Data Analytics Architectural Framework for Smarter Radio Resource Management in 5G Radio Access Networks

Ferrús, R.; Sallent, O.; Perez-Romero, J.

doi:10.1109/mcom.001.1900475

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…In [80], a novel framework is presented for the extraction and delivery of RAN data analytics information to radio resource management (RRM) algorithms. To this end, the RAN data analytics function (RANDAF) has been proposed, which can operate independently from the RAN nodes.…”

Section: A Nwdaf-aided Resource Optimizationmentioning

confidence: 99%

Leveraging Network Data Analytics Function and Machine Learning for Data Collection, Resource Optimization, Security and Privacy in 6G Networks

Gkonis,

Nomikos,

Trakadas

et al. 2024

IEEE Access

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The full deployment of sixth-generation (6G) networks is inextricably connected with a holistic network redesign able to deal with various emerging challenges, such as integration of heterogeneous technologies and devices, as well as support of latency and bandwidth demanding applications. In such a complex environment, resource optimization, and security and privacy enhancement can be quite demanding, due to the vast and diverse data generation endpoints and associated hardware elements. Therefore, efficient data collection mechanisms are needed that can be deployed at any network infrastructure. In this context, the network data analytics function (NWDAF) has already been defined in the fifth-generation (5G) architecture from Release 15 of 3GPP, that can perform data collection from various network functions (NFs). When combined with advanced machine learning (ML) techniques, a full-scale network optimization can be supported, according to traffic demands and service requirements. In addition, the collected data from NWDAF can be used for anomaly detection and thus, security and privacy enhancement. Therefore, the main goal of this paper is to present the current state-of-the-art on the role of the NWDAF towards data collection, resource optimization and security enhancement in next generation broadband networks. Furthermore, various key enabling technologies for data collection and threat mitigation in the 6G framework are identified and categorized, along with advanced ML approaches. Finally, a high level architectural approach is presented and discussed, based on the NWDAF, for efficient data collection and ML model training in large scale heterogeneous environments.

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Section: A Nwdaf-aided Resource Optimizationmentioning

confidence: 99%

Leveraging Network Data Analytics Function and Machine Learning for Data Collection, Resource Optimization, Security and Privacy in 6G Networks

Gkonis,

Nomikos,

Trakadas

et al. 2024

IEEE Access

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“…TS 29.520 in R-16 is the standardization effort of 3GPP for 5G network automation using ML and data analytics. Within the latest approved 5G specification in 3GPP (Release 15), 3GPP identifies two main building blocks responsible for data analytics [146] 6 shows the functional framework for the management and network data analytics services in 3GPP 5G systems and the corresponding mapping to the defined components of the data engineering pipeline. Data connection and data ingestion modules are located within Service Based Management and Control Interfaces.…”

Section: Gpp Network Data Analytics Functionmentioning

confidence: 99%

Recent Advances in Data Engineering for Networking

Zeydan

Mangues‐Bafalluy

2022

IEEE Access

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“…All these ANM framework requirements and the increasing interest in AI/ML-assisted approaches to resource orchestration and optimization led to the efforts of ITU-T [34] and 3GPP [35] to define the architectural framework for integrating ML solutions to 5G and beyond networks, whereas exploiting AI solutions in every network segment possible to learn and adapt to network dynamics is conceptualized as the 'AI Everywhere' principle for networks [1,36]. The 3GPP has introduced the network data analytics function (NWDAF) and the management data analytics function (MDAF) for core services [37] that can be used for centralized optimization of the network resources. On the other hand, the 5G RAN data analytics function (DAF) is proposed for radio resource management [38], which targets managing machine learning and AI solutions in the RAN with open interfaces inside the O-RAN project [33].…”

Section: Autonomic Network Managementmentioning

confidence: 99%

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

2021

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Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G.

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Data Analytics Architectural Framework for Smarter Radio Resource Management in 5G Radio Access Networks

Cited by 9 publications

References 13 publications

Leveraging Network Data Analytics Function and Machine Learning for Data Collection, Resource Optimization, Security and Privacy in 6G Networks

Leveraging Network Data Analytics Function and Machine Learning for Data Collection, Resource Optimization, Security and Privacy in 6G Networks

Recent Advances in Data Engineering for Networking

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

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