World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
19852
World Ranking
8568
National Ranking
3664

Overview

Jakob Hoydis is affiliated with Nvidia in the United States. Their research spans multiple fields within engineering and computer science, focusing primarily on electrical and electronic engineering as well as computer networks and communications. Their work also covers aspects of artificial intelligence, aerospace engineering, and general health professions.

The scientist's research topics include:

  • Advanced MIMO Systems Optimization
  • Wireless Signal Modulation Classification
  • Advanced Wireless Communication Techniques
  • Millimeter-Wave Propagation and Modeling
  • PAPR reduction in OFDM
  • Radio Frequency Integrated Circuit Design
  • Radar Systems and Signal Processing

Jakob Hoydis has authored numerous papers, with recent notable publications including:

  • "Sionna: An Open-Source Library for Next-Generation Physical Layer Research," 2022, arXiv (Cornell University)
  • "The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning," 2021, 2021 IEEE Globecom Workshops (GC Wkshps)
  • "Graph Neural Networks for Channel Decoding," 2022, 2022 IEEE Globecom Workshops (GC Wkshps)
  • "Machine Learning for MU-MIMO Receive Processing in OFDM Systems," 2021, IEEE Journal on Selected Areas in Communications
  • "Toward Joint Learning of Optimal MAC Signaling and Wireless Channel Access," 2021, IEEE Transactions on Cognitive Communications and Networking

The scientist frequently collaborates with a core group of co-authors, including Sebastian Cammerer, Alexander Keller, and Fayçal Aït Aoudia.

Jakob Hoydis publishes regularly in a few key venues that reflect the focus of their work. These include:

  • arXiv (Cornell University)
  • IEEE Journal on Selected Areas in Communications
  • IEEE Transactions on Wireless Communications
  • 2021 IEEE Globecom Workshops (GC Wkshps)
  • IEEE Transactions on Communications

The main fields of study related to their research are:

  • Engineering
  • Computer Science

Jakob Hoydis's work contributes to advancing the understanding and development of wireless communication systems, signal processing, and machine learning applications in communication technologies.

Best Publications

  • An Introduction to Deep Learning for the Physical Layer

    Timothy O'Shea;Jakob Hoydis

  • Massive MIMO in the UL/DL of Cellular Networks: How Many Antennas Do We Need?

    J. Hoydis;Stephan ten Brink;M. Debbah

  • Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency

    Emil Björnson;Jakob Hoydis;Luca Sanguinetti

  • Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come

    Marco Di Renzo;Merouane Debbah;Dinh-Thuy Phan-Huy;Alessio Zappone

  • Massive MIMO Systems With Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits

    Emil Bjornson;Jakob Hoydis;Marios Kountouris;Merouane Debbah

  • Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer?

    Emil Bjornson;Luca Sanguinetti;Jakob Hoydis;Merouane Debbah

  • Deep Learning Based Communication Over the Air

    Sebastian Dorner;Sebastian Cammerer;Jakob Hoydis;Stephan ten Brink

  • Massive MIMO is a reality—What is next?: Five promising research directions for antenna arrays

    Emil Björnson;Luca Sanguinetti;Henk Wymeersch;Jakob Hoydis

  • Green Small-Cell Networks

    J Hoydis;M Kobayashi;M Debbah

  • On deep learning-based channel decoding

    Tobias Gruber;Sebastian Cammerer;Jakob Hoydis;Stephan ten Brink

  • Massive MIMO Has Unlimited Capacity

    Emil Bjornson;Jakob Hoydis;Luca Sanguinetti

  • Toward Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination

    Luca Sanguinetti;Emil Bjornson;Jakob Hoydis

  • Massive MIMO: How many antennas do we need?

    Jakob Hoydis;Stephan ten Brink;Merouane Debbah

  • Channel measurements for large antenna arrays

    Jakob Hoydis;Cornelis Hoek;Thorsten Wild;Stephan ten Brink

  • Adaptive Neural Signal Detection for Massive MIMO

    Mehrdad Khani;Mohammad Alizadeh;Jakob Hoydis;Phil Fleming

  • Designing multi-user MIMO for energy efficiency: When is massive MIMO the answer?

    Emil Björnson;Luca Sanguinetti;Jakob Hoydis;Mérouane Debbah

  • OFDM-Autoencoder for End-to-End Learning of Communications Systems

    Alexander Felix;Sebastian Cammerer;Sebastian Dorner;Jakob Hoydis

  • Making smart use of excess antennas: Massive MIMO, small cells, and TDD

    Jakob Hoydis;Kianoush Hosseini;Stephan ten Brink;Mérouane Debbah

  • Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning

    Sebastian Cammerer;Tobias Gruber;Jakob Hoydis;Stephan ten Brink

  • Model-Free Training of End-to-End Communication Systems

    Faycal Ait Aoudia;Jakob Hoydis

  • Towards Massive MIMO 2.0: Understanding spatial correlation, interference suppression, and pilot contamination

    Luca Sanguinetti;Emil Björnson;Jakob Hoydis

Frequent Co-Authors

Merouane Debbah
Merouane Debbah Khalifa University
Stephan ten Brink
Stephan ten Brink University of Stuttgart
Emil Bjornson
Emil Bjornson Royal Institute of Technology
Luca Sanguinetti
Luca Sanguinetti University of Pisa
Mari Kobayashi
Mari Kobayashi Technical University of Munich
Bruno Clerckx
Bruno Clerckx Imperial College London
Yonina C. Eldar
Yonina C. Eldar Weizmann Institute of Science
Vincenzo Sciancalepore
Vincenzo Sciancalepore NEC Laboratories Europe GmbH

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