World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
19852
World Ranking
8567
National Ranking
3664

Jakob Hoydis publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Jakob Hoydis sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 131 publications — 19th percentile

19% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Jakob Hoydis D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Jakob Hoydis sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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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