A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks (original) (raw)
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2019 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS), 2019
With the ever-increasing rise of a wide range of data-driven applications and services, as well as the synergies of gigabit wireless connectivity and pervasive broadband connectivity, there is a need for a paradigm shift in network methodologies to develop and deploy networks, such as 5G wireless. User-centric approaches to implementing self-organizing networks (SON) using machine learning (ML) have the potential to address the above challenges for 5G wireless communications networks and provide a seamlessly connected eco-system with superior user experience. This paper focuses on the potential performance improvements that can be achieved by integrating self-organizing networks and machine learning using user-centric approaches, with a focus on self-healing and self-optimizing SON functions.
From 4G to 5G: Self-organized Network Management meets Machine Learning
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.
Application of Self-Organizing Map to Intelligent Analysis of Cellular Networks
2013
In this work, the efficacy and scalability of the self-organizing map (SOM) algorithm, which is a class of artificial neural network (ANN), over traditional methods of analyzing cellular network variables was shown using key performance indicators (KPIs) data collected from an operational network service provider in Nigeria. Performance trends of various cells over a period of time were evaluated and rules of significance measure extracted which could form the basis for network optimization.
A Survey of Self-Organizing Networks
2016
One of the main new technologies in future generation networks is that of the automation of their organization. These networks are called Self-Organizing Networks (SON). SON functions are responsible for automatically planning, configuring, managing, optimizing and healing a mobile network. Well-designed and efficient SON functions are able to achieve and maintain high levels of network performance by continuously finding improvement patterns that may not be easily distinguishable to an expert. This is done so via the modification of various network parameters and by using rollback algorithms. These operations can be performed efficiently due to the availability of rich statistical models on Key Performance Indicators (KPI), their dependencies on one another and their interactions with each other. However, SON implementation is not easy. SON functions need to be specifically tuned to each individual network. Correct parameters need to be used which comply with the existing network p...
Transport and Telecommunication Journal
The application of SON algorithms for automating the processes of operating fourth-generation mobile networks based on the networks of operation, administration and management of OAM (Operation and Maintenance) is considered. The features of the tasks at the stages of self-optimization and self-configuration of the network for the various stages of 4G mobile network life cycle are shown. Criteria and approaches to assessing the effectiveness of solving problems by the SON network are proposed. The technical requirements are also formulated for SON algorithms. The experimentally achieved values of the selected performance exponents depending on the duration of the test cluster self-optimization time of the 4G network are shown.
Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
Applied Sciences
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 ce...
Self-Organizing Networks for 5G and Beyond: A View from the Top
Future Internet, 2022
We describe self-organizing network (SON) concepts and architectures and their potential to play a central role in 5G deployment and next-generation networks. Our focus is on the basic SON use case applied to radio access networks (RAN), which is self-optimization. We analyze SON applications’ rationale and operation, the design and dimensioning of SON systems, possible deficiencies and conflicts that occur through the parallel operation of functions, and describe the strong reliance on machine learning (ML) and artificial intelligence (AI). Moreover, we present and comment on very recent proposals for SON deployment in 5G networks. Typical examples include the binding of SON systems with techniques such as Network Function Virtualization (NFV), Cloud RAN (C-RAN), Ultra-Reliable Low Latency Communications (URLLC), massive Machine-Type Communication (mMTC) for IoT, and automated backhauling, which lead the way towards the adoption of SON techniques in Beyond 5G (B5G) networks.
International Journal of Wireless & Mobile Networks, 2019
Existing mobile networking systems lack the level of intelligence, scalability, and autonomous adaptability required to optimally enable next-generation networks like 5G and beyond, which are expected to be Self-Organizing Networks (SONs). It is anticipated that machine learning (ML) will be instrumental in designing future "x"G SON networks with their demanding Quality of Experience (QoE) requirements. This paper evaluates a methodology that uses supervised machine learning to predict the QoE level of the end user experiences and uses this information to detect anomalous behavior of dysfunctional network nodes (eNodeBs/base stations) in self-organizing mobile networks. An end-to-end network scenario is created using the network simulator ns-3, where end users interact with a remote host that is accessed over the Internet to run the most commonly used applications like file downloads and uploads and the resulting output is used as a dataset to implement ML algorithms for QoE prediction and eNodeB (eNB) anomaly detection. Three ML algorithms were implemented and compared to study their effectiveness and the scalability of the methodology. In the test network, an accuracy score greater than 99% is achieved using the ML algorithms. As suggested by the ns-3 simulation the use of ML for QoE prediction will help network operators understand end-user needs and identify network elements that are failing and need attention and recovery.
A Survey of Self Organisation in Future Cellular Networks
IEEE Communications Surveys & Tutorials, 2000
This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks.