![](bg5.png)
ing the business aspects of this approach, for example,
the MNOs need to share their QoS prediction data ei-
ther to each other or to a third party. Additionally,
an MNO might not be forthcoming with our proposed
idea due to data trust or privacy concerns. Even if this
could be resolved by SLAs or similar, the agreements
might be quite complicated, and so will be the service
tariffs and business models.
5 CONCLUSIONS
Making predictions about the performance and hence
the available QoS for a cellular mobile network is a
challenging task because a network serves thousands
of users at a time and the users have varying mobil-
ity and network usage. This task becomes even more
complex if one tries to make similar predictions about
a multitude of such networks because each network
has its own characteristics (e.g. capacity, topology,
maintenance schedule, roaming agreements) as well
as its own set of users. Thus, providing predictive-
QoS in a multi-MNO environment is very challeng-
ing.
In this paper, we propose a novel approach for
multi-MNO P-QoS management that focuses on pro-
viding a vehicle with best possible QoS from mul-
tiple MNOs operating within vehicle’s range. This
approach also ensures a more uniform data-traffic
load distribution among the MNO networks. This
is achieved by introducing a dedicated ’Communica-
tion Decision Entity’ (CDE) for a vehicle or fleet of
vehicles that collects QoS relevant data for multiple
MNOs and makes the connectivity decisions for the
vehicle(s). On one hand, this approach acts in the
best interest of the vehicle(s), on the other hand, it
can alleviate over-loading problems for the involved
MNOs. We envision multiple configurations for CDE
to suit different network configurations or business
paradigms.
In future, we plan to provide simulation-based ev-
idence for this approach. We also intend to work
on analyzing the challenges of adopting such an ap-
proach in real-life environment.
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