140 citations · 178 across the 15 of their papers we have counts for
9 papers · 1 filter
Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios
Gouranga Charan, Muhammad Alrabeiah, Tawfik Osman +1
Leveraging sensory information to aid the millimeter-wave (mmWave) and sub-terahertz (sub-THz) beam selection process is attracting increasing interest. This sensory data, captured…
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…
Learning Beam Codebooks with Neural Networks: Towards Environment-Aware mmWave MIMO
Yu Zhang, Muhammad Alrabeiah, Ahmed Alkhateeb
Scaling the number of antennas up is a key characteristic of current and future wireless communication systems. The hardware cost and power consumption, however, motivate large-sca…
Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction
Muhammad Alrabeiah, Andrew Hredzak, Ahmed Alkhateeb
This paper investigates a novel research direction that leverages vision to help overcome the critical wireless communication challenges. In particular, this paper considers millim…
Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels
Muhammad Alrabeiah, Ahmed Alkhateeb
Predicting the millimeter wave (mmWave) beams and blockages using sub-6GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. These gains a…