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Found 4 papers, 1 papers with code
Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Case
In the context of sixth-generation (6G) networks, where diverse network slices coexist, the adoption of AI-driven zero-touch management and orchestration (MANO) becomes crucial.
Decision Making
Explainable Artificial Intelligence (XAI)
+3
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 that AI faithfulness would be a quantifiable service-level agreement (SLA) metric along with telecommunications key performance indicators (KPIs).
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
TEFL: Turbo Explainable Federated Learning for 6G Trustworthy Zero-Touch Network Slicing
Sixth-generation (6G) networks anticipate intelligently supporting a massive number of coexisting and heterogeneous slices associated with various vertical use cases.
Explainable Artificial Intelligence (XAI)
Federated Learning
+1
Rician $K$-Factor-Based Analysis of XLOS Service Probability in 5G Outdoor Ultra-Dense Networks
In this report, we introduce the concept of Rician $K$-factor-based radio resource and mobility management for fifth generation (5G) ultra-dense networks (UDN), where the information on the gradual visibility between the new radio node B (gNB) and the user equipment (UE)---dubbed X-line-of-sight (XLOS)---would be required.
Information Theory Information Theory
