On the advantages of location resolved input data for throughput optimization algorithms in self-organizing wireless networks

Performing and evaluating Call Trace (CT) or also called Minimization of Drive Test (MDT) measurements entails additional expenses to the operator as well as to the users. However, by using location specific network data obtained from the measurements as additional information for self-organization procedures, we expect these procedures to perform essentially better since they can take their decisions upon an improved knowledge base. In this paper, we investigate the performance gain induced by the use of location specific input data for self-organization procedures. For this purpose we propose, evaluate, and compare two force field based algorithms, which are dedicated to optimizing the coverage as well as the user throughput in a macro cell deployment with heterogeneous traffic demand. The results of this work confirm the expectations: It is shown that using the additional location specific information the self-organization procedure investigated leads to higher average throughput gains and an improved robustness in the sense that the procedure was able to successfully handle all scenarios investigated.