5 papers
Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management
Nasir Khan, Asmaa Abdallah, Abdulkadir Celik +2
Efficient and reliable beam alignment is a critical requirement for mmWave multiple-input multiple-output (MIMO) systems, especially in 6G and beyond, where communication must be f…
Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems
Nasir Khan, Asmaa Abdallah, Abdulkadir Celik +2
In line with the AI-native 6G vision, explainability and robustness are crucial for building trust and ensuring reliable performance in millimeter-wave (mmWave) systems. Efficient…
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks
Nasir Khan, Asmaa Abdallah, Abdulkadir Celik +2
Integrated artificial intelligence (AI) and communication has been recognized as a key pillar of 6G and beyond networks. In line with AI-native 6G vision, explainability and robust…
Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation
Nasir Khan, Asmaa Abdallah, Abdulkadir Celik +2
Artificial intelligence (AI) is expected to significantly enhance radio resource management (RRM) in sixth-generation (6G) networks. However, the lack of explainability in complex…
Event-Triggered Reinforcement Learning Based Joint Resource Allocation for Ultra-Reliable Low-Latency V2X Communications
Nasir Khan, Sinem Coleri
Future 6G-enabled vehicular networks face the challenge of ensuring ultra-reliable low-latency communication (URLLC) for delivering safety-critical information in a timely manner.…