellular Networks

In this thesis, the study of small cell deployment in heterogeneous networks is presented. The research work can be divided into three aspects. The first part is user data traffic analysis for an existing 3G network in London. The second part is the deployment of additional small cells on top of existing heterogeneous networks. The third part is small cell deployment based on stochastic geometry analysis of heterogeneous networks. In the first part, an analysis of 3G network user downlink data traffic is presented. With the increasing demands for high data rate and energy-efficient cellular service, it is important to understand how cellular user data traffic changes over time and in space. A statistical model of time-varying throughput per cell and the distribution of instantaneous throughput per cell over different cells based on throughput measurements from a real-world large-scale urban cellular network are provided. The model can generate network traffic data that are very close to the measured traffic and can be used in simulations of large-scale urban-area mobile networks. In the second part of the work, three different small-cell deployment strategies are proposed. As the mobile data demand keeps growing, an existing heterogeneous network composed of macrocells and small cells may still face the problem of not being able to provide sufficient capacity for unexpected but reoccurring hot spots. The proposed strategies avoid replanning the overall network while fulfilling the hot spot demand by optimizing the deployment of additional mobile small cells on top of the existing HetNet. By simplified the optimization problem, we first proposed a fixed number deployment algorithm and then extend it into deployment over existing network algorithm to solve the joint optimization problem. The simulation results show that these two proposed algorithms require less small cells to be deployed while providing higher minimum user throughput. Moreover, a reduced-complexity iterative algorithm is proposed. The simulation results show that it significantly outperforms the random deployment of new small cells and achieves performance very close to numerically solving the joint optimization in terms of minimum user throughput and required number of new small cells, especially for a large number of unexpected hot-spot users. In the third part, a stochastic geometry analysis is provided for a heterogeneous network affected by a large hot spot. Based on the analysis, the optimal numbers of additional small cells required in the HS and non-HS areas are obtained by minimizing the difference between the numbers of macrocell users after and before the HS occurs. Then an algorithm is proposed to maximize the average user throughput by jointly optimizing the locations of additional small cells and user associations of all cells. Simulation results show that the proposed algorithm can maintain the average user throughput above a threshold with excellent fairness among all users even for a very high density of HS users.