@Article{10798587.2017.1312893,
AUTHOR = {Amin Mohajer, Morteza Barari, Houman Zarrabi},
TITLE = {Big Data Based Self-optimization Networking: A Novel Approach Beyond Cognition},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {2},
PAGES = {413--420},
URL = {http://www.techscience.com/iasc/v24n2/39766},
ISSN = {2326-005X},
ABSTRACT = {It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation 
wireless networks. A self-optimization technique is introduced as the only viable solution for controlling 
and managing this type of huge data networks. This technique allows control of resources and key 
performance indicators without human intervention, based solely on the network intelligence. The 
present study proposes a big data based self optimization networking (BD-SON) model for wireless 
networks in which the KPI parameters affecting the QoS are assumed to be controlled through a multidimensional decision-making process. Also, Resource Management Center (RMC) was used to allocate 
the required resources to each part of the network based on made decision in SON engine, which 
can satisfy QoS constraints of a multicast session in which satisfying interference constraints is the 
main challenge. A load-balanced gradient power allocation (L-GPA) scheme was also applied for the 
QoS-aware multicast model to accommodate the effect of transmission power level based on link 
capacity requirements. Experimental results confirm that the proposed power allocation techniques 
considerably increase the chances of finding an optimal solution. Also, results confirm that proposed 
model achieves significant gain in terms of quality of service and capacity along with low complexity 
and load balancing optimality in the network.},
DOI = {10.1080/10798587.2017.1312893}
}