KU 6GRC听organizes its research around three primary themes, each targeting key areas needed for future听:
KU 6GRC听organizes its research around three primary themes, each targeting key areas needed for future听:
Connectivity听(led by Prof. Sami Muhaidat): This theme explores advanced wireless communication techniques to massively increase data throughput, reliability, and coverage. Research topics include holographic MIMO antennas, terabit-per-second (Tbps) coding schemes,听听(OAM) communications, cell-free massive MIMO architectures, and large intelligent surfaces for signal propagation. The center also investigates听听and sub-terahertz transmission, integration of low Earth orbit (LEO) satellites for 3D cellular networks, novel waveforms beyond OFDM, ultra-massive connectivity techniques such as NOMA,听听systems, and even time-reversal communication methods.
Localization and Sensing听(led by Prof. Arafat Al-Dweik): This research theme focuses on technologies that merge communications with sensing and precise positioning. Topics include joint sensing and communication waveforms (enabling devices and networks to simultaneously detect and communicate), THz communications for high-resolution sensing, radio-based wireless imaging, and extreme ultra-reliable low-latency communication (URLLC) for mission-critical sensing applicationsf. The center also studies metrics like Age of Information (to optimize data freshness in networks) and explores beyond Nyquist sampling paradigms for efficient signal processing. These efforts aim to allow future 6G networks to be aware of their environment and support services like accurate localization, mapping, and ambient sensing.
Native AI听():听The Native AI theme is dedicated to the deep integration of artificial intelligence into wireless networks 鈥� essentially making AI 鈥渘ative鈥� to the 6G architecture. Research in this area includes developing AI-driven transceivers (radio components that use AI for signal processing and decision-making) and distributed AI algorithms for networks. The center is working on multi-agent generative AI systems and Distributed GPT models tailored for telecommunication networks. There is also a focus on edge learning (bringing machine learning capabilities to the network edge and devices) and on semantic communications, such as designing semantic-level source and channel coding schemes that transmit the intended meaning of data rather than raw bits. These efforts are illustrated through the center鈥檚 concept of听鈥淭elecom AI鈥�听鈥� developing telecom-specific AI foundation models and autonomous agent frameworks to manage complex networks. The Native AI research pillar is sometimes described in terms of Telecom Big Data, Telecom Foundation Models, and Telecom Autonomy, reflecting work on large language models for telecom (e.g.听TelecomGPT), multi-modal data fusion, and AI-powered network automation.