Hi everyone, I'm Zhou Qin from Rutgers university
and today i'm going to present our work called the CellPred: a behavioral aware
scheme for cellular data usage prediction
There will be four different parts for this presentation. The first is
introduction. Let's go into that and the cellular networks as we all know is
ubiquitous in our daily life we can access it in serial networks for
multimedia and content such as music video and etc. if you're on
the road then you're gonna use the navigation for relatively long time
and we can also use cellular networks for
traditional businesses like the phone calls and
the messages and the data traffic prediction is
actually one of the major applications in several networks
previous work that they focus on cellular tower level
and in this work we approach the data traffic prediction from
individual level with the consideration of the user behaviors
and the contributions of this paper can be summarized as
we are the first study from individual perspective
on cellular traffic prediction and we consider user behaviors
from subscription tags which i will introduce later
and we propose this prediction framework by large datasets
which is cellular signaling data and we consider the individual mobility and the usage
and we designed this framework by
soft attention addressing behavioral tags. And in the end we
summarized some of the lessons learned through this investigation and together
with the limitation generalization and the privacy issues
the second part is data driven models so that's the architecture of the whole pipeline.
From the very beginning we have the
processing section which will process the signaling data into user
based traces then we filter outliers and do some other processing to have the
traces of mobility and the usages
by mobility I mean different locations and by usage i mean the data
traffic within a certain period of time and once we have those trips then we fit
those trips into the prediction model for future
location and the usage prediction and at the end
we aggregate all the individual level data traffic
into the cell tower level then it can be used for future
applications such as location service and network optimization etc
and that's the details of our modeling and there are three parts
of the attention encoder decoder on top of the standard encoder and the
decoder we have the soft attention addressing all the
behavior tags from different groups of users by considering those
encoded features of those texts then we have series of different
weights on different groups of users and by combining the features and
mobility features and the user features on different group of users and the
historic location and usage then we can better predict the future
location usage and in the end we aggregate the
individual level usage to sell power level
and let's go into the evaluation results and we have three different um
figures here representing the baseline comparison and the first figure is standing
for the detailed comparison of our cellpred together with other
baselines so during different time of day we have the MAPE
representing the accuracy of our of different methods. the lower the MAPE
the higher the accuracy so our CellPred achieves the better
performance compared to other baselines and the cellpred-wo here means the cellpred without
consideration of those features that we introduced, including
mobility features and usage features and the second figure
is corresponding to the first one but it's a bar chart figure and the last
one is the comparison between the cellpred
without mobility features and without usage
features respectively and we can see that
if we consider both features uh the performance is the best
um the next one is the contextual investigation. we
actually have a much more detailed
evaluations regarding cellpred right here we just
present two examples to show that we consider other contextual factors
the first one is the different regions so for different regions we consider the
cellpred downtown area and the suburban area and we see that the
cellpred performs slightly better in those
suburban areas because one reason may be that cell towers are
far away from each other in the suburban area
well in the downtown it's relatively harder to predict
which cell tower the user will be connecting to
and we also investigate the different day of week so basically we separated the week into weekends and
weekdays so we see that during um
the first 12 hours the performance is relatively comparable and
uh for the rest of the day it's actually weekend we can do we can do
better than the weekday and let's go into the discussion last
so we are the first to use a perspective investigation on cellular data traffic
prediction and we're using 3 million
cell phones and 23 000 cell towers to design a data driven
solution by considering behaviors and we're also
quantifying the impacts of different contextual factors
so for more details please refer to our paper and you can
also access those two websites for more information
thank you