Measurement based statistical channel modeling 229 670 -8 Doppler frequency in H of paths v meters of the paths are estimated 10 dB No.of clusters 6 8.3.3 Experimental results for path clustering C时酸尚。 o Czink(2007).We would like to verify ne scena macroce to one cluster.We can observe that,the clusters are more well-sep rated in the delay and Dopple frequency do domanbeiogoaiferendstersasepeced,PathshiedinhecenierofheEoAAoAigueaepltnodstes nich arelso delay domains.This clus ing res .This is due to the limited number of custers selected in Fig.8.7 depicts the convergence rate of the lu ers spreh ds in d Doppler ters sprea stabilize.This indicates that the KPower-mean approach does exhibit an excellent convergence property.Measurement based statistical channel modeling 229 −10 −8 −6 −4 −2 0 2 4 6 0 0.5 1 1.5 2 2.5 3 3.5 x 10−9 Doppler frequency in Hz Amplitude in linear Fig. 8.5 Example of the powers of paths versus the Doppler frequencies of the paths. The parameters of the paths are estimated from measurement data collected in a macro suburban environment. The Tx moves at a pedestrian speed, following the first route. Table 8.1 Parameter setting in the SAGE algorithm and in the clustering algorithm. No. of paths 20 No. of iterations 5 Dynamic range 10 dB No. of bursts in one segment 3 No. of clusters 6 8.3.3 Experimental results for path clustering We have determined that one data segment is composed of 3 consecutive bursts. The SAGE algorithm was used to process the Oulu channel measurement data. The path estimates were grouped using the MCD-based approach as described in Czink (2007). Notice that the parameter ζ is heuristically pre-defined to be 5 according to Czink (2007). We would like to verify how the clustering algorithm performs when ζ takes different values. In the following, we select the data collected in the scenario “macrocell, suburban, Pedestrian route 1” and choose ζτ = ζν = 5 and 8 for path clustering respectively. Here, ζτ and ζν denote the weighting factor, i.e. a multiplicative factor, for the distance in delay and in Doppler frequency respectively. Table 8.1 depicts the setting used in the SAGE algorithm for processing the data. Fig. 8.6 depicts the constellation of paths and the clustering results when ζτ = ζν = 1, 5, 8, 20 is selected respectively. The paths with same color belong to one cluster. We can observe that when ζ increases, the clusters are more well-separated in the delay and Doppler frequency domains. From these results, we see that ζτ = ζν = 5 to 8 is more reasonable. From Fig. 8.6 it can be observed that for ζτ = ζν = 5 or 8, the paths which can be well separated in the EoA-AoA domain belong to different clusters as expected. Paths mixed in the center of the EoA-AoA figure are split into clusters which are well separated in the Doppler frequency and delay domains. This clustering result looks quite promising. We can also observe some outliers in the EoA-AoA domain that are assigned to clusters whose centroid are far from the outliers’ locations. This is due to the limited number of clusters selected in the algorithm. Fig. 8.7 depicts the convergence rate of the clusters’ spreads in delay, Doppler frequency, azimuth and elevation of arrival, versus the iteration number. It can be observed that after 5 iterations, the values of the clusters’ spreads stabilize. This indicates that the KPower-mean approach does exhibit an excellent convergence property