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 dev...
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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 networkfunctions (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.
network slicing is a critical feature of the beyond fifth-generation (B5G) network that supports a wide range of innovative services from 5.0 industries, next-generation consumer electronics, smart healthcare, etc. Ne...
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network slicing is a critical feature of the beyond fifth-generation (B5G) network that supports a wide range of innovative services from 5.0 industries, next-generation consumer electronics, smart healthcare, etc. network slicing guarantees the provisioning of quality of service (QoS) aware dedicated resources to each service. However, the orchestration and management of network slicing is very challenging because of the complex configuration process for underlying network resources. Furthermore, the third generation partnership project (3GPP) presented artificial intelligence (AI) based network data analytics function (NWDAF) in 5G for proactive management and intelligence. Therefore, we have developed an intent-based networking (IBN) system for automating network slices and an AI-driven NWDAF for proactive and intelligent resource assurance. The network data analytics function uses a hybrid stacking ensemble learning (STEL) algorithm to predict network resource utilization and a novel automated machine learning (AutoML) and voting ensemble learning-based mechanism to detect and mitigate network anomalies. To validate the performance of the implemented work, real-time datasets were employed, and a comparative analysis was conducted. The experimental result shows that our STEL model enhances the accuracy by 20% and reduces the error rate by 45%. The AutoML and ensemble learning-based optimized model achieved 99.22% accuracy for anomaly detection.
NWDAF (network data analytics function) is a networkfunction defined by 3GPP that enables networkdata analysis for mobile core network. After R17 was frozen as the last version of the 5G phase, 3GPP continued to pro...
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With the coming of the B5G and 6G era, lots of research reports held on predictions that the number of connected devices will keep exploding. According to specifications determined by the 3rd Generation Partnership Pr...
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ISBN:
(数字)9784885523397
ISBN:
(纸本)9784885523397
With the coming of the B5G and 6G era, lots of research reports held on predictions that the number of connected devices will keep exploding. According to specifications determined by the 3rd Generation Partnership Project (3GPP), when excess devices request internet may lead to signaling overhead of the charging system, it is necessary to predict the internet traffic required. Therefore, we take the advantage of meta-learning to effectively predict according to a few samples in the past. We implement the network data analytics function (NWDAF) and charging function based on meta-learning and implement it on a public cloud platform. Experimental results show that our proposed Meta-NWDAF architecture can reduce signaling significantly. Our research contributions are to show that meta-learning can be applied to not only classification problems but also time series prediction problems, and we also prove that the future diverse connected devices are well suited for leveraging me-ta-learning. The managerial implication of this research is that our proposed architecture effectively reduces the signaling overhead for the charging system.
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