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A. Gerdenitsch:
"System Capacity Optimization of UMTS FDD Networks";
Supervisor, Reviewer: E. Bonek, G. Raidl; Institut für Nachrichtentechnik und Hochfrequenztechnik, 2004.

In this thesis I investigate the problem of capacity optimization in UMTS FDD networks. The goal is to improve the capacity of the network, measured as served users, only by changing the base station parameters. The focus is on the optimization of antenna tilt and common pilot channel (CPICH) power of the base stations. These parameter adjustments improve the UMTS radio network capacity by means of reducing inter-cell interference, achieve cell load sharing, and optimize base station power resources.

Altogether five different algorithms for finding the best settings of antenna tilt and CPICH power are presented. The first three optimization algorithms, Rule Based Approach, Simulated Annealing and Adaptive Rule Based Approach, are local techniques. Furthermore, a global technique, the Genetic Algorithm, will be presented. Also, an Analytic Optimization Algorithm will be discussed.

The fitness function used for the algorithms considers the number of served users as the main optimization goal. For the Genetic Algorithm I use a fitness function that additionally also considers coverage and soft handover.

First, the Rule Based Approach is adressed. The optimization process is characterized by reducing the CPICH power and increasing the antenna downtilt in the individual cells according to a configurable rule set. Subsequently, this algorithm is extended by incorporating Simulated Annealing. Here, the decision whether to take a worse result is, in contrast to the first method, independent of the rule set. The third local algorithm is also a further development of the Rule Based Approach. The main difference between the Adaptive Rule Based Approach and the other two local approaches is that CPICH power and antenna tilt are changed together, and that also an increase of CPICH power and antenna up tilting is possible during the optimization process.

Further, a Genetic Algorithm is introduced which I improved by taking operators that are adapted for the UMTS capacity optimization problem by taking into account the quality of the network. In addition, a local optimization is included to improve the performance.

Finally, I address an Analytical Optimization Algorithm. Beside antenna tilt and CPICH power settings, this algorithm optimizes also the antenna azimuth.

The performance of the algorithms is evaluated using a static UMTS FDD network simulator on two virtualscenarios of a typical European city. In the first scenario the network covers the whole area of the city. The second scenario only spans across downtown.

With the different algorithms, I show improvements in capacity of up to 105% compared to the initial settings. The Genetic Algorithm performs best, but with the drawback of a high computation time. If we compare the three local optimization techniques, Rule Based Approach, Simulated Annealing and Adaptive Rule Based Approach, we see that the Adaptive Rule Based Approach achieves the highest improvement. The computation effort for all three algorithms is approximately the same. The Analytic Optimization Algorithm shows, with only five network evaluations, almost the same optimization result as the local algorithms.