TY - GEN
T1 - Demo
T2 - 2014 11th International Symposium on Wireless Communications Systems, ISWCS 2014
AU - Ciocarlie, Gabriela F.
AU - Cheng, Chih Chieh
AU - Connolly, Christopher
AU - Lindqvist, Ulf
AU - Nitz, Kenneth
AU - Nováczki, Szabolcs
AU - Sanneck, Henning
AU - Naseer-Ul-Islam, Muhammad
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/21
Y1 - 2014/10/21
N2 - The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. Changes to the configuration management (CM) parameters for network elements could be a cause for degraded network performance and stability; hence, the verification of their effects becomes crucial. In this paper, we present SONVer, a tool that performs SON verification, using anomaly detection and diagnosis techniques that operate within a specified spatial scope larger than an individual cell [1]. SONVer automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions. SONVer uses Key Performance Indicators (KPIs) and CM history from real cellular networks to determine the state of the network; visualize anomalies at a large scale; and identify the causes of anomalies and the group of cells that were affected.
AB - The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. Changes to the configuration management (CM) parameters for network elements could be a cause for degraded network performance and stability; hence, the verification of their effects becomes crucial. In this paper, we present SONVer, a tool that performs SON verification, using anomaly detection and diagnosis techniques that operate within a specified spatial scope larger than an individual cell [1]. SONVer automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions. SONVer uses Key Performance Indicators (KPIs) and CM history from real cellular networks to determine the state of the network; visualize anomalies at a large scale; and identify the causes of anomalies and the group of cells that were affected.
KW - anomaly detection
KW - diagnosis
KW - network automation
KW - self-organized networks (SON)
KW - SON verification
UR - https://www.scopus.com/pages/publications/84911960610
UR - https://www.scopus.com/pages/publications/84911960610#tab=citedBy
U2 - 10.1109/ISWCS.2014.6933426
DO - 10.1109/ISWCS.2014.6933426
M3 - Conference contribution
AN - SCOPUS:84911960610
T3 - 2014 11th International Symposium on Wireless Communications Systems, ISWCS 2014 - Proceedings
SP - 611
EP - 612
BT - 2014 11th International Symposium on Wireless Communications Systems, ISWCS 2014 - Proceedings
Y2 - 26 August 2014 through 29 August 2014
ER -