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Disambiguation of "Spantideas, Sotirios T."

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A Cost-Efficient 5G Non-Public Network Architectural Approach: Key Concepts and Enablers, Building Blocks and Potential Use Cases.

, , , , , , , , , , , and . Sensors, 21 (16): 5578 (2021)

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Sotirios Siampos

Sotirios Wlachos

Sotirios Chalkas

Sotirios Giorginis

Sotirios Papageorgiu

 

Other publications of authors with the same name

Towards Closed-loop Automation in 5G Open RAN: Coupling an Open-Source Simulator with xApps., , , , , and . EuCNC, page 232-237. IEEE, (2022)A Reference Architecture for Cloud-Edge Meta-Operating Systems Enabling Cross-Domain, Data-Intensive, ML-Assisted Applications: Architectural Overview and Key Concepts., , , , , , , , , and 2 other author(s). Sensors, 22 (22): 9003 (2022)A Cost-Efficient 5G Non-Public Network Architectural Approach: Key Concepts and Enablers, Building Blocks and Potential Use Cases., , , , , , , , , and 2 other author(s). Sensors, 21 (16): 5578 (2021)Deep Reinforcement Learning for Energy-Efficient Multi-Channel Transmissions in 5G Cognitive HetNets: Centralized, Decentralized and Transfer Learning Based Solutions., , , , and . IEEE Access, (2021)Joint Energy-efficient and Throughput-sufficient Transmissions in 5G Cells with Deep Q-Learning., , , , , and . MeditCom, page 265-270. IEEE, (2021)Power Control in 5G Heterogeneous Cells Considering User Demands Using Deep Reinforcement Learning., , , and . AIAI Workshops, volume 628 of IFIP Advances in Information and Communication Technology, page 95-105. Springer, (2021)Impact of Classifiers to Drift Detection Method: A Comparison., , , , , , and . EANN, 3, page 399-410. Springer, (2021)WIP: Demand-Driven Power Allocation in Wireless Networks with Deep Q-Learning., , , , , , , and . WOWMOM, page 248-251. IEEE, (2021)Allocating Orders to Printing Machines for Defect Minimization: A Comparative Machine Learning Approach., , , , , , and . AIAI (2), volume 647 of IFIP Advances in Information and Communication Technology, page 79-88. Springer, (2022)Learning to Fulfill the User Demands in 5G-enabled Wireless Networks through Power Allocation: a Reinforcement Learning approach., , , , and . DRCN, page 1-7. IEEE, (2023)