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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - AI-Driven Data Analytics and Intent-Based Networking for Orchestration and Control of B5G Consumer Electronics Services
AU - Abbas, Khizar
AU - Nauman, Ali
AU - Bilal, Muhammad
AU - Yoo, Jae-Hyung
AU - Hong, James Won-Ki
AU - Song, Wang-Cheol
PY - 2024/2/29
Y1 - 2024/2/29
N2 - 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.
AB - 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.
KW - Electrical and Electronic Engineering
KW - 5G communications
KW - intent-based networking
U2 - 10.1109/tce.2023.3324010
DO - 10.1109/tce.2023.3324010
M3 - Journal article
VL - 70
SP - 2155
EP - 2169
JO - IEEE Transactions on Consumer Electronics
JF - IEEE Transactions on Consumer Electronics
SN - 0098-3063
IS - 1
ER -