140 citations · 174 across the 13 of their papers we have counts for
17 papers
Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence
Muhammad Alrabeiah, Umut Demirhan, Andrew Hredzak +1
The support of coexisting ultra-reliable and low-latency (URLL) and enhanced Mobile BroadBand (eMBB) services is a key challenge for the current and future wireless communication n…
Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems
Yu Zhang, Muhammad Alrabeiah, Ahmed Alkhateeb
Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. Being pre-defined, however, these codeb…
Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction
Nof Abuzainab, Muhammad Alrabeiah, Ahmed Alkhateeb +1
We consider the problem of proactive handoff and beam selection in Terahertz (THz) drone communication networks assisted with reconfigurable intelligent surfaces (RIS). Drones have…
Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff
Gouranga Charan, Muhammad Alrabeiah, Ahmed Alkhateeb
The sensitivity to blockages is a key challenge for the high-frequency (5G millimeter wave and 6G sub-terahertz) wireless networks. Since these networks mainly rely on line-of-sigh…
Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems
Yu Zhang, Muhammad Alrabeiah, Ahmed Alkhateeb
Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. However, due to the adoption of fully analog or hybrid analo…
Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements
Shunyao Wu, Muhammad Alrabeiah, Andrew Hredzak +2
Millimeter wave (mmWave) communication is a key component of 5G and beyond. Harvesting the gains of the large bandwidth and low latency at mmWave systems, however, is challenged by…