Adaptive SON management using KPI measurements

Lohmuller, Simon; Schmelz, Lars Christoph; Hahn, Soren

doi:10.1109/noms.2016.7502868

Cited by 11 publications

(12 citation statements)

References 7 publications

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“…They have considered context-specific KPI targets with priority levels in order to map the request to correct configurations. Lohmüller et al [85] have presented an adaptive SON function management mechanism that analyzes context-specific measurements in order to define the effect of a configuration set on the network. Moreover, Lohmüller et al [84] have studied whether the unknown configuration sets of SON functions can be estimated reliably.…”

Section: Characterizing Son Functions By Analyzing and Evaluating Thementioning

confidence: 99%

“…The future perspectives of autonomic network management consider SON management as an important building block [95,1,159]. Characterization of SON functions has been studied in view of the dynamic mapping of operator goals for SON function configurations [55,85]. Also, some approaches have enriched the predefined SON function models by gathering experience of their actual performance and impact on the network [85,84].…”

Section: Characterizing Son Functions By Analyzing and Evaluating Thementioning

confidence: 99%

“…Characterization of SON functions has been studied in view of the dynamic mapping of operator goals for SON function configurations [55,85]. Also, some approaches have enriched the predefined SON function models by gathering experience of their actual performance and impact on the network [85,84]. Other SON-related studies have specifically focused on the verification and coordination of the SON functions, either to avoid degradations [137,145,28] or to analyze joint effects [59,68,14,54] of the functions.…”

Section: Characterizing Son Functions By Analyzing and Evaluating Thementioning

confidence: 99%

See 2 more Smart Citations

Correlation-Based Feature Mapping of Crowdsourced LTE Data

Apajalahti

Walelgne

Manner

et al. 2018

2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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Section: Characterizing Son Functions By Analyzing and Evaluating Thementioning

confidence: 99%

Section: Characterizing Son Functions By Analyzing and Evaluating Thementioning

confidence: 99%

Section: Characterizing Son Functions By Analyzing and Evaluating Thementioning

confidence: 99%

See 1 more Smart Citation

Correlation-Based Feature Mapping of Crowdsourced LTE Data

Apajalahti

Walelgne

Manner

et al. 2018

2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

View full text Add to dashboard Cite

“…In [5,6] the authors propose an approach for PSBM based on the so called SON Function Models (SFM). SFM is a mapping between SCV sets and KPI outcomes of a certain SON function.…”

Section: Fig 1: Pbsm Functional Descriptionmentioning

confidence: 99%

Softwarized and distributed learning for SON management systems

Daher

Jemaa

Decreusefond

2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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Self-Organizing Networks (SON) functions have already proven to be useful for network operations. However, a higher automation level is required to make a network enabled with SON capabilities respond as a whole to the operator's objectives. For this purpose, a Policy Based SON Management (PBSM) layer has been proposed to manage the deployed SON functions. In this paper, we propose to empower the PBSM with cognition capability in order to manage efficiently SON enabled networks. We focus particularly on the implementation of such a Cognitive PBSM (C-PBSM) on a large scale network and propose a scalable approach based on distributed Reinforcement Learning (RL): RL agents are deployed on different clusters of the network. These clusters should be defined in such a way that the RL agents can learn independently. As the interaction between these clusters may evolve in time due for instance to traffic dynamics, we propose a flexible implementation of this C-PBSM framework with dynamic clustering to adapt to network's evolutions. We show how this flexible implementation is rendered possible under Software Defined Networks (SDN) framework. We also assess the performance of the proposed distributed learning approach on an LTE-A simulator.

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“…This modeling is performed through exhaustive simulations [3] and enhanced through network feedback [4]. We propose in this paper a different approach which does not model the individual SON functions behavior, but learns the most efficient global decision to be made for the the whole SON system directly from the real network, hence enabling PBSM with cognition capabilities.…”

Section: Towards Cognitive Son Managementmentioning

confidence: 99%

Q-Learning for Policy Based SON Management in wireless Access Networks

Daher

Jemaa

Decreusefond

2017

2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)

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Abstract-Self organized networks has been one of the first concrete implementations of autonomic network management concept. Currently, several Self-Organizing-Network (SON) functions are developed by Radio Access Network (RAN) vendors and already deployed in many networks all around the world. These functions have been designed independently to replace different operational tasks. The concern of making these functions work together in a coherent manner has been studied later in particular in SEMAFOUR project where a Policy Based SON Management (PBSM) framework has been proposed to holistically manage a SON enabled network, namely a network with several individual SON functions. Enriching this PBSM framework with cognition capability is the next step towards the realization of the initial promise of SON concept: a unique self-managed network that responds autonomously and efficiently to the operator high level requirements and objectives. This paper proposes a cognitive PBSM system that enhances the SON management decisions by learning from past experience using Q-learning approach. Our approach is evaluated by simulation on a SON enabled Long-Term Evolution Advanced (LTE-A) network with several SON functions. The paper shows that the decisions are enhanced during the learning process and discusses the implementation options of this solution.

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Adaptive SON management using KPI measurements

Cited by 11 publications

References 7 publications

Correlation-Based Feature Mapping of Crowdsourced LTE Data

Correlation-Based Feature Mapping of Crowdsourced LTE Data

Softwarized and distributed learning for SON management systems

Q-Learning for Policy Based SON Management in wireless Access Networks

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