Sebastian Cammerer

2.8k total citations · 2 hit papers
28 papers, 1.5k citations indexed

About

Sebastian Cammerer is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Sebastian Cammerer has authored 28 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Electrical and Electronic Engineering, 16 papers in Computer Networks and Communications and 10 papers in Artificial Intelligence. Recurrent topics in Sebastian Cammerer's work include Error Correcting Code Techniques (14 papers), Advanced Wireless Communication Techniques (11 papers) and Wireless Signal Modulation Classification (8 papers). Sebastian Cammerer is often cited by papers focused on Error Correcting Code Techniques (14 papers), Advanced Wireless Communication Techniques (11 papers) and Wireless Signal Modulation Classification (8 papers). Sebastian Cammerer collaborates with scholars based in Germany, France and Switzerland. Sebastian Cammerer's co-authors include Stephan ten Brink, Jakob Hoydis, Sebastian Dörner, Tobias Gruber, Alexander Keller, Merlin Nimier-David, Fayçal Ait Aoudia, Nikolaus B. Binder, Guillermo Marcus and Laurent Schmalen and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and IEEE Transactions on Communications.

In The Last Decade

Sebastian Cammerer

25 papers receiving 1.5k citations

Hit Papers

Deep Learning Based Communication Over the Air 2017 2026 2020 2023 2017 2017 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Learning Based Communication Over the Air
    2017 548 citations Sebastian Dörner, Sebastian Cammerer et al. IEEE Journal of Selected Topics in Signal Processing profile →
  2. On deep learning-based channel decoding
    2017 351 citations Tobias Gruber, Sebastian Cammerer et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sebastian Cammerer Germany 14 1.0k 804 617 218 198 28 1.5k
Sudhan Majhi India 21 1.1k 1.1× 541 0.7× 608 1.0× 120 0.6× 364 1.8× 144 1.6k
S.S. Soliman United States 13 688 0.7× 476 0.6× 411 0.7× 191 0.9× 292 1.5× 45 1.1k
Tugba Erpek United States 19 552 0.5× 695 0.9× 511 0.8× 56 0.3× 248 1.3× 56 1.2k
Baoming Bai China 20 1.7k 1.6× 240 0.3× 1.1k 1.8× 96 0.4× 250 1.3× 240 1.9k
A. J. Han Vinck Germany 22 2.0k 1.9× 436 0.5× 765 1.2× 147 0.7× 44 0.2× 158 2.3k
Richard D. Wesel United States 29 2.7k 2.6× 520 0.6× 2.9k 4.7× 163 0.7× 82 0.4× 191 3.2k
Jung-Fu Cheng United States 14 1.1k 1.0× 343 0.4× 976 1.6× 64 0.3× 79 0.4× 47 1.4k
Robert G. Maunder United Kingdom 25 1.9k 1.9× 365 0.5× 1.5k 2.4× 77 0.4× 757 3.8× 158 2.5k
David Declercq France 26 2.2k 2.1× 388 0.5× 2.3k 3.8× 284 1.3× 83 0.4× 124 2.6k
Nazanin Rahnavard United States 21 872 0.8× 309 0.4× 983 1.6× 52 0.2× 50 0.3× 91 1.5k

Countries citing papers authored by Sebastian Cammerer

Since Specialization
Citations

This map shows the geographic impact of Sebastian Cammerer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sebastian Cammerer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sebastian Cammerer more than expected).

Fields of papers citing papers by Sebastian Cammerer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sebastian Cammerer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sebastian Cammerer. The network helps show where Sebastian Cammerer may publish in the future.

Co-authorship network of co-authors of Sebastian Cammerer

This figure shows the co-authorship network connecting the top 25 collaborators of Sebastian Cammerer. A scholar is included among the top collaborators of Sebastian Cammerer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sebastian Cammerer. Sebastian Cammerer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cammerer, Sebastian, et al.. (2025). AI-Native 6G: Empowering Intelligent RAN With Accelerated Compute. IEEE Wireless Communications. 32(6). 11–14.
2.
Cammerer, Sebastian, et al.. (2025). Sionna Research Kit: A GPU-Accelerated Research Platform for AI-RAN. 1–2.
3.
Cammerer, Sebastian, et al.. (2025). Design of a Standard-Compliant Real-Time Neural Receiver for 5G NR. 1–6. 5 indexed citations
4.
Hoydis, Jakob, et al.. (2024). Learning Radio Environments by Differentiable Ray Tracing. SHILAP Revista de lepidopterología. 2. 1527–1539. 19 indexed citations
5.
Cammerer, Sebastian, et al.. (2024). Graph Neural Networks for Enhanced Decoding of Quantum LDPC Codes. 2700–2705. 2 indexed citations
6.
Hoydis, Jakob, Fayçal Ait Aoudia, Sebastian Cammerer, et al.. (2023). Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling. 317–321. 69 indexed citations
7.
Dörner, Sebastian, et al.. (2022). Adaptive NN-based OFDM Receivers: Computational Complexity vs. Achievable Performance. 194–199. 5 indexed citations
8.
Dörner, Sebastian, et al.. (2022). On End-to-End Learning of Joint Detection and Decoding for Short-Packet Communications. 2022 IEEE Globecom Workshops (GC Wkshps). 1 indexed citations
9.
Dörner, Sebastian, et al.. (2022). Adaptive Neural Network-based OFDM Receivers. 1–5. 13 indexed citations
10.
Dörner, Sebastian, et al.. (2022). Learning Joint Detection, Equalization and Decoding for Short-Packet Communications. IEEE Transactions on Communications. 71(2). 837–850. 13 indexed citations
11.
Cammerer, Sebastian, et al.. (2022). Graph Neural Networks for Channel Decoding. 2022 IEEE Globecom Workshops (GC Wkshps). 486–491. 31 indexed citations
12.
13.
14.
Cammerer, Sebastian, et al.. (2019). Near-Capacity Detection and Decoding: Code Design for Dynamic User Loads in Gaussian Multiple Access Channels. IEEE Transactions on Communications. 67(11). 7417–7430. 15 indexed citations
15.
Cammerer, Sebastian, et al.. (2019). Decoder-in-the-Loop: Genetic Optimization-Based LDPC Code Design. IEEE Access. 7. 141161–141170. 22 indexed citations
16.
Cammerer, Sebastian, et al.. (2019). Decoder-Tailored Polar Code Design Using the Genetic Algorithm. IEEE Transactions on Communications. 67(7). 4521–4534. 79 indexed citations
17.
Cammerer, Sebastian, et al.. (2018). Belief Propagation List Decoding of Polar Codes. IEEE Communications Letters. 22(8). 1536–1539. 94 indexed citations
18.
Dörner, Sebastian, Sebastian Cammerer, Jakob Hoydis, & Stephan ten Brink. (2017). Deep Learning Based Communication Over the Air. IEEE Journal of Selected Topics in Signal Processing. 12(1). 132–143. 548 indexed citations breakdown →
19.
Dörner, Sebastian, Sebastian Cammerer, Jakob Hoydis, & Stephan ten Brink. (2017). On deep learning-based communication over the air. 1791–1795. 39 indexed citations
20.
Cammerer, Sebastian, Tobias Gruber, Jakob Hoydis, & Stephan ten Brink. (2017). Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning. 1–6. 137 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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