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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.
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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
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Items 601–700 of 859
Leonard, W. A., Saunders, A., Calabro, M. K., & Olsen, K. (2019) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom47813.2019.9020991 · Cited by 2
Counting Lattice Points in the Sphere using Deep Neural Networks
Askri, A., Othman, G. R.-B., & Ghauch, H. (2019) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf44664.2019.9048858 · Cited by 2
A DNN Framework for Secure Transmissions in UAV-Relaying Networks with a Jamming Receiver
Liu, N., Tang, X., Zhang, R., Wang, D., & Zhai, D. (2020) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct50939.2020.9295902 · Cited by 2
Edge Processing: A LoRa-Based LCDT System for Smart Building With Energy and Delay Constraints
Shilpa, B., Gupta, H. P., & Jha, R. K. (2023) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2022.3204848 · Cited by 3
Detection Over Rapidly Changing Communication Channels Using Deep Learning
Farsad, N., & Goldsmith, A. J. (2018) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2018.8645187 · Cited by 2
A Physical Layer Security Communication Mechanism Based on End-to-End Learning
Wu, H., Sun, Z., & Zhou, X. (2019) · IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · doi:10.1109/imcec46724.2019.8984189 · Cited by 2
Run-Length Limited Decoding for Visible Light Communications: A Deep Learning Approach
Le, D.-D., Nguyen, D.-P., Tran, T.-H., & Nakashima, Y. (2019) · Asia-Pacific Conference on Communications (APCC) · doi:10.1109/apcc47188.2019.9026486 · Cited by 2
Auto-encoder Based Analog Network Coding for Free-Space Optical Two-Way Communications
Li, J., & Xiong, J. (2019) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc47050.2019.9064418 · Cited by 2
Complete Stacked Denoising Auto-Encoders for Regression
Fernández-García, M.-E., Sancho‐Gómez, J., Ros-Ros, A., & Figueiras-Vidal, A. R. (2021) · Neural Processing Letters · doi:10.1007/s11063-020-10419-0 · Cited by 2
Autoencoder-bank based design for adaptive channel-blind robust transmission
Safi, H., Akbari, M., Vaezpour, E., Parsaeefard, S., & Shubair, R. M. (2021) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-021-01929-z · Cited by 2
A Kalman-based Autoencoder Framework for End-to-End Communication Systems
Hu, B., Wang, J., Chen, X., Zhang, G., & Li, R. (2021) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc50174.2021.9569618 · Cited by 3
Learning the Impedance Entanglement for Wireline Data Communication
Tonello, A. M., Letizia, N. A., & Piante, M. D. (2021) · International Balkan Conference on Communications and Networking (BalkanCom) · doi:10.1109/balkancom53780.2021.9593164 · Cited by 2
Compression Algorithm for End-to-End Communication using CNN
Liang, Y., Lam, C., & Ng, B. K. (2021) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc54389.2021.9674292 · Cited by 2
Deep Learning Based Low Complexity Symbol Detection and Modulation Classification Detector
Hao, C., Dang, X., Li, S., & Chenghua, W. A. N. G. (2022) · IEICE Transactions on Communications · doi:10.1587/transcom.2021ebp3148 · Cited by 2
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Kansal, P., Gangadharappa, M., & Kumar, A. (2022) · Journal of Network and Systems Management · doi:10.1007/s10922-022-09652-w · Cited by 2
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Karanov, B., Bayvel, P., & Schmalen, L. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-385227-2.00010-3 · Cited by 2
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Cao, L., Shu, S., Lin, F., Wang, W., & Gong, C. (2022) · International Conference on Control and Automation · doi:10.1109/icca54724.2022.9831874 · Cited by 2
Deep MMSE Estimation for Data Detection
Mirkarimi, F., Tellambura, C., & Li, G. Y. (2022) · IEEE Communications Letters · doi:10.1109/lcomm.2022.3219382 · Cited by 2
Turbo AI, Part IV: Estimating Uplink Channels for Ultra High Mobility with Sparse Pilots
Chen, Y., Mohammadi, J. A., Wesemann, S., & Wild, T. (2022) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc54779.2022.9978084 · Cited by 2
Transforming towards 6G: Critical Review of Key Performance Indicators
Balan, R. V. S., Deepa, S. M., & Balakrishnan, C. (2022) · International Conference on Circuits, Control, Communication and Computing (I4C) · doi:10.1109/i4c57141.2022.10057812 · Cited by 2
Zhu, J., Bai, C., Zhu, Y., Lu, X., & Wang, K. (2023) · Nano Communication Networks · doi:10.1016/j.nancom.2023.100456 · Cited by 2
Turbo AI, Part V: Verifying AI-Enhanced Channel Estimation for RAN from System Level
Chen, Y., Wesemann, S., & Wild, T. (2023) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/eucnc/6gsummit58263.2023.10188274 · Cited by 4
DNN Based Channel Estimation for Indoor Mimo Multipath Visible Light Communication System
Bose, S., Ghorai, S. K., & Kumar, A. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307463 · Cited by 2
Machine Learning Applied to an RF Communication Channel
McCaskey, M., Kukura, E., Corrigan, R., Bhasin, K. B., & Chelmins, D. (2018) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon.2018.8556708 · Cited by 2
A Neural Network Simulator of a Nonstationary Medium in an Adaptive Data Transmission System
Nevzorov, A. A., Orlov, A. A., & Stankevich, D. A. (2019) · Technical Physics Letters · doi:10.1134/s1063785019070241 · Cited by 2
Learning to Communicate with Limited Co-design
Sahai, A., Sanz, J., Subramanian, V., Tran, C., & Vodrahalli, K. (2019) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2019.8919749 · Cited by 2
CsiNet-Plus Model with Truncation and Noise on CSI Feedback
Liu, F., He, X., Li, C., & Xu, Y. (2019) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2019eal2123 · Cited by 2
User Preference Aware Lossless Data Compression at the Edge
Lu, Y., Chen, W., & Poor, H. V. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.2978072 · Cited by 2
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Yıldırım, M. (2020) · European Journal of Science and Technology · doi:10.31590/ejosat.679929 · Cited by 3
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Vedula, K. (2020) · Cited by 2
Scalable Machine Learning Algorithms to Design Massive MIMO Systems
Martí, D. G., Badini, D., Donno, D. D., & Widmer, J. (2021) · ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM) · doi:10.1145/3479239.3485672 · Cited by 2
Wu, F., Wu, J., Kong, Y., Yang, C., Yang, G., Shu, H., Carrault, G., & Senhadji, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.088 · Cited by 2
Data-Rate Driven Transmission Strategy for Deep Learning Based Communication SystemsPreprint
Chen, X., Cheng, J., Zhang, Z., Wu, L., & Dang, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.08869 · Cited by 2
ANN-Based Detection in MIMO-OFDM Systems with Low-Resolution ADCsPreprint
Rezaei, S., & Affes, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.11643 · Cited by 2
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Hemadri, R. V., Rayaluru, A., & Pandya, R. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.09391 · Cited by 2
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Li, D., Deng, K., Zhao, M., Zhang, S., & Zhu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11178 · Cited by 2
Conclusions and future directions
Hong, Y., Thaj, T., & Viterbo, E. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-385028-5.00017-7 · Cited by 2
6G Communication: A Vision on Deep Learning in URLLC
Medha, A. R., Gupta, M., Nayak, S., & Patgiri, R. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-0836-1_45 · Cited by 1
Research on Autoencoders for Higher Order Modulation
Liu, Q., Yu, G., Liu, J., & Zhou, X. (2022) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct56141.2022.10072635 · Cited by 1
Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
Ninković, V., Vukobratović, D., Häger, C., Wymeersch, H., & Amat, A. G. I. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10278853 · Cited by 1
Autoencoders for Signal Enhancement in Communication Systems
Duque, V., Lewandowsky, J., Adrat, M., Hardenbicker, T., & Jax, P. (2024) · International Conference on Military Communications and Information Systems (ICMCIS) · doi:10.1109/icmcis61231.2024.10540784 · Cited by 1
Deep Learning-based Multiuser Physical Layer Communication Without Known Channel
Ji, J., Xiong, Z., Zhu, K., & Quek, T. Q. S. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10571174 · Cited by 2
BEM-ANN Receiver With Linear Complexity for Severe Doubly-Selective Fading Channels
Liu, X., Guan, Y. L., Xie, Y., Qin, Y., & Teh, K. C. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3441559 · Cited by 2
Power Is Knowledge: Distributed and Throughput Optimal Power Control in Wireless Networks
Bistritz, I., & Bambos, N. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3444602 · Cited by 1
Han, M., Gemeda, M. D., Abebe, A. T., & Kang, C. G. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3454264 · Cited by 1
Beck, E., Lin, H.-Y., Rückert, P., Bao, Y., von Helversen, B., Fehrler, S., Tracht, K., & Dekorsy, A. (2026) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2026.3652845 · Cited by 1
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Wu, Q., Liang, Y., Ng, B. K., Lam, C., & Ma, Y. (2025) · IEEE Access · doi:10.1109/access.2024.3524571 · Cited by 1
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Point-to-multipoint coherent PON system modeling based on the diffusion model
Guan, X., Xu, Y., Jiang, W., Wang, X., Han, C., Hu, W., & Yi, L. (2025) · Optics Letters · doi:10.1364/ol.553826 · Cited by 1
Combined compression and encryption of linear wireless sensor network data using autoencoders
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Shatov, V., Shanin, N., Veihelmann, T., Perner, B., Franchi, N., & Lübke, M. (2025) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/eucnc/6gsummit63408.2025.11037089 · Cited by 1
Zhang, Y., Yang, J., Zhang, T., Zhou, X., Tan, B., & Wang, X. (2025) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc65529.2025.11149333 · Cited by 1
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Haußmann, S., Euchner, F., Schoch, B., Wrana, D., Tessmann, A., & Kallfass, I. (2025) · International Journal of Microwave and Wireless Technologies · doi:10.1017/s1759078725102365 · Cited by 1
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Ahmed-Ade, F., Akpan, V. A., & Ogolo, E. O. (2025) · American Journal of Embedded Systems and Applications · doi:10.11648/j.ajesa.20251101.13 · Cited by 2
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Velloso, E. N., Silveira, L. F. Q., & Sousa, E. S. (2026) · IEEE Access · doi:10.1109/access.2026.3659620 · Cited by 1
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Obeed, M., & Jian, M. (2025) · Globecom · doi:10.1109/globecom59602.2025.11432464 · Cited by 1
Zhu, C., Liang, K., Bao, J., Zhao, Z., Yang, Z., Zhang, Z., & Shikh‐Bahaei, M. (2026) · Digital Communications and Networks · doi:10.1016/j.dcan.2026.03.014 · Cited by 1
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Balaji, L., Dhanalakshmi, A., Chempavathy, B., Aswini, S., Parvez, S. Z., & Sridhar, D. (2026) · Telecommunications and Radio Engineering · doi:10.1615/telecomradeng.2026063417 · Cited by 1
Deng, Z., & Sang, Q. (2020) · Electronics · doi:10.3390/electronics9020294 · Cited by 1
Sakran, H. Y. (2021) · Telecommunication Systems · doi:10.1007/s11235-021-00806-w · Cited by 1
Modulation Signal Denoising Based on Auto-encoder
Mo, Z., Li, H., Wang, J., Huang, H., & Li, J. (2021) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb53066.2021.9547158 · Cited by 1
Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
Cwalina, K. K., Rajchowski, P., Olejniczak, A., Błaszkiewicz, O., & Burczyk, R. (2021) · Sensors · doi:10.3390/s21227716 · Cited by 1
Convolutional Self-Attention-Based Multi-User MIMO Demapper
Michon, A., Aoudia, F. A., & Srinath, K. P. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9839254 · Cited by 1
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Zhou, G., Zhuang, Y., Ding, X., Huang, Y., Abbas, S., & Tu, X. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897341 · Cited by 1
Linear MIMO Precoders Design for Finite Alphabet Inputs via Model-Free Training
Cao, C., Feng, B., Wu, Y., Ng, D. W. K., & Zhang, W. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001528 · Cited by 1
Deep reinforcement learning autoencoder with RA-GAN and GAN
Nguyen, H.-S., & Huynh, C.-D. (2022) · International Journal of Advances in Intelligent Informatics · doi:10.26555/ijain.v8i3.896 · Cited by 1
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Yousuf, U., Sambhavi, Nalband, A. H., & Ahmed, M. R. (2023) · International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON) · doi:10.1109/reedcon57544.2023.10151398 · Cited by 1
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García, D., Ruiz, R., Lacruz, J. O., & Widmer, J. (2023) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom53939.2023.10229087 · Cited by 1
Towards Asymmetric Auto-Encoders for the IoBNT
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Fan, J., Sun, Z., Yue, G., & Yu, J. (2023) · Physical Communication · doi:10.1016/j.phycom.2023.102197 · Cited by 1
Ramanathan, S., Kanaparthi, T., Yarrabothu, R. S., & Sundar, R. (2023) · Information Dynamics and Applications · doi:10.56578/ida020303 · Cited by 1
Guo, J., Wen, C.-K., & Jin, S. (2023) · Signals and communication technology · doi:10.1007/978-3-031-37920-8_6 · Cited by 1
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Davey, C. P., Shakeel, I., Deo, R. C., & Salcedo‐Sanz, S. (2023) · Sensors · doi:10.3390/s23249848 · Cited by 1
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Li, Z., Shi, J., & Chi, N. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-98829-2.00005-0 · Cited by 1
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Aziz, M. A., Rahman, M. H., Tabassum, R., Sejan, M. A. S., Baek, M.-S., & Song, H. (2024) · Mathematics · doi:10.3390/math12233685 · Cited by 1
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Нейросетевой имитатор нестационарной среды в адаптивной системе передачи данных
Невзоров, А. А., Орлов, А. А., & Stankevich, D. A. (2019) · Письма в журнал технической физики · doi:10.21883/pjtf.2019.14.48013.17836 · Cited by 1
Learn to Demodulate: MIMO-OFDM Symbol Detection through Downlink Pilots
Zhou, Z., Liu, L., & Chang, H.-H. (2019) · Cited by 1
Liu, H., Wang, Y., & Hu, B. (2019) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess47205.2019.9040844 · Cited by 1
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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
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