A survey on explainable ai for 6g o-ran: Architecture, use cases, challenges and research directions
The recent O-RAN specifications promote the evolution of RAN architecture by function
disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop …
disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop …
Resource Management From Single-domain 5G to End-to-End 6G Network Slicing: A Survey
S Ebrahimi, F Bouali, OCL Haas - … Communications Surveys & …, 2024 - ieeexplore.ieee.org
Network Slicing (NS) is one of the pillars of the fifth/sixth generation (5G/6G) of mobile
networks. It provides the means for Mobile Network Operators (MNOs) to leverage physical …
networks. It provides the means for Mobile Network Operators (MNOs) to leverage physical …
Toward Explainable Reasoning in 6G: A Proof of Concept Study on Radio Resource Allocation
The move toward artificial intelligence (AI)-native sixth-generation (6G) networks has put
more emphasis on the importance of explainability and trustworthiness in network …
more emphasis on the importance of explainability and trustworthiness in network …
DRL-based Latency-Aware Network Slicing in O-RAN with Time-Varying SLAs
The Open Radio Access Network (Open RAN) paradigm, and its reference architecture
proposed by the O-RAN Alliance, is paving the way toward open, interoperable, observable …
proposed by the O-RAN Alliance, is paving the way toward open, interoperable, observable …
Towards accountable and resilient AI-assisted networks: case studies and future challenges
Artificial Intelligence (AI) will play a critical role in future networks, exploiting real-time data
collection for optimized utilization of network resources. However, current AI solutions …
collection for optimized utilization of network resources. However, current AI solutions …
Explainable AI for enhancing efficiency of DL-based channel estimation
The support of artificial intelligence (AI) based decision-making is a key element in future 6G
networks, where the concept of native AI will be introduced. Moreover, AI is widely employed …
networks, where the concept of native AI will be introduced. Moreover, AI is widely employed …
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing
Future zero-touch artificial intelligence (AI)-driven 6G network automation requires building
trust in the AI black boxes via explainable artificial intelligence (XAI), where it is expected …
trust in the AI black boxes via explainable artificial intelligence (XAI), where it is expected …
[HTML][HTML] Explainable Machine Learning in Critical Decision Systems: Ensuring Safe Application and Correctness
J Wiggerthale, C Reich - AI, 2024 - mdpi.com
Machine learning (ML) is increasingly used to support or automate decision processes in
critical decision systems such as self driving cars or systems for medical diagnosis. These …
critical decision systems such as self driving cars or systems for medical diagnosis. These …
Leveraging LLMs to eXplain DRL Decisions for Transparent 6G Network Slicing
M Ameur, B Brik, A Ksentini - 2024 IEEE 10th International …, 2024 - ieeexplore.ieee.org
The emergence of 6G networks heralds a transformative era in network slicing, facilitating
tailored service delivery and optimal resource utilization. Despite its promise, network slice …
tailored service delivery and optimal resource utilization. Despite its promise, network slice …
Demystifying Reinforcement Learning in Production Scheduling via Explainable AI
D Fischer, HM Hüsener, F Grumbach… - arXiv preprint arXiv …, 2024 - arxiv.org
Deep Reinforcement Learning (DRL) is a frequently employed technique to solve
scheduling problems. Although DRL agents ace at delivering viable results in short …
scheduling problems. Although DRL agents ace at delivering viable results in short …