Sentence examples for propagation graph from inspiring English sources
The propagation graph modeling approach has been applied to predicting channel IRs in[8] by exhaustively searching of the propagation paths connecting the transmitters and receivers.
Moreover, the above consistencies are obtained based on the appropriate settings of the parameters in propagation graph modeling, such as reflection gain and distribution of the scatterers.
A propagation graph can be generated by taking into account the geometry of the environment, the scatterers' distribution, the mobility, and visibility of the scatterers, as well as the scatterers' EM properties.
As few measurement data of DAS propagation channels are available, the propagation graph modeling based on the electromagnetic wave reverberation theory is used to generate the synthetic impulse responses (IRs) for the composite channels induced by the different groups of antennas in the DAS.
Then, the applicability of random propagation graphs in channel modeling was validated.
Totally, 12,384 IRs are generated using the propagation graphs for 1,376 UE locations, i.e., nine graphs per UE location.
The spatial cross-correlation of channel fadings in different links of a DAS has been investigated via simulations using stochastic propagation graphs.
To evaluate modeling accuracy using propagation graphs, two large-scale channel parameters, i.e., path loss and delay spread are chosen for comparison between the graph models and the standard WINNER II models.
In Figure VB in the Data Supplement, phase propagation graphs are somewhat sensitive to the initial directionality of the exiting wave, but the sequence of the phases is mainly determined by the spatial location of the pacing site; ie, V6 and V5 precede other precodials when pacing the lateral wall; V1 and V2 precede other precordials when pacing the septum.
The obtained 3D volume allows initializing the belief-propagation graph by attributing a set of possible labels (i.e., disparities) for each node (i.e., pixels).
This also suggests that simultaneously performing label propagation and graph learning is necessary and effective. .
