World's Best Scientists 2026 revealed!
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Rising Stars
2025

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Rising Stars

D-Index
49
Citations
17035
World Ranking
337
National Ranking
57

Electronics and Electrical Engineering

D-Index
51
Citations
19355
World Ranking
2580
National Ranking
983

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Ahmed Alkhateeb is affiliated with Arizona State University in the United States and has a significant research portfolio in engineering, particularly electrical and electronic engineering.

Their recent papers include:

  • Design and Evaluation of Reconfigurable Intelligent Surfaces in Real-World Environment (2022, IEEE Open Journal of the Communications Society)
  • Deep Transfer Learning-Based Downlink Channel Prediction for FDD Massive MIMO Systems (2020, IEEE Transactions on Communications)
  • DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset (2023, IEEE Communications Magazine)
  • Hybrid Blockchain Platforms for the Internet of Things (IoT): A Systematic Literature Review (2022, Sensors)
  • Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles (2023, IEEE Communications Magazine)

The scientist frequently publishes in venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Wireless Communications
  • IEEE Transactions on Communications
  • IEEE Open Journal of the Communications Society
  • IEEE Wireless Communications Letters

Frequent co-authors in their work include:

  • Gouranga Charan
  • Muhammad Alrabeiah
  • Umut Demirhan
  • Shuaifeng Jiang
  • Tawfik Osman

The main field of study for Ahmed Alkhateeb is engineering, with a total of 336 publications. Their subfields of study emphasize:

  • Electrical and Electronic Engineering
  • Aerospace Engineering
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Key topics addressed in this researcher's body of work include:

  • Millimeter-Wave Propagation and Modeling
  • Advanced MIMO Systems Optimization
  • Indoor and Outdoor Localization Technologies
  • Microwave Engineering and Waveguides
  • Advanced Wireless Communication Technologies
  • Antenna Design and Analysis
  • Antenna Design and Optimization

Best Publications

  • Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems

    Ahmed Alkhateeb;Omar El Ayach;Geert Leus;Robert W. Heath

  • Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond

    Theodore S. Rappaport;Yunchou Xing;Ojas Kanhere;Shihao Ju

  • Limited Feedback Hybrid Precoding for Multi-User Millimeter Wave Systems

    Ahmed Alkhateeb;Geert Leus;Robert W. Heath

  • Hybrid MIMO Architectures for Millimeter Wave Communications: Phase Shifters or Switches?

    Roi Mendez-Rial;Cristian Rusu;Nuria Gonzalez-Prelcic;Ahmed Alkhateeb

  • Modeling and Analyzing Millimeter Wave Cellular Systems

    Jeffrey G. Andrews;Tianyang Bai;Mandar N. Kulkarni;Ahmed Alkhateeb

  • MIMO Precoding and Combining Solutions for Millimeter-Wave Systems

    Ahmed Alkhateeb;Jianhua Mo;Nuria Gonzalez-Prelcic;Robert W. Heath

  • Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning

    Abdelrahman Taha;Muhammad Alrabeiah;Ahmed Alkhateeb

  • Frequency Selective Hybrid Precoding for Limited Feedback Millimeter Wave Systems

    Ahmed Alkhateeb;Robert W. Heath

  • Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems

    Ahmed Alkhateeb;Sam Alex;Paul Varkey;Ying Li

  • Dynamic Subarrays for Hybrid Precoding in Wideband mmWave MIMO Systems

    Sungwoo Park;Ahmed Alkhateeb;Robert W. Heath

  • Coverage and capacity of millimeter-wave cellular networks

    Tianyang Bai;Ahmed Alkhateeb;Robert W Heath

  • Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems

    Kiran Venugopal;Ahmed Alkhateeb;Nuria Gonzalez Prelcic;Robert W. Heath

  • Hybrid precoding for millimeter wave cellular systems with partial channel knowledge

    A. Alkhateeb;O. El Ayach;G. Leus;Robert W. Heath

  • Compressed sensing based multi-user millimeter wave systems: How many measurements are needed?

    Ahmed Alkhateeby;Geert Leusz;Robert W. Heath

  • Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination

    Faris B. Mismar;Brian L. Evans;Ahmed Alkhateeb

  • Hybrid Architectures With Few-Bit ADC Receivers: Achievable Rates and Energy-Rate Tradeoffs

    Jianhua Mo;Ahmed Alkhateeb;Shadi Abu-Surra;Robert W. Heath

  • Channel estimation and hybrid combining for mmWave: Phase shifters or switches?

    Roi Mendez-Rial;Cristian Rusu;Ahmed Alkhateeb;Nuria Gonzalez-Prelcic

  • DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications.

    Ahmed Alkhateeb

  • Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels

    Muhammad Alrabeiah;Ahmed Alkhateeb

  • DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset

    Unknown

  • Millimeter Wave Energy Harvesting

    Talha Ahmed Khan;Ahmed Alkhateeb;Robert W. Heath

Frequent Co-Authors

Robert W. Heath
Robert W. Heath University of California, San Diego
Feifei Gao
Feifei Gao Tsinghua University
Geert Leus
Geert Leus Delft University of Technology
Jeffrey G. Andrews
Jeffrey G. Andrews The University of Texas at Austin
Brian L. Evans
Brian L. Evans The University of Texas at Austin
Young-Han Nam
Young-Han Nam Samsung (United States)
Jianzhong Zhang
Jianzhong Zhang Samsung (United States)
Chaitali Chakrabarti
Chaitali Chakrabarti Arizona State University
Xiaoming Tao
Xiaoming Tao Hong Kong Polytechnic University
Cihan Tepedelenlioglu
Cihan Tepedelenlioglu Arizona State University

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