Self-coordination of parameter conflicts in D-SON architectures: a Markov decision process framework

Moysen, Jessica; Giupponi, Lorenza

doi:10.1186/s13638-015-0312-7

Cited by 14 publications

(5 citation statements)

References 27 publications

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“…Decision trees have been proposed in [186] to properly adjust Remote Electrical Tilt (RET) and transmission power. Additionally, in [179] the authors provide a functional architecture that can be used to deal with the conflicts generated by the concurrent execution of multiple SON functions. They show that the proposed approach is general enough to model all the SON functions and their derived conflicts.…”

Section: ) Son Conflicts Coordinationmentioning

confidence: 99%

From 4G to 5G: Self-organized network management meets machine learning

Moysen

Giupponi

2018

Computer Communications

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174

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In this paper, we provide an analysis of selforganized network management, with an end-to-end perspective of the network. Self-organization as applied to cellular networks is usually referred to Self-organizing Networks (SONs), and it is a key driver for improving Operations, Administration, and Maintenance (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how network management can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyse the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.

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Section: ) Son Conflicts Coordinationmentioning

confidence: 99%

From 4G to 5G: Self-organized network management meets machine learning

Moysen

Giupponi

2018

Computer Communications

Self Cite

174

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“…Examples of solutions for the self-coordination problem can be found in [6], [7]. In [6], the authors focus on a preventive coordination mechanism that uses a policy-based decision process.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

“…However, in this solution, they address the coordination of a single SON function among the different cells. In [7], the authors propose a solution for the concurrent execution of multiple SON functions. They demonstrate that the proposed approach is able to coordinate several SON functions by taking advantage of game theory.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Conflict Resolution in Mobile Networks: A Self-Coordination Framework Based on Non-Dominated Solutions and Machine Learning for Data Analytics [Application Notes]

Moysen

García-Lozano

Giupponi

et al. 2018

IEEE Comput. Intell. Mag.

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Self-organizing network (SON) is a well-known term used to describe an autonomous cellular network. SON functionalities aim at improving network operational tasks through the capability to configure, optimize and heal itself. However, as the deployment of independent SON functions increases, the number of dependencies between them also grows. This work proposes a tool for efficient conflict resolution based on network performance predictions. Unlike other state-of-the-art solutions, the proposed self-coordination framework guarantees the right selection of network operation even if conflicting SON functions are running in parallel. This self-coordination is based on the history of network measurements, Corresponding Author: Jessica Moysen (Email: jessica.moysen@tsc.upc.edu) which helps to optimize conflicting objectives with low computational complexity. To do this, machine learning (ML) is used to build a predictive model, and then we solve the SON conflict by optimizing more than one objective function simultaneously. Without loss of generality, we present an analysis of how the proposed scheme provides a solution to deal with the potential conflicts between two of the most important SON functions in the context of mobility, namely mobility load balancing (MLB) and mobility robustness optimization (MRO), which require the updating of the same set of handover parameters. The proposed scheme allows fast performance evaluations when the optimization is running. This is done by shifting the complexity to the creation of a prediction model that uses historical data and that allows to anticipate the network performance. The simulation results demonstrate the ability of the proposed scheme to find a compromise among conflicting actions, and show it is possible to improve the overall system throughput.

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“…The paper "Self-coordination of parameter conflicts in D-SON architectures: A Markov Decision Process Framework" by Moysen et al [12] addresses the conflict issue between concurrent self-organizing network (SON) functions. A general functional architecture and a theoretical framework based on the theory of Markov decision process (MDP)s is proposed for the self-coordination of different actions taken by different SON functions.…”

Section: Coordination In Small Cell Networkmentioning

confidence: 99%

Technical advances in the design and deployment of future heterogeneous networks

Kliks

Pérez‐Romero

Boukhatem

et al. 2015

J Wireless Com Network

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The trend in wireless communications systems is the enhancement of the network infrastructure with the introduction of small cells, where a specific geographical area is served by low-range, low-power access points. The result is the creation of a heterogeneous topology where macrocells coexist with a variety of small-cell types. In this editorial article we briefly summarize the recent technical advances in the design and deployment of future heterogeneous networks addressed in the papers that compose this special issue. In particular the following aspects are considered: the design of interference and radio resource management algorithms, the analysis of the energy efficiency and power control issues in heterogeneous networks, the concept of coordination in small cell networks, key backhaul aspects of HetNets, deployment issues and overall management strategies.

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Self-coordination of parameter conflicts in D-SON architectures: a Markov decision process framework

Cited by 14 publications

References 27 publications

From 4G to 5G: Self-organized network management meets machine learning

From 4G to 5G: Self-organized network management meets machine learning

Conflict Resolution in Mobile Networks: A Self-Coordination Framework Based on Non-Dominated Solutions and Machine Learning for Data Analytics [Application Notes]

Technical advances in the design and deployment of future heterogeneous networks

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