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Environment Modeling and Abstraction of Network States for Cognitive Functions.

, , , and . NOMS, page 1-8. IEEE, (2020)

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An Exposed Closed-Loop Model for Customer-Driven Service Assurance Automation., , , , , , , and . EuCNC/6G Summit, page 419-424. IEEE, (2021)Distributing Intelligence for 6G Network Automation: Performance and Architectural Impact., , , and . ICC, page 6224-6229. IEEE, (2023)Q-Learning for Conflict Resolution in B5G Network Automation., , and . CoRR, (2021)Toward Massive Distribution of Intelligence for 6G Network Management Using Double Deep Q-Networks., , , and . IEEE Trans. Netw. Serv. Manag., 21 (2): 2077-2094 (April 2024)Scalability of Distributed Intelligence Architecture for 6G Network Automation., , and . ICC, page 2321-2326. IEEE, (2022)Understanding Exploration and Exploitation of Q-Learning Agents in B5G Network Management., , and . GLOBECOM (Workshops), page 1-6. IEEE, (2021)Environment Modeling and Abstraction of Network States for Cognitive Functions., , , and . NOMS, page 1-8. IEEE, (2020)AI Anomaly Detection for Cloudified Mobile Core Architectures., , , , , , , , , and 1 other author(s). IEEE Trans. Netw. Serv. Manag., 20 (2): 1976-1992 (June 2023)Improving Scalability of 6G Network Automation with Distributed Deep Q-Networks., , , and . GLOBECOM, page 1265-1270. IEEE, (2022)