Ahmed Alkhateeb

Assistant Professor

Arizona State University

United States

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Dr. Ahmed Alkhateeb is an Assistant Professor at Arizona State University, USA. His research focuses on integrated sensing and communications, machine learning applications in wireless systems, and advanced MIMO technologies. Recent publications include topics such as beam-based user identification and dynamic joint communications and sensing precoding design.

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Collaborators (5)

Jeonghun Park

Yonsei University

SOUTH KOREA

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Yu Zhang

Arizona State University

UNITED STATES

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Ian Roberts

Assistant Professor

UCLA

UNITED STATES

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Namyoon Lee

Korea University

SOUTH KOREA

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Daniel W. Bliss

Arizona State University

UNITED STATES

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Ahmed Alkhateeb

Research Interests

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About

Articles (28)

Enabling ISAC in Real World: Beam-Based User Identification With Machine Learning

Cell-Free ISAC MIMO Systems: Joint Sensing and Communication Beamforming

Decentralized Interference-Aware Codebook Learning in Millimeter Wave MIMO Systems

Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios

FDD Massive MIMO: How to Optimally Combine UL Pilot and Limited DL CSI Feedback?

Context-Aware Machine Learning-Based Beam Selection With Multi-Panel Devices in the Presence of Self-Blockage

DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset

Dynamic Joint Communications and Sensing Precoding Design: A Lyapunov Approach

Integrated Sensing and Communications in Downlink FDD MIMO without CSI Feedback

Intelligent Implicit CSI Feedback Approaches in Wideband Wireless Communications Systems

Joint and Robust Beamforming Framework for Integrated Sensing and Communication Systems

Vision-Guided MIMO Radar Beamforming for Enhanced Vital Signs Detection in Crowds

Real-World Evaluation of Full-Duplex Millimeter Wave Communication Systems

Online Beam Learning With Interference Nulling for Millimeter Wave MIMO Systems

PARAMOUNT: Toward Generalizable Deep Learning for mmWave Beam Selection Using Sub-6 GHz Channel Measurements

Multiple Access Techniques for Intelligent and Multifunctional 6G: Tutorial, Survey, and Outlook

User Identification: A Key Enabler for Multi-User Vision-Aided Communications

Zone-Specific CSI Feedback for Massive MIMO: A Situation-Aware Deep Learning Approach

Computer Vision Aided Codebook Design for MIMO Communications Systems

Computer Vision Aided mmWave Beam Alignment in V2X Communications

LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications

Deep Learning of Near Field Beam Focusing in Terahertz Wideband Massive MIMO Systems

Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures

Sensing Aided Reconfigurable Intelligent Surfaces for 3GPP 5G Transparent Operation

Enabling Cell-Free Massive MIMO Systems With Wireless Millimeter Wave Fronthaul

Neural Networks Based Beam Codebooks: Learning mmWave Massive MIMO Beams That Adapt to Deployment and Hardware

Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems

Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration

Collaborators (5)

Jeonghun Park

Yu Zhang

Ian Roberts

Namyoon Lee

Daniel W. Bliss

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