Malla Reddy Sama
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Malla Reddy Sama has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
Atsushi Minokuchi
NTT DoCoMo Inc
1 shared publication- Bahador Bakhshi
DoCoMo Euro-Labs, Munich, Germany
1 shared publication - DongJin Lee
SK Telecom, Seoul, South Korea
1 shared publication - Jari Mutikainen
DOCOMO Communications Laboratories Europe, Munich, Germany
1 shared publication - Morteza Kheirkhah
University College London
1 shared publication - Sushmit Bhattacharjee
Applied Network Technology Laboratory, Huawei Technologies Duesseldorf GmbH, Munich, Germany
1 shared publication
Latest Publications
- 11 February 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Communications Standards Magazine
- Vol. 10 (2) , 80-88
Architectural Foundations and Standardization Outlook for AI-Enabled 6G Core Network
6G will provide a new opportunity to integrate Artificial Intelligence (AI) and Machine Learning (ML) into wireless communication. The current 5G Core (5GC) architecture possesses limitations that restrict its ability to effectively deploy AI functions, including the lack of a standardized, unified access layer for network data, limited service exposure models that cannot fully support AI feedback loops, and tight coupling between logic and signaling that inhibits the insertion of AI-based decision-making components. This paper explores approaches to address the 5GC shortcomings in the future 6G Core (6GC) network. Recommendations for architectural evolution and standardization are discussed to facilitate scalable, efficient, and adaptable AI-enabled 6G networks.
- 11 February 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Communications Standards Magazine
- Vol. 10 (2) , 80-88
Architectural Foundations and Standardization Outlook for AI-Enabled 6G Core Network
6G will provide a new opportunity to integrate Artificial Intelligence (AI) and Machine Learning (ML) into wireless communication. The current 5G Core (5GC) architecture possesses limitations that restrict its ability to effectively deploy AI functions, including the lack of a standardized, unified access layer for network data, limited service exposure models that cannot fully support AI feedback loops, and tight coupling between logic and signaling that inhibits the insertion of AI-based decision-making components. This paper explores approaches to address the 5GC shortcomings in the future 6G Core (6GC) network. Recommendations for architectural evolution and standardization are discussed to facilitate scalable, efficient, and adaptable AI-enabled 6G networks.