Towards proactive context-aware self-healing for 5G networks

Asghar, Muhammad Zeeshan; Nieminen, P.; Hämäläinen, Seppo; Ristaniemi, Tapani; Imran, Muhammad Ali; Hämäläinen, Timo D.

doi:10.1016/j.comnet.2017.04.053

Cited by 13 publications

(7 citation statements)

References 31 publications

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“…This process could add to much complexity and time-consuming operations where Machine learning algorithms and data analytic tools can be replaced for less complex process mapping crowded venue-locations. Also, in [20] the authors compare two classification algorithms called Multi-Layer Perceptron (MLP) and Classification and Regression Tree (CART) to establish the future crowded venue-locations. Other works propose a framework mechanism based on Big Data-driven (BDD) as in [21] and [22] demonstrating the new possible solutions to improve the performance of mobile networks toward 5G based on attributes of connections such as throughput and data volume, but the communication overhead and latency caused by using the data analytic have to be investigated to apply BDD optimization.…”

Section: A Source Data Analysismentioning

confidence: 99%

Social-Aware Load Balancing System for Crowds in Cellular Networks

et al. 2021

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Over time, load balancing systems in cellular networks have been key to avoiding overload problems in the network and to maintaining a correct resource allocation and performance. However, the classical approaches were not designed for the dynamism generated by user behavior. The big crowds of users at certain social venues are one of the main concerns of mobile operators due to the load imbalance generated. Additionally, the mobility of users who attend social events (e.g., sports events, concerts, etc.) greatly impacts network performance due to its high correlation with network traffic. The availability to inform about events, particularly regarding venue location (e.g., stadiums, concert halls, convention centers) is exponentially growing thanks to its proliferation in social networks through geolocation databases and other functionalities. Therefore, the present work proposes a novel load balancing system integrating a fuzzy logic controller algorithm with social-awareness, which considers the relative position between cell sites and the social event venue in order to configure the network parameters. This approach is evaluated for different configurations of load balancing methods simulated on an urban macro scenario, mitigating the impact of the number of users per cell without degrading the signal quality. In this way, results show that social event data information plus soft or aggressive transmission power changes in cells can help to maintain the balance in the number of users per cell during mass events.

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Section: A Source Data Analysismentioning

confidence: 99%

Social-Aware Load Balancing System for Crowds in Cellular Networks

et al. 2021

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“…ANM encompasses self-management and cognitive functionalities, such as self-awareness, self-configuration, self-optimization, and self-healing [22], addressing the ability of networks to be aware of themselves and their environment, and thus self-govern their behavior to achieve specific goals. Accordingly, collection, modeling, reasoning, and distribution of context in relation to sensor data play a critical role in ANM [23].…”

Section: Autonomic Network Managementmentioning

confidence: 99%

Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments

et al. 2019

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The demand for Autonomic Network Management (ANM) and optimization is as intense as ever, even though significant research has been devoted towards this direction. This paper addresses such need in Software Defined (SDR) based Cognitive Radio Networks (CRNs). We propose a new framework for ANM and network reconfiguration combining Software Defined Networks (SDN) with SDR via Network Function Virtualization (NFV) enabled Virtual Utility Functions (VUFs). This is the first approach combining ANM with SDR and SDN via NFV, demonstrating how these state-of-the-art technologies can be effectively combined to achieve reconfiguration flexibility, improved performance and efficient use of available resources. In order to show the feasibility of the proposed framework, we implemented its main functionalities in a cross-layer resource allocation mechanism for CRNs over real SDR testbeds provided by the Orchestration and Reconfiguration Control Architecture (ORCA) EU project. We demonstrate the efficacy of our framework, and based on the obtained results, we identify aspects that can be further investigated for improving the applicability and increasing performance of our broader framework.

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“…There has already been a lot of work done on the use of machine learning approaches in the cellular network domain [54][55][56][57][58][59]. In this section, we describe how SON and deep learning could play an important roles in jointly driving future cellular networks [60].…”

Section: Enabling 5g With Son and Deep Learningmentioning

confidence: 99%

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

et al. 2019

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In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of autoencoders (AE) for cell outage detection. First, we briefly introduce deep learning (DL) and also shed light on why it is a promising technique to make self organizing networks intelligent, cognitive, and intuitive so that they behave as fully self-configured, self-optimized, and self-healed cellular networks. The concept of SON is then explained with applications of intrusion detection and mobility load balancing. Our empirical study presents a framework for cell outage detection based on an autoencoder using simulated data obtained from a SON simulator. Finally, we provide a comparative analysis of the proposed framework with the existing frameworks.

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Towards proactive context-aware self-healing for 5G networks

Cited by 13 publications

References 31 publications

Social-Aware Load Balancing System for Crowds in Cellular Networks

Social-Aware Load Balancing System for Crowds in Cellular Networks

Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

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