3 citations · 5 across the 17 of their papers we have counts for
17 papers
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…
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…
Wireless Multimodal Foundation Model (WMFM): Integrating Vision and Communication Modalities for 6G ISAC Systems
Mohammad Farzanullah, Han Zhang, Akram Bin Sediq +2
The emergence of multimodal foundation models has revolutionized learning paradigms by enabling joint understanding across diverse data types. In the context of next-generation wir…
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…
Conditional Denoising Diffusion for ISAC Enhanced Channel Estimation in Cell-Free 6G
Mohammad Farzanullah, Han Zhang, Akram Bin Sediq +2
Cell-free Integrated Sensing and Communication (ISAC) aims to revolutionize 6th Generation (6G) networks. By combining distributed access points with ISAC capabilities, it boosts s…
Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection
Han Zhang, Akram Bin Sediq, Ali Afana +1
Mobile traffic prediction is an important enabler for optimizing resource allocation and improving energy efficiency in mobile wireless networks. Building on the advanced contextua…
Intelligent Attacks and Defense Methods in Federated Learning-enabled Energy-Efficient Wireless Networks
Han Zhang, Hao Zhou, Medhat Elsayed +4
Federated learning (FL) is a promising technique for learning-based functions in wireless networks, thanks to its distributed implementation capability. On the other hand, distribu…
Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning
Mohammad Farzanullah, Han Zhang, Akram Bin Sediq +2
Sixth generation (6G) wireless technology is anticipated to introduce Integrated Sensing and Communication (ISAC) as a transformative paradigm. ISAC unifies wireless communication…
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…
Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks
Han Zhang, Medhat Elsayed, Majid Bavand +3
Federated learning (FL) is an innovative distributed artificial intelligence (AI) technique. It has been used for interdisciplinary studies in different fields such as healthcare,…
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…
Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks
Han Zhang, Akram Bin Sediq, Ali Afana +1
In recent years, machine learning (ML) techniques have created numerous opportunities for intelligent mobile networks and have accelerated the automation of network operations. How…
Generative AI Empowered LiDAR Point Cloud Generation with Multimodal Transformer
Mohammad Farzanullah, Han Zhang, Akram Bin Sediq +2
Integrated sensing and communications is a key enabler for the 6G wireless communication systems. The multiple sensing modalities will allow the base station to have a more accurat…
Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection
Han Zhang, Akram Bin Sediq, Ali Afana +1
Large language models (LLMs), especially generative pre-trained transformers (GPTs), have recently demonstrated outstanding ability in information comprehension and problem-solving…
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks
Han Zhang, Medhat Elsayed, Majid Bavand +3
Federated learning (FL) allows distributed participants to train machine learning models in a decentralized manner. It can be used for radio signal classification with multiple rec…
Distributed Attacks over Federated Reinforcement Learning-enabled Cell Sleep Control
Han Zhang, Hao Zhou, Medhat Elsayed +4
Federated learning (FL) is particularly useful in wireless networks due to its distributed implementation and privacy-preserving features. However, as a distributed learning system…