【文件属性】:
文件名称:An Automatic Detection and Diagnosis Framework
文件大小:471KB
文件格式:PDF
更新时间:2018-03-30 09:23:17
self-healing
As the complexity of commercial cellular networks
grows, there is an increasing need for automated methods detecting
and diagnosing cells not only in complete outage but with
degraded performance as well. Root cause analysis of the detected
anomalies can be tedious and currently carried out mostly
manually if at all; in most practical cases, operators simply reset
problematic cells. In this paper, a novel integrated detection and
diagnosis framework is presented that can identify anomalies and
find the most probable root cause of not only severe problems
but even smaller degradations as well. Detecting an anomaly is
based on monitoring radio measurements and other performance
indicators and comparing them to their usual behavior captured
by profiles, which are also automatically built without the need
for thresholding or manual calibration. Diagnosis is based on
reports of previous fault cases by identifying and learning their
characteristic impact on different performance indicators. The
designed framework has been evaluated with proof-of-concept
simulations including artificial faults in an LTE system. Results
show the feasibility of the framework for providing the correct
root cause of anomalies and possibly ranking the problems by
their severity.