6 papers
Dual-Transformer Aided Hierarchical Deep Reinforcement Learning for Robust RIS-Assisted Near-Field Communications
Mohammad Ghassemi, Han Zhang, Ali Afana +2
The deployment of extremely large aperture arrays (ELAAs) in sixth-generation (6G) networks is expected to shift communications into the near-field regime, where spherical-wave pro…
Foundation Model-Aided Hierarchical Deep Reinforcement Learning for Blockage-Aware Link in RIS-Assisted Networks
Mohammad Ghassemi, Han Zhang, Ali Afana +2
Reconfigurable intelligent surface (RIS) technology has the potential to significantly enhance the spectral efficiency (SE) of 6G wireless networks. However, practical deployment r…
Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey
Han Zhang, Mohammad Farzanullah, Mohammad Ghassemi +3
Foundation models (FMs) are recognized as a transformative breakthrough that has started to reshape the future of artificial intelligence (AI) across both academia and industry. Th…
Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication
Mohammad Ghassemi, Sara Farrag Mobarak, Han Zhang +3
Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by dynamically controlling signal propagation in the environme…
Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication
Mohammad Ghassemi, Han Zhang, Ali Afana +2
In mmWave wireless networks, signal blockages present a significant challenge due to the susceptibility to environmental moving obstructions. Recently, the availability of visual d…
Multi-Modal Transformer and Reinforcement Learning-based Beam Management
Mohammad Ghassemi, Han Zhang, Ali Afana +2
Beam management is an important technique to improve signal strength and reduce interference in wireless communication systems. Recently, there has been increasing interest in usin…