4 citations · 6 across the 24 of their papers we have counts for
13 papers · 1 filter
Out-of-Distribution Detection in Wireless Multimodal Foundation Models for 6G ISAC
Mohammad Farzanullah, Akram Bin Sediq, Ali Afana +1
The integration of Foundation Models (FMs), such as the Wireless Multimodal Foundation Model (WMFM), into 6G networks provides a unified framework for Integrated Sensing and Commun…
PERA: A Perceive-Reason-Act Interface Bridging Sensing, Cognitive Reasoning, and Trustworthy Agentic Response for 6G
Mohammad Farzanullah, Melike Erol-Kantarci, Lajos Hanzo
The realization of next-generation (NG) networks hinges on a fundamental departure from preprogrammed protocol engineering towards a paradigm of self-consciously evolving, autonomo…
Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers
Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimi…
Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction
MohammadMahdi Ghadaksaz, Mohammad Farzanullah, Akram Bin Sediq +2
Accurate short-term mobile traffic prediction is important for proactive resource allocation and low-latency network management in fifth generation (5G) and sixth generation (6G).…
Reimagining RAN Automation in 6G: An Agentic AI Framework with Hierarchical Online Decision Transformer
Md Arafat Habib, Medhat Elsayed, Majid Bavand +3
In this paper, we propose an Agentic Artificial Intelligence (AI) framework for wireless networks. The framework coordinates a pool of AI agents guided by Natural Language (NL) inp…
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…
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…
GenAI-enabled Residual Motion Estimation for Energy-Efficient Semantic Video Communication
Shavbo Salehi, Pedro Enrique Iturria-Rivera, Medhat Elsayed +3
Semantic communication addresses the limitations of the Shannon paradigm by focusing on transmitting meaning rather than exact representations, thereby reducing unnecessary resourc…
Hierarchical Decision Mamba Meets Agentic AI: A Novel Approach for RAN Slicing in 6G
Md Arafat Habib, Medhat Elsayed, Majid Bavand +3
Radio Access Network (RAN) slicing enables multiple logical networks to exist on top of the same physical infrastructure by allocating resources to distinct service groups, where r…
Generative AI for Intent-Driven Network Management in 6G RAN: A Case Study on the Mamba Model
Md Arafat Habib, Medhat Elsayed, Yigit Ozcan +3
With the emergence of 6G, mobile networks are becoming increasingly heterogeneous and dynamic, necessitating advanced automation for efficient management. Intent-Driven Networks (I…
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…
Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G
Md Arafat Habib, Pedro Enrique Iturria Rivera, Yigit Ozcan +4
Intent-driven network management is critical for managing the complexity of 5G and 6G networks. It enables adaptive, on-demand management of the network based on the objectives of…
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,…