Maciej J. Nawrocki

Improved Modeling of Highly Loaded UMTS Network with Nonnegative Constraints

In this paper, we deal with the power control problem in a non-uniform and highly loaded UMTS network with omnidirectional and smart antennas. If the pole capacity limit in a cell is exceeded no reasonable solution to the power control problem can be obtained with the methods such as the Gaussian elimination, Jacobi, Gauss-Seidel, SOR, or some Krylov subspace projection methods. In such a case, some users are usually removed from the system. In our approach, we solve a non-negatively constrained least square problem with very efficient gradient based linear solvers. This allows us to approximately estimate the powers of all the users even though a pole capacity is slightly affected in some cells. It can be implemented in radio resource management as an effective solution for networks in overload state. Additionally, to reduce a complexity of computation, we extend the dimension reduction technique proposed by Mendo and Hernando to the case of smart antennas. Our considerations are supported with some simulations based on a UMTS network model

On cost function analysis for antenna optimization in UMTS networks

The paper presents a detailed analysis of the properties of the cost function vital to WCDMA radio network optimization. The cost function for the downlink is carefully selected and analyzed numerically for three representative small network layouts and antenna orientation parameters (azimuth and tilt). The analysis includes local minima search, their distribution and their attraction regions. Based on obtained results, several notable conclusions about optimal solutions for network configuration are made as well as some directives for proper optimization algorithm design suggested

Application of linear solvers to UMTS network optimization without and with smart antennas

The paper presents a brief survey of the methods for solving systems of linear equations (linear solvers), which could be useful in application to WCDMA network optimization. The methods are scored with respect to their computational cost when base stations are equipped with traditional antennas as well as with smart antennas. Preconditioning in the conjugate gradient square (CGS) method is discussed. We also analyze the technique, which without any loss of accuracy, may reduce dimensions of the power control problem in a multi-service network but is limited only to traditional antennas the smart antenna case must be solved with conventional solver. Theoretical considerations are confirmed experimentally

Transformation Function for Analytical Optimisation of CDMA Networks

In this paper a novel approach to the analytical optimization of CDMA networks is presented. The approach is based on a transformation of the CDMA real network into an ideal one using a Transformation Function (TF). This function allows us to optimize the performance of the real CDMA network analytically. The proposed approach is a purely analytical method and therefore assures deeper understanding of the optimization problem. Such approach seems to be a good alternative for the MORANS initiative, since the transfer function allows us to easy analyze and compare various network scenarios without the need for defining any sophisticated reference scenarios.

An Analytical Optimization of CDMA Networks

This paper presents innovative method for analytical optimization CDMA networks. In contrast to known approaches, presented method is purely analytical therefore it allows proper problem understanding. The presented method allows for finding direct relation between every general system scenario (GSS) and easy to optimize simplified system scenario (SSS). The knowledge about mentioned direct relation made us capable of taking the advantage of optimality conditions easy achievable under SSS to optimize GSS. This direct relation is named as transformation function (TF). In transformation process, each real mobile user is substituted by a number of virtual ones. Presented method, through its analytical nature, ensures optimality of obtained solution.