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ردیف | عنوان | نوع |
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1 |
Big Data Analytics, Machine Learning and Artificial Intelligence in Next-Generation Wireless Networks
تحلیل داده های بزرگ، یادگیری ماشین و هوش مصنوعی در شبکه های بی سیم نسل بعدی-2018 The next-generation wireless networks are evolving into very complex systems because of the very diversified
service requirements, heterogeneity in applications, devices, and
networks. The network operators need to make the best use of
the available resources, for example, power, spectrum, as well as
infrastructures. Traditional networking approaches, i.e., reactive,
centrally-managed, one-size-fits-all approaches and conventional
data analysis tools that have limited capability (space and time)
are not competent anymore and cannot satisfy and serve that
future complex networks regarding operation and optimization
cost-effectively. A novel paradigm of proactive, self-aware, selfadaptive and predictive networking is much needed. The network
operators have access to large amounts of data, especially from
the network and the subscribers. Systematic exploitation of the
big data dramatically helps in making the system smart, intelligent, and facilitates efficient as well as cost-effective operation and
optimization. We envision data-driven next-generation wireless
networks, where the network operators employ advanced data
analytics, machine learning and artificial intelligence. We discuss
the data sources and strong drivers for the adoption of the data
analytics, and the role of machine learning, artificial intelligence
in making the system intelligent regarding being self-aware, selfadaptive, proactive and prescriptive. A set of network design and
optimization schemes are presented concerning data analytics.
The paper concludes with a discussion of challenges and benefits
of adopting big data analytics, machine learning, and artificial
intelligence in the next-generation communication systems.
Index Terms: Big data analytics, Machine learning, Artificial intelligence, Next-generation wireless. |
مقاله انگلیسی |
2 |
Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network
تجزیه و تحلیل داده بزرگ برای تجزیه و تحلیل کاربری فعالیت و تشخیص ناهنجاری کاربر در شبکه های بی سیم سیار-2017 The next generation wireless networks are expected to
operate in fully automated fashion to meet the burgeoning
capacity demand and to serve users with superior quality of
experience. Mobile wireless networks can leverage spatiotemporal information about user and network condition to
embed the system with end-to-end visibility and intelligence. Big
data analytics has emerged as a promising approach to unearth
meaningful insights and to build artificially intelligent models
with assistance of machine learning tools. Utilizing
aforementioned tools and techniques, this paper contributes in
two ways. First, we utilize mobile network data (big data) – call
detail record (CDR) – to analyze anomalous behavior of mobile
wireless network. For anomaly detection purposes, we use
unsupervised clustering techniques namely k-means clustering
and hierarchical clustering. We compare the detected anomalies
with ground truth information to verify their correctness. From
the comparative analysis, we observe that when the network
experiences abruptly high (unusual) traffic demand at any
location and time, it identifies that as anomaly. This helps in
identifying regions of interest (RoI) in the network for special
action such as resource allocation, fault avoidance solution etc.
Second, we train a neural-network based prediction model with
anomalous and anomaly-free data to highlight the effect of
anomalies in data while training/building intelligent models. In
this phase, we transform our anomalous data to anomaly-free
and we observe that the error in prediction while training the
model with anomaly-free data has largely decreased as compared
to the case when the model was trained with anomalous data.
Index Terms: Next generation wireless networks | 5G | Anomaly detection | call detail record | machine learning | network analytics | network behavior analysis | wireless cellular network |
مقاله انگلیسی |