and pico eNodeBs. A three-dimensional digital map of the area is used, which contains information of the building locations (and their dimensions), streets, parks, open, squares and SO on. The macro eNodeBs are deployed to provide wide area blanket coverage, while the picos are installed in local areas with high traffic density, where the macros have difficulties carrying all the offered traffic during peak hours. An advanced ray tracing tool is used to compute the radio propagation characteristics between each transmitter and receiver point, taking into account the environment characteristics from the three-dimensional map of the metropolitan area. Similarly as for the 3GPP-defined scenarios, a bursty traffic model is applied, where the arrival of new calls is according to Poisson process, assuming a finite payload per user. The spatial traffic distribution is, however, highly non-uniform in line with observations from the considered area. In essence, the considered dense urban metropolitan area is rather irregular in coherence with real-life operator installations, meaning that propagation characteristics vary significantly depending on localized environment, the inter-site distance between macro cells varies from approximately 100 meters up to several hundred meters. Similarly, the placement of picos is also irregular, SO the equivalent number of picos per macro cell area varies from zero up to seven for the considered use case. The performance gain of eICIC therefore varies significantly from one localized area to another in the metropole. In order to illustrate the former, we have analysed the eICIC in four different local areas, denoted Area 1-4. Area 1 corresponds to the coverage of one macro cell with four distantly placed picos; Area 2 is illuminated by 4 macro cells and containing 11 picos; Area 3 has similar characteristics as Area 1; while Area 4 has six picos deployed in the coverage area LTE Small Cell Optimization 5%-ile throughput gain in each region Static eICIC configuration Dynamic eICIC config