Network Management and Orchestration Using Artificial Intelligence: Overview of ETSI ENI

Wang, Yue; Forbes, Ray; Cavigioli, Chris; Hai-ning, Wang; Gamelas, Antonio; Wade, Archie; Strassner, John; Cai, Shengming; Liu, Shucheng

doi:10.1109/mcomstd.2018.1800033

Cited by 36 publications

(27 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the allocation of virtual network resources, the management and orchestration of the VNFs in a multi-tenant environment require cuttingedge tools and promising solutions to be addressed, such as the AI techniques [58] and ML algorithms [98]. In this context, the European Telecommunication Standards Institute (ETSI) Industry Specification Group (ISG) introduced the Experiential Network Intelligence (ENI) working group in order to improve the experience of network operators and add value to the teleco provided services [99]. The main objectives of the ENI is to exploit AI and ML techniques in order to adjust the VNFs of the networked services based on dynamic changes in the requirements of the end users, the conditions of the environments, and the goals of the business.…”

Section: ) Softwarization and Virtualizationmentioning

confidence: 99%

The Road Towards 6G: A Comprehensive Survey

Jiang

Han

Habibi

et al. 2021

IEEE Open J. Commun. Soc.

861

241

View full text Add to dashboard Cite

As of today, the fifth generation (5G) mobile communication system has been rolled out in many countries and the number of 5G subscribers already reaches a very large scale. It is time for academia and industry to shift their attention towards the next generation. At this crossroad, an overview of the current state of the art and a vision of future communications are definitely of interest. This article thus aims to provide a comprehensive survey to draw a picture of the sixth generation (6G) system in terms of drivers, use cases, usage scenarios, requirements, key performance indicators (KPIs), architecture, and enabling technologies. First, we attempt to answer the question of "Is there any need for 6G?" by shedding light on its key driving factors, in which we predict the explosive growth of mobile traffic until 2030, and envision potential use cases and usage scenarios. Second, the technical requirements of 6G are discussed and compared with those of 5G with respect to a set of KPIs in a quantitative manner. Third, the state-of-the-art 6G research efforts and activities from representative institutions and countries are summarized, and a tentative roadmap of definition, specification, standardization, and regulation is projected. Then, we identify a dozen of potential technologies and introduce their principles, advantages, challenges, and open research issues. Finally, the conclusions are drawn to paint a picture of "What 6G may look like?". This survey is intended to serve as an enlightening guideline to spur interests and further investigations for subsequent research and development of 6G communications systems.

show abstract

Section: ) Softwarization and Virtualizationmentioning

confidence: 99%

The Road Towards 6G: A Comprehensive Survey

Jiang

Han

Habibi

et al. 2021

IEEE Open J. Commun. Soc.

861

241

View full text Add to dashboard Cite

show abstract

“…To enable the comparability of the results implemented in different scenarios, we set (n, k)=(1,1), (1,2), (1,3) and (1,4) respectively throughout this work, to compare with other competitive conventional modulation schemes n-psk and n-QAM. The equivalent modulations for comparing are following the setting of 2 k/n -psk/QAM for different parameters (n, k).…”

Section: System Model a System Overview And Dnn Basicsmentioning

confidence: 99%

“…It has been demonstrated to significantly improvement of the system performance, as well as the quality of the services [1] [2]. Therefore, design, development and use of AI systems has attracted great attention, not only in industry, but also in the research community [3]. Many studies of the AI technologies have been carried out in communication systems in recent years, including unknown channel estimation and detection through DL [4], super-resolution channel estimation for a massive multiple-input multiple-output (MIMO) system, novel DL based algorithm for decoding [6], joint channel encoding and source encoding [8] and DL for joint channel estimation and detection in Orthogonal Frequency Division Multiplexing (OFDM) systems [7].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Autoencoder for m-User Wireless Interference Channel Physical Layer Design

Nekovee

Wang

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising, and potentially disruptive approach to design the physical layer of beyond-5G communication systems. Compared to a traditional communication system with a multiple-block structure, the DL based AE approach provides a new paradigm to physical layer design with a pure data-driven and end-to-end learning based solution. In this paper, we address the dynamic interference in a multiuser Gaussian interference channel. We show that standard constellation are not optimal for this context, in particular, for a high interference condition. We propose a novel adaptive DL based AE to overcome this problem. With our approach, dynamic interference can be learned and predicted, which updates the learning processing for the decoder. Compared to other machine learning approaches, our method does not rely on a fixed training function, but is adaptive and applicable to practical systems. In comparison with the conventional system using n-psk or n-QAM modulation schemes with zero force (ZF) and minimum mean square error (MMSE) equalizer, the proposed adaptive deep learning (ADL) based AE demonstrates a significant achievable BER in the presence of interference, especially in strong and very strong interference scenarios. The proposed approach has laid the foundation of enabling adaptable constellation for 5G and beyond communication systems, where dynamic and heterogeneous network conditions are envisaged. INDEX TERMS Deep learning, Autoencoder, 5G physical layer, and Interference channel.

show abstract

“…In response to the industry demand for AI-driven intelligent networks, ETSI has created the ENI work-group [5]. ENI's goal is to improve operator's experience and add value to the telco provided services, by assisting in decision making to deliver operational expenditure (OPEX) reduction and to enable 5G deployment with automation and intelligence.…”

Section: Ai-enabled 5g Network Architecturementioning

confidence: 99%

Artificial Intelligence for Elastic Management and Orchestration of 5G Networks

Gutierrez-Estevez

Gramaglia

Domenico

et al. 2019

IEEE Wireless Commun.

Self Cite

View full text Add to dashboard Cite

The emergence of 5G enables a broad set of diversified and heterogeneous services with complex and potentially conflicting demands. For networks to be able to satisfy those needs, a flexible, adaptable, and programmable architecture based on network slicing is being proposed. Moreover, a softwarization and cloudification of the communications networks is required, where network functions (NFs) are being transformed from programs running on dedicated hardware platforms to programs running over a shared pool of computational and communication resources. This architectural framework allows the introduction of resource elasticity as a key means to make an efficient use of the computational resources of 5G systems, but adds challenges related to resource sharing and efficiency. In this paper, we propose Artificial Intelligence (AI) as a built-in architectural feature that allows the exploitation of the resource elasticity of a 5G network. Building on the work of the recently formed Experiential Network Intelligence (ENI) industry specification group of the European Telecommunications Standards Institute (ETSI) to embed an AI engine in the network, we describe a novel taxonomy for learning mechanisms that target exploiting the elasticity of the network as well as three different resource elastic use cases leveraging AI. This work describes the basis of a use case recently approved at ETSI ENI.

show abstract

Network Management and Orchestration Using Artificial Intelligence: Overview of ETSI ENI

Cited by 36 publications

References 1 publication

The Road Towards 6G: A Comprehensive Survey

The Road Towards 6G: A Comprehensive Survey

Deep Learning-Based Autoencoder for m-User Wireless Interference Channel Physical Layer Design

Artificial Intelligence for Elastic Management and Orchestration of 5G Networks

Contact Info

Product

Resources

About