با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Semi-Supervised Learning Based Big Data-Driven Anomaly Detection in Mobile Wireless Networks
تشخیص ناهنجاری های رانده شده با داده های نیمه نظارت بر اساس داده ها در شبکه های بی سیم سیار-2018 With rising capacity demand in
mobile networks, the infrastructure is also becoming increasingly denser and complex. This
results in collection of larger amount of raw
data (big data) that is generated at different
levels of network architecture and is typically
underutilized. To unleash its full value, innovative machine learning algorithms need to be
utilized in order to extract valuable insights
which can be used for improving the overall
network’s performance. Additionally, a major
challenge for network operators is to cope up
with increasing number of complete (or partial) cell outages and to simultaneously reduce
operational expenditure. This paper contributes
towards the aforementioned problems by exploiting big data generated from the core network of 4G LTE-A to detect network’s anomalous behavior. We present a semi-supervised
statistical-based anomaly detection technique
to identify in time: first, unusually low user
activity region depicting sleeping cell, which
is a special case of cell outage; and second,
unusually high user traffic area corresponding
to a situation where special action such as
additional resource allocation, fault avoidance
solution etc. may be needed. Achieved results
demonstrate that the proposed method can be
used for timely and reliable anomaly detection
in current and future cellular networks.
Keywords: 5G; 4G LTE-A; anomaly detec tion; call detail record; machine learning; big data analytics; network behavior analysis; sleeping cell |
مقاله انگلیسی |