Self-optimization of LTE networks utilizing Celnet Xplorer

In order to meet demanding performance objectives in Long Term Evolution (LTE) networks, it is mandatory to implement highly efficient, autonomic self-optimization and configuration processes. Self-optimization processes have already been studied in second generation (2G) and third generation (3G) n...

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Bibliographic Details
Published in:Bell Labs technical journal 2010-12, Vol.15 (3), p.99-117
Main Authors: Buvaneswari, Arumugam, Drabeck, Lawrence, Nithi, Nachi, Haner, Mark, Polakos, Paul, Sawkar, Chitra
Format: Article
Language:English
Subjects:
Architecture
Engines
Networks
Optimization
Radio
Reproduction
Tuning
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Summary:In order to meet demanding performance objectives in Long Term Evolution (LTE) networks, it is mandatory to implement highly efficient, autonomic self-optimization and configuration processes. Self-optimization processes have already been studied in second generation (2G) and third generation (3G) networks, typically with the objective of improving radio coverage and channel capacity. The 3rd Generation Partnership Project (3GPP) standard for LTE self-organization of networks (SON) provides guidelines on self-configuration of physical cell ID and neighbor relation function and self-optimization for mobility robustness, load balancing, and inter-cell interference reduction. While these are very important from an optimization perspective of local phenomenon (i.e., the eNodeB's interaction with its neighbors), it is also essential to architect control algorithms to optimize the network as a whole. In this paper, we propose a Celnet Xplorer-based SON architecture that allows detailed analysis of network performance combined with a SON control engine to optimize the LTE network. The network performance data is obtained in two stages. In the first stage, data is acquired through intelligent non-intrusive monitoring of the standard interfaces of the Evolved UMTS Terrestrial Radio Access Network (E-UTRAN) and Evolved Packet Core (EPC), coupled with reports from a software client running in the eNodeBs. In the second stage, powerful data analysis is performed on this data, which is then utilized as input for the SON engine. Use cases involving tracking area optimization, dynamic bearer profile reconfiguration, and tuning of network-wide coverage and capacity parameters are presented.
ISSN:1089-7089
1538-7305
1538-7305
DOI:10.1002/bltj.20459