Over the past few years, indoor cellular deployments
have been on the rise. These scenarios are characterized by their
user density and fast-changing conditions, thus, being prone to
failures. Moreover, the steady development of indoor and outdoor
positioning techniques is expected to provide a reliable source
of information. Thus, the availability of user location is being
considered to be a key enabler to improve the resilience and
performance of automatic failure management and optimization
techniques. Taking this into consideration, the present work
proposes a semi-supervised location-aware anomaly detection
method for the management of failures such as cell outages and
interference problems.
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