xDRL-RCS | eXplainable Deep Reinforcement Learning Assisted 5G/6G RAN & Core Slicing | 6G-XR OC2
Автор: 6G-XR
Загружено: 2025-12-21
Просмотров: 1
Organisation: iThermAI (SME) – ithermai.com
Country: Belgium
Topic: TOP4 – AI/ML for slicing: AI/ML algorithm for efficient resource optimization in the 5G slicing techniques
6G-XR Facilities: North node – OULU 5GTN
xDRL-RCS aims to transform network management and service delivery in 5G/6G environments by leveraging open-radio access networks (O-RAN), Open-Source MANO (OSM), and Open5Gs. xDRL-RCS focuses on enhancing network efficiency and agility through explainable AI, particularly distributed deep reinforcement learning (DRL). The project involves preparing comprehensive datasets from open-source, synthetic, and real data to develop advanced xDRL algorithms. These will be integrated into the FlexRIC system, OSM, and Open5Gs for improved real-time network slice management. Key network parameters such as air interface resources, transmit power, and slice configuration will be optimized to enhance performance and responsiveness, using real-time metrics for AI model training.
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