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ENI-ETSI Meets the Proactive Network Solutions for Multi-tier Networking


Proactive network optimizations (PNO) based on artificial intelligence (AI) technology have been widely introduced to improve end-to-end (E2E) communication quality of services (QoS) of next-generation mobile networks (NGMN). The novelty services under the era of NGMN (e.g., 5G, B5G, and 6G), including service level agreement (SLA) and mission-critical Internet of Things (IoT) applications, the PNO have been proposed to invent future edge infrastructure and empower computation resources for innovation network services obligations based on intelligent edge systems. AI-based PNO has been appealed to contribute to the large-scale edge computing unit (ECU) in terms of diversity management and orchestration (MANO), and it is mainly used to perform proactive network solutions (PNS) for assurance of QoS obligations of time-sensitive network (TSN) services. The network failure issues are the critical challenges of TSN communications and lessen E2E TSN communication QoS metrics. It especially reduces the reliability metrics that are crucial for mission-critical service and perspective of the ultra-reliable low latency communication (URLLC) system. This paper presents state-of-the-art discussions on AI for networking, including opportunities, issues, and trends for next-generation networking.

2022 Papers

Local and Global Orientation Correction for Oriented Human (Pose) Detection

Preliminary Study on SSCF-derived Polar Coordinate for ASR

Text Recognition on the Khmer Identification Cards and Its Application in Electronic Know Your Customer (e-KYC)

Exploration of Semantic Information of Previous Sentences for Automatic Speech Recognition

Cambodia Distributed Ledger – CamDL

Job Trends Analysis Using Power BI

Students’ Sentiment and Feedback Analysis on Online Learning System during COVID-19

Temperature Forcasting in Pnhom Penh Using Time Series Models

Eveluation of Regularization based Contiual Learning Alogorithm in the Context of Human Activity Recognition

Implementation of Deep Learning for Smart City Application: Lessons Learned

Intelligent Control in SDN/NFV-Empowered IoT System for Smart City Application


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