The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Deep Learning Based Communication Over the Air
Open accessAbstract
End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). In this paper, we demonstrate that over-the-air transmissions are possible: We build, train, and run a complete communications system solely composed of NNs using unsynchronized off-the-shelf software-defined radios and open-source deep learning software libraries. We extend the existing ideas toward continuous data transmission, which eases their current restriction to short block lengths but also entails the issue of receiver synchronization. We overcome this problem by introducing a frame synchronization module based on another NN. A comparison of the BLER performance of the “learned” system with that of a practical baseline shows competitive performance close to 1 dB, even without extensive hyperparameter tuning. We identify several practical challenges of training such a system over actual channels, in particular, the missing channel gradient, and propose a two-step learning procedure based on the idea of transfer learning that circumvents this issue.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Theano: A Python framework for fast computation of mathematical expressionsPreprint
The Theano Development Team, Al‐Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.02688 · Cited by 2,176
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., & Zhang, Z. (2015) · arXiv · doi:10.48550/arxiv.1512.01274 · Cited by 1,819
Static Analysis of Shape in TensorFlow ProgramsPreprint
The Theano Development Team, Al‐Rfou, R., Alain, G., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.ecoop.2020.15 · Cited by 1,629
Torch7: A Matlab-like Environment for Machine Learning
Collobert, R., Kavukcuoglu, K., & Farabet, C. (2011) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,241
Deep Learning for Identifying Metastatic Breast CancerPreprint
Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. H. (2016) · arXiv.org · doi:10.48550/arxiv.1606.05718 · Cited by 1,028
8-PSK trellis codes for a Rayleigh channel
Zehavi, E. (1992) · IEEE Transactions on Communications · doi:10.1109/26.141453 · Cited by 1,018
On deep learning-based channel decoding
Gruber, T., Cammerer, S., Hoydis, J., & ten Brink, S. (2017) · Conference on Information Sciences and Systems · doi:10.1109/ciss.2017.7926071 · Cited by 593
Self-citationA Survey on Machine-Learning Techniques in Cognitive Radios
Bkassiny, M., Li, Y., & Jayaweera, S. K. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.100412.00017 · Cited by 580
Learning to decode linear codes using deep learning
Nachmani, E., Be'ery, Y., & Burshtein, D. (2016) · 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton) · doi:10.1109/allerton.2016.7852251 · Cited by 561
Multirate Signal Processing for Communication Systems
Harris, F. J. (2022) · River Publishers eBooks · doi:10.1201/9781003338888 · Cited by 472
Samuel, N., Diskin, T., & Wiesel, A. (2017) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc.2017.8227772 · Cited by 471
Parallel distributed processing explorations in the microstructure of cognition
McClelland, J. L., & Rumelhart, D. E. (1986) · Cited by 390
Deep neural networks to enable real-time multimessenger astrophysics
George, D., & Huerta, E. A. (2018) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.97.044039 · Cited by 319
Learning to invert: Signal recovery via Deep Convolutional Networks
Mousavi, A., & Baraniuk, R. G. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952561 · Cited by 302
Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention
O’Shea, T. J., Karra, K., & Clancy, T. C. (2016) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2016.7886039 · Cited by 236
A survey of results for deletion channels and related synchronization channels
Mitzenmacher, M. (2009) · Probability Surveys · doi:10.1214/08-ps141 · Cited by 235
Applications of neural networks to digital communications – a survey
Ibnkahla, M. A. (2000) · Signal Processing · doi:10.1016/s0165-1684(00)00030-x · Cited by 233
Learning to Protect Communications with Adversarial Neural Cryptography
Abadi, M., & Andersen, D. G. (2016) · arXiv · doi:10.48550/arxiv.1610.06918 · Cited by 182
Detection Algorithms for Communication Systems Using Deep LearningPreprint
Farsad, N., & Goldsmith, A. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08044 · Cited by 144
Zorzi, M., Zanella, A., Testolin, A., Grazia, M. D. F. D., & Zorzi, M. (2015) · IEEE Access · doi:10.1109/access.2015.2471178 · Cited by 120
Radio transformer networks: Attention models for learning to synchronize in wireless systems
O’Shea, T. J., Pemula, L., Batra, D., & Clancy, T. C. (2016) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2016.7869126 · Cited by 98
Onsager-corrected deep learning for sparse linear inverse problems
Borgerding, M., & Schniter, P. (2016) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2016.7905837 · Cited by 95
RNN Decoding of Linear Block Codes
Nachmani, E., Marciano, E., Burshtein, D., & Be'ery, Y. (2017) · arXiv · doi:10.48550/arxiv.1702.07560 · Cited by 93
Unsupervised representation learning of structured radio communication signals
O’Shea, T. J., Corgan, J., & Clancy, T. C. (2016) · International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) · doi:10.1109/splim.2016.7528397 · Cited by 92
An Introduction to Machine Learning Communications Systems.Preprint
O’Shea, T. J., & Hoydis, J. (2017) · arXiv (Cornell University) · Cited by 83
Self-citationBlind detection for MIMO systems with low-resolution ADCs using supervised learning
Jeon, Y., Hong, S., & Lee, N. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7997434 · Cited by 60
On Deep Learning-Based Channel DecodingPreprint
Gruber, T., Cammerer, S., Hoydis, J., & ten Brink, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.07738 · Cited by 55
Self-citationLearning to Invert: Signal Recovery via Deep Convolutional NetworksPreprint
Mousavi, A., & Baraniuk, R. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.03891 · Cited by 45
Muhammad, N. S., & Speidel, J. (2005) · IEEE Communications Letters · doi:10.1109/lcomm.2005.09013 · Cited by 38
Unsupervised Representation Learning of Structured Radio Communication SignalsPreprint
O’Shea, T. J., Corgan, J., & Clancy, T. C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.07078 · Cited by 8
Blind Detection for MIMO Systems With Low-Resolution ADCs Using Supervised Learning
Jeon, Y., Hong, S., & Lee, N. (2016) · Cited by 2
Research works printed but not linked: 1
- goodfellow, 2016, Deep Learning
Cited by
Sorted by citations
On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds
Wang, C., You, X., Gao, X., Zhu, X., Li, Z., Zhang, C., Wang, H., Huang, Y., Chen, Y., Haas, H., et al. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3249835 · Cited by 2,613
Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts
You, X., Wang, C., Huang, J., Gao, X., Zhang, Z., Wang, M., Huang, Y., Zhang, C., Jiang, Y., Wang, J., et al. (2020) · Science China Information Sciences · doi:10.1007/s11432-020-2955-6 · Cited by 2,110
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,913
Deep Learning Enabled Semantic Communication Systems
Xie, H., Qin, Z., Li, G. Y., & Juang, B.-H. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3071210 · Cited by 1,602
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research
de Alwis, C., Kalla, A., Pham, Q., Kumar, P., Dev, K., Hwang, W., & Liyanage, M. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3071496 · Cited by 809
Metadata
- Authors
- Dorner, S., Cammerer, S., Hoydis, J., & Brink, S. T.
- Affiliations
- University of Stuttgart (DE)
- Nokia (France) (FR)
- Venue
- IEEE Journal of Selected Topics in Signal Processing
- Published
- 2018-02
- Volume
- 12
- Issue
- 1
- Pages
- 132-143
- ISSN
- 1932-4553 (ISSN-L)
- 1941-0484
- OpenAlex
- W2736068844
- Cited by
- 859
- References
- 35
- Citations by year
- 2026: 44 · 2025: 76 · 2024: 85 · 2023: 93 · 2022: 131 · 2021: 133 · 2020: 139 · 2019: 120 · 2018: 37
- FWCI
- 48.45
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Journal of Selected Topics in Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Wireless Signal Modulation Classification
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Keywords
- software-defined radio, frame synchronization, block error rate, transfer learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
