Clustering and modeling of wireless backhaul data traffic
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
To handle the future demands of mobile broadband, knowledge of user behavior is
of great importance. Knowing when and where user demand will be high allows
for better planning of spectrum and computational resources. To aid this, this
thesis investigates if the behavior of traffic throughput of wireless backhaul links
from a European operator can be segmented into different clusters. We use both
the k-means clustering algorithm and t-distributed stochastic neighbor embedding
to attain the clusters. No distinct patterns emerge from the data, which instead
appears to be uniformly spaced without clear boundaries.
To aid simulation of wireless links in future studies we also model the traffic behavior
of urban links. The correlation between the model parameters as well as the error
terms are calculated as a function of the geographical distance between the links.
This helps decide whether links in proximity behave similarly or not. We find
that the traffic on urban links has a similar shape but the model parameters are
not correlated with respect to the distance between them. The parameters can be
sampled from given distributions to generate synthetic traffic data.
Beskrivning
Ämne/nyckelord
machine learning, wireless backhaul, traffic model, clustering, k-means