back icon
close icon

Capture phrases in quotes for more specific queries (e.g. "rocket ship" or "Fred Lynn")

Chapter  May 1, 2015

Anomaly Detection and Diagnosis for Automatic Radio Network Verification

SRI Authors Ulf Lindqvist

Citation

COPY

Ciocarlie, G. F., Connolly, C., Cheng, C.-C., Lindqvist, U., Nov·czki, S., Sanneck, H., & Naseer-ul-Islam, M. (2015). Anomaly detection and diagnosis for automatic radio network verification. In R. Ag¸ero, T. Zinner, R. Goleva, A. Timm-Giel & P. Tran-Gia (Eds.), Mobile Networks and Management (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Vol. 141, pp. 163-176): Springer International Publishing.

Abstract

The concept known as Self-Organizing Networks (SON) has been developed for modern radio networks that deliver mobile broadband capabilities. In such highly complex and dynamic networks, changes to the configuration management (CM) parameters for network elements could have unintended effects on network performance and stability. To minimize unintended effects, the coordination of configuration changes before they are carried out and the verification of their effects in a timely manner are crucial. This paper focuses on the verification problem, proposing a novel framework that uses anomaly detection and diagnosis techniques that operate within a specified spatial scope. The aim is to detect any anomaly, which may indicate actual degradations due to any external or system-internal condition and also to characterize the state of the network and thereby determine whether the CM changes negatively impacted the network state. The results, generated using real cellular network data, suggest that the proposed verification framework automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions.

How can we help?

Once you hit send…

We’ll match your inquiry to the person who can best help you. Expect a response within 48 hours.

Our Privacy Policy