Cell Planning And Optimization Algorithm For Heterogeneous Cellular Networks

Due to the staggering growth of the traffic demand, the high installation cost and the scarcity of radio resource in mobile communications, efficient cell planning ap-pears of paramount importance. Generally, shrinking cell sizes serves as the over-riding scheme to keep up with the mounting demand for higher data rates. As a re-sult, a growing number of base stations (BSs) need to be installed, engendering the sharp increase of deployment cost. In the standardization process of the next genera-tion cellular networks, such as3GPP (3rd Generation Partnership Project) Long Term Evolution-Advanced (LTE-A), heterogeneous cellular networks are deemed as a cost-efficient way to satisfy the increasing traffic demand and received extensive attention from industry and academia. Heterogeneous cellular network is able to significantly improve the spectrum utilization, system capacity gain and indoor coverage, by taking advantage of the short range between the lower power BSs and users. Furthermore, low-power BS with lower transmission power and smaller physical size, offers flexible site acquisitions. Consequently, cell planning will contribute to a new cost-efficient and flexible planning paradigm for cellular networks with low-power cells. In this thesis, we study the cell planning problems for heterogeneous cellular networks. The main contributions of this work are summarized as follows.1. We developed a general models to illustrate the cell planning problems for heterogeneous cellular networks, in which the power budget and the bandwidth budget of each selected cell are both taken into consideration. Besides, traffic demand and relay backhaul constraints are considered. Comparing to existing work in the literature, our models are more practical for heterogeneous cellular networks. 2. We formulated the minimum cost cell planning problem for heterogeneous cellular networks. The formulated problem is NP-hard, which cannot be solved in polynomial time. We propose an O(log R)-approximation algorithm to tackle it, where R is the maximum achievable capacity of cells. Simulation results show our proposal is valid to significantly decrease total deployment cost of network planning.3. We formulated the budgeted cell planning problem for heterogeneous cellu-lar networks. We address the formulated problem by decomposing it into two sub-problems. The first subproblem is to minimize the required power of a given BS to satisfy the traffic demands of a given set of users using whole bandwidth., which is formulated as a convex problem. The optimal solution is obtained by using the Karush-Kuhn-Tucker (KKT) conditions. The second subproblem is to maximize the number of users that can be allocated to a set of BSs. The problem is also NP-hard and we pro-pose a1/2-approximation algorithm for this subproblem. Taking account of the solution to the two subproblems, we developed an e-1/2e-approximate algorithm for the budgeted cell planning problem.In summary, our proposed methods perform superiorly than macro-only cell plan-ning in literature and throw some insights on how to deploy heterogenous cellular net-works for the next generation cellular system. Parts of them have been published in or submitted to leading journals and conferences in the related fields.