A Novel Approach to Optimize Signaling Between Network Elements With Guaranteed Qos
With the advent of increase in demand for best Quality of Services (QoS) in the field of telecommunication,
especially with the oncoming trend of connected devices in Internet of Things (IoT) world and heavy data traffic with 5G
mobile communications, operators are forced to deal with a big task of maintaining QoS for their subscribers. Hence there is
a compelling need to bring in more sophisticated approaches toward satisfying end-users. More importantly, when it comes
to influencers, operators nowadays are progressively focusing on providing guaranteed QoS for a segment of high-valued
customers. Yet with the current mode of architecture pertaining to Operational and Business Support Solutions (OSS/BSS)
infrastructure, there is a necessity to bring in an innovative approach to solve this problem. In case of simply scaling up their
network components, their operational expenses are expected to shoot through the roof. In this paper, a novel approach is
proposed to address this problem by incorporating a new network element into the existing architecture in combination with
machine learning techniques; that would continually monitor the behavioral pattern of interested parties (influencers in this
case). Using the learned methodology, the immediate serving element shall be asked to cater asynchronously in providing
the end-users with state-of-the-art QoS (by decoupling charging interrogations toward BSS) and optimizing signaling costs
incurred between network elements at an operator’s infrastructure in parallel.
Index Terms— Guaranteed QoS, Signaling efficiency, Communication Optimizer, Machine Learning.