Deep Learning Based Communication Over the Air

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

A survey on transfer learning

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

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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

LoRa DL: a deep learning model for enhancing the data transmission over LoRa using autoencoder

Shilpa, B., Kumar, P. R., & Jha, R. K. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05355-4 · Cited by 16

Implementing Graph Neural Networks Over Wireless Networks via Over-the-Air Computing: A Joint Communication and Computation Framework

Yang, Y., Zhang, Z., Tian, Y., Jin, R., & Huang, C. (2023) · IEEE Wireless Communications · doi:10.1109/mwc.012.2200552 · Cited by 16

GAN Based Channel Estimation and Interference Cancellation for Mixed RF/FSO Cooperative Relaying Systems

Upadhya, A. (2023) · Physical Communication · doi:10.1016/j.phycom.2023.102199 · Cited by 16

Deep Learning Based End-to-End Optical Wireless Communication Systems With Autoencoders

Safi, H., Tavakkolnia, I., & Haas, H. (2024) · IEEE Communications Letters · doi:10.1109/lcomm.2024.3387286 · Cited by 16

Signal Detection in Uplink Pilot-Assisted Multi-User MIMO Systems with Deep Learning

Hua, H., Wang, X., & Xu, Y. (2019) · Computing, Communications and IoT Applications (ComComAp) · doi:10.1109/comcomap46287.2019.9018672 · Cited by 15

Economics of Semantic Communication System: An Auction Approach

Liew, Z. Q., Du, H., Lim, W. Y. B., Xiong, Z., Niyato, D. T., Miao, C., & Kim, D. I. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3278467 · Cited by 18

A Deep Learning Receiver for Underwater Acoustic OTFS Communications With Doppler Squint Effect

Zhang, Y., Wang, Y., Liu, Y., Shi, L., & Zang, Y. (2025) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2025.3538017 · Cited by 15

Deep Learning in IoT

Devika, G., & Karegowda, A. G. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-2108-3.ch001 · Cited by 15

Autoencoder-Based Unequal Error Protection Codes

Ninković, V., Vukobratović, D., Häger, C., Wymeersch, H., & Amat, A. G. I. (2021) · IEEE Communications Letters · doi:10.1109/lcomm.2021.3108845 · Cited by 15

An Improved End-to-End Autoencoder Based on Reinforcement Learning by Using Decision Tree for Optical Transceivers

Zhang, Q., Wang, Z., Duan, S., Cao, B., Wu, Y., Chen, J., Zhang, H., & Wang, M. (2021) · Micromachines · doi:10.3390/mi13010031 · Cited by 16

Deep-learning-based multi-user framework for end-to-end fiber-MMW communications

Li, Z., Jia, J., Li, G., Dong, B., Shen, W., Huang, C., Shi, J., Chi, N., & Zhang, J. (2023) · Optics Express · doi:10.1364/oe.479500 · Cited by 15

Machine learning approach on synchronization for FEC enabled channels

Chadov, T. A., Дмитриевич, Е. С., & Tikhonyuk, A. I. (2018) · 2018Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO) · doi:10.1109/synchroinfo.2018.8456985 · Cited by 15

A Novel Neural Network-Based Method for Decoding and Detecting of the DS8-PSK Scheme in an OCC System

Pham, T. L., Nguyen, H. Q., Nguyen, T., & Jang, Y. M. (2019) · Applied Sciences · doi:10.3390/app9112242 · Cited by 15

Lightweight Machine Learning for Efficient Frequency-Offset-Aware Demodulation

Siyari, P., Rahbari, H., & Krunz, M. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933956 · Cited by 15

Redefining Wireless Communication for 6G:Signal Processing Meets Deep Learning with Deep UnfoldingPreprint

Jagannath, A., Jagannath, J., & Melodia, T. (2021) · doi:10.36227/techrxiv.16434855 · Cited by 15

Semantic Edge Computing and Semantic Communications in 6G networks: A unifying survey and research challenges

Zhang, M., Abdi, M., Dasari, V. R., & Restuccia, F. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111531 · Cited by 26

Design and Prototyping of Neural Network Compression for Non-Orthogonal IoT Signals

Xu, T., & Darwazeh, I. (2019) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2019.8885830 · Cited by 14

Automated deep learning-based wide-band receiver

Azari, B., Cheng, H., Soltani, N., Li, H., Li, Y., Belgiovine, M., Imbiriba, T., D’Oro, S., Melodia, T., Wang, Y., et al. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109367 · Cited by 14

End-to-End Learning for Symbol-Level Precoding and Detection With Adaptive Modulation

Liu, R., Bo, Z., Li, M., & Liu, Q. (2022) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2022.3216848 · Cited by 14

End-to-End Underwater Acoustic Communication Based on Autoencoder with Dense Convolution

Xie, F., Zhu, Y., Wang, B., Wang, W., & Jin, P. (2023) · Electronics · doi:10.3390/electronics12020253 · Cited by 16

Deep-Learning-Based Carrier Frequency Offset Estimation and Its Cross-Evaluation in Multiple-Channel Models

Wang, Z., Wei, S., Zou, L., Liao, F., Lang, W., & Li, Y. (2023) · Information · doi:10.3390/info14020098 · Cited by 14

Learning for Detection: A Deep Learning Wireless Communication Receiver Over Rayleigh Fading Channels

Al-Baidhani, A., & Fan, H. H. (2019) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2019.8685517 · Cited by 14

Deep Learning and Channel Estimation

Thakkar, K., Goyal, A., & Bhattacharyya, B. (2020) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs48705.2020.9074414 · Cited by 14

Image Target Recognition Model of Multi- Channel Structure Convolutional Neural Network Training Automatic Encoder

Zhang, S., Cheng, Q., Chen, D., & Zhang, H. (2020) · IEEE Access · doi:10.1109/access.2020.3003059 · Cited by 14

PU-DetNet: Deep Unfolding Aided Smart Sensing Framework for Cognitive Radio

Soni, B., Patel, D. K., Shah, S. B., López‐Benítez, M., & Govindasamy, S. (2022) · IEEE Access · doi:10.1109/access.2022.3206814 · Cited by 15

Deep Learning based Underwater Acoustic OFDM Receiver with Joint Channel Estimation and Signal Detection

Zhang, Y., Chang, J., Liu, Y., Shen, X., & Bai, W. (2022) · IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) · doi:10.1109/icspcc55723.2022.9984340 · Cited by 14

Deep Learning-Based Beamforming for Millimeter-Wave Systems Using Parametric ReLU Activation Function

Ismail, A. H., Soliman, T. A., Rihan, M., & Dessouky, M. I. (2023) · Wireless Personal Communications · doi:10.1007/s11277-022-10157-7 · Cited by 14

Implementation of DNN-Based Physical-Layer Network Coding

Wang, X., & Lu, L. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3237589 · Cited by 14

Deep-Learning-Based Resource Allocation for Multi-Band Communications in CubeSat Networks

Nie, S., Jornet, J. M., & Akyildiz, I. F. (2019) · IEEE International Conference on Communications workshops · doi:10.1109/iccw.2019.8757157 · Cited by 15

Hybrid optimization‐based deep neuro‐fuzzy network for designing m‐user multiple‐input multiple‐output interference channel

Reddy, G. N., & Kumar, C. V. R. (2023) · International Journal of Communication Systems · doi:10.1002/dac.5606 · Cited by 13

Symbol error rate minimization using deep learning approaches for short-reach optical communication networks

Iqbal, M., Ghafoor, S., Ahmad, A., Aljohani, A. J., Mirza, J., Aziz, I., & Potì, L. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1387284 · Cited by 17

CRissNet: An Efficient and Lightweight Network for CSI Feedback in Massive MIMO Systems

Wang, B., Teng, Y., Zhao, Y., Yu, Y., & Lau, V. K. N. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3486168 · Cited by 13

Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO

Balevi, E., & Andrews, J. G. (2019) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf44664.2019.9048915 · Cited by 13

Joint Constellation Design and Multiuser Detection for Grant-Free NOMA

Ma, Z., Wu, W. R., Jian, M., Gao, F., & Shen, X. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3108666 · Cited by 13

Impulsive Noise Mitigation in Digital Subscriber Lines: The State-of-the-Art and Research Opportunities

Bai, T., Zhang, H., Zhang, J., Xu, C., Rawi, A. F. A., & Hanzo, L. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800858 · Cited by 13

Accelerating wireless channel autoencoders for short coherence-time communications

Morocho-Cayamcela, M. E., & Lim, W. (2020) · Journal of Communications and Networks · doi:10.1109/jcn.2020.000011 · Cited by 13

End-to-End Learning in Optical Fiber Communications: Experimental Demonstration and Future Trends

Karanov, B., Oliari, V., Chagnon, M., Liga, G., Alvarado, A., Aref, V., Lavery, D., Bayvel, P., & Schmalen, L. (2020) · European Conference on Optical Communication · doi:10.1109/ecoc48923.2020.9333265 · Cited by 14

A Review of Machine Learning and Cryptography Applications

Sooksatra, K., & Rivas, P. (2020) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci51800.2020.00105 · Cited by 14

Deep learning-driven MIMO: Data encoding and processing mechanism

Song, Z., & Ma, J. (2022) · Physical Communication · doi:10.1016/j.phycom.2022.101976 · Cited by 12

ResNet-WGAN-Based End-to-End Learning for IoV Communication With Unknown Channels

Zhao, J., Mu, H., Zhang, Q., & Zhang, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3274209 · Cited by 12

Deep Learning-Based Joint Optimization of Closed-Loop FDD mmWave Massive MIMO: Pilot Adaptation, CSI Feedback, and Beamforming

Jee, J., & Park, H. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3327276 · Cited by 13

Deep Learning-Based Detection for Marker Codes Over Insertion and Deletion Channels

Ma, G., Jiao, X., Mu, J., Han, H., & Yang, Y. (2024) · IEEE Transactions on Communications · doi:10.1109/tcomm.2024.3394039 · Cited by 12

Full-Duplex Millimeter Wave MIMO Channel Estimation: A Neural Network Approach

Sattari, M., Guo, H., Gündüz, D., Panahi, A., & Svensson, T. (2024) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2024.3432865 · Cited by 12

Deep Learning-Assisted Multi-Dimensional Modulation and Resource Mapping for Advanced OFDM Systems

Kim, J.-H., Lee, B., Lee, H., Kim, Y., & Lee, J. (2018) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/glocomw.2018.8644281 · Cited by 14

α-Fairness-Maximizing User Association in Energy-Constrained Small Cell Networks

Jang, J., & Yang, H. J. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3158694 · Cited by 12

Learning-Augmented Streaming Codes are Approximately Optimal for Variable-Size Messages

Rudow, M., & Rashmi, K. V. (2022) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit50566.2022.9834539 · Cited by 12

Learning-Aided Physical Layer Attacks Against Multicarrier Communications in IoT

Nooraiepour, A., Bajwa, W. U., & Mandayam, N. B. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.2990657 · Cited by 13

A Deep Learning Based Channel Estimation for High Mobility Vehicular Communications

Mehrabi, M., Mohammadkarimi, M., Ardakani, M., & Jing, Y. (2020) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/icnc47757.2020.9049735 · Cited by 12

Deep Learning based End-to-End Wireless Communication Systems with Conditional GAN as Unknown ChannelPreprint

Ye, H., Liang, L., Li, G. Y., & Juang, B.-H. F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02551 · Cited by 12

COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms

Ninković, V., Vukobratović, D., Mišković, D., & Zennaro, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3487246 · Cited by 12

Model-Free End-to-End Deep Learning of Joint Geometric and Probabilistic Shaping for Optical Fiber Communication in IM/DD System

Li, Z., Huang, O., Yan, A., Li, G., Dong, B., Shen, W., Xing, S., Shi, J., Li, Z., Shen, C., et al. (2024) · Journal of Lightwave Technology · doi:10.1109/jlt.2024.3488121 · Cited by 13

A Deep Learning Wireless Transceiver with Fully Learned Modulation and Synchronization

Schmitz, J., von Lengerke, C., Airee, N., Behboodi, A., & Mathar, R. (2019) · IEEE International Conference on Communications workshops · doi:10.1109/iccw.2019.8757051 · Cited by 11

6G CloudNet: Towards a Distributed, Autonomous, and Federated AI-Enabled Cloud and Edge Computing

Alimi, I. A., Patel, R. K., Zaouga, A., Muga, N. J., Pinto, A. N., Teixeira, A., & Monteiro, P. P. (2021) · Computer communications and networks · doi:10.1007/978-3-030-72777-2_13 · Cited by 11

Turbo Autoencoder with a Trainable Interleaver

Chahine, K., Jiang, Y., Nuti, P., Kim, H., & Cho, J. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9839051 · Cited by 11

End-to-End Deep Learning for Multipair Two-Way Massive MIMO with PA Impairments

Cherif, M., Arfaoui, A., Zayani, R., & Bouallègue, R. (2023) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3232590 · Cited by 11

Probability density function based data augmentation for deep neural network automatic modulation classification with limited training data

Hao, C., Dang, X., Yu, X., Li, S., & Wang, C. (2023) · IET Communications · doi:10.1049/cmu2.12588 · Cited by 11

Identification and practical validation of spectrally efficient non-orthogonal frequency shaping waveform

Xu, T., & Darwazeh, I. (2023) · Communications Engineering · doi:10.1038/s44172-023-00108-w · Cited by 12

Mitigating nonlinear distortions of high-powered LEDs for VLC using deep neural networks

Abhaynarasimha, K. S., Mani, V. V., & Sellathurai, M. (2023) · Optics Communications · doi:10.1016/j.optcom.2023.129997 · Cited by 12

Soft-Decoding for Multi-Set Space-Time Shift-Keying mmWave Systems: A Deep Learning Approach

Satyanarayana, K., El‐Hajjar, M., Mourad, A., Pietraski, P. J., & Hanzo, L. (2020) · IEEE Access · doi:10.1109/access.2020.2973318 · Cited by 11

Neural Mutual Information Estimation for Channel Coding: State-of-the-Art Estimators, Analysis, and Performance Comparison

Fritschek, R., Schaefer, R. F., & Wunder, G. (2020) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc48557.2020.9154239 · Cited by 11

End-to-End Learning in Optical Fiber Communications: Concept and Transceiver Design

Karanov, B., Bayvel, P., & Schmalen, L. (2020) · European Conference on Optical Communication · doi:10.1109/ecoc48923.2020.9333174 · Cited by 14

Spectral Efficient Beamforming for mmWave MISO Systems using Deep Learning Techniques

Nalband, A. H., Sarvagya, M., & Ahmed, M. R. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-05552-4 · Cited by 11

End-to-End Learning-Based Framework for Amplify-and-Forward Relay Networks

Gupta, A. K., & Sellathurai, M. (2021) · IEEE Access · doi:10.1109/access.2021.3085901 · Cited by 12

End-to-End Learning of Communication System without Known Channel

Jiang, H., & Dai, L. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500550 · Cited by 11

Unsupervised Deep Learning for Blind Multiuser Frequency Synchronization in OFDMA Uplink

Li, A., Ma, Y., Xue, S., Yi, N., Tafazolli, R., & Dodgson, T. E. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761937 · Cited by 11

Model-Driven Deep-Learning for End-to-End Optimization in Fiber-Terahertz Communication Systems

Li, Z., Wang, C., Jia, J., Huang, O., Dong, B., Li, G., Xing, S., Zhou, Y., Shi, J., Li, Z., et al. (2024) · Journal of Lightwave Technology · doi:10.1109/jlt.2024.3519360 · Cited by 11

A comparative study of ultra-massive MIMO intelligent receivers with adversarial robustness and energy efficiency for 6G applications

Nidagundi, P., Darvekar, A., Amber, M., & Ramesh, R. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.105409 · Cited by 10

Autoencoder for Optical Wireless Communication System in Atmospheric Turbulence

Liu, X., Wei, Z., Pepe, A., Wang, Z., & Fu, H. Y. (2020) · Opto-Electronics and Communications Conference (OECC) · doi:10.1109/oecc48412.2020.9273524 · Cited by 10

Bilinear Convolutional Auto-encoder based Pilot-free End-to-end Communication Systems

Ye, H., Li, G. Y., & Juang, B.-H. F. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9149030 · Cited by 10

Distance-Agnostic Auto-Encoders for Short Reach Fiber Communications

Karanov, B., Schmalen, L., & Alvarado, A. (2021) · Optical Fiber Communication Conference · doi:10.1364/ofc.2021.tu5d.1 · Cited by 10

Mobility, Path Loss, and Composite Fading: Performance of a Conventional and of a Non-Conventional System With a Robust Autoencoder

Pereira, P. M. R., Bairros, T., de Souza, R. A. A., & Yacoub, M. D. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3294756 · Cited by 10

A RNN Decoder for Channel Decoding under Correlated Noise

Zhang, X., & Luo, T. (2019) · IEEE/CIC International Conference on Communications in China - Workshops (CIC/ICCC) · doi:10.1109/iccchinaw.2019.8849949 · Cited by 11

Deep Learning Techniques for Decoding Polar Codes

Gross, W. J., Doan, N., Mambou, E. N., & Hashemi, S. A. (2019) · Wiley eBooks · doi:10.1002/9781119562306.ch15 · Cited by 10

Fueling the Next Quantum Leap in Cellular Networks: Embracing AI in 5G Evolution towards 6GPreprint

Lin, X., Chen, M., Rydén, H., Jeong, J., Lee, H., Sundberg, M., Timo, R. N., Razaghi, H. S., & Poor, H. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.10663 · Cited by 12

End-to-end learning of adaptive coded modulation schemes for resilient wireless communications

Davey, C. P., Shakeel, I., Deo, R. C., Sharma, E., Salcedo‐Sanz, S., & Soar, J. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111672 · Cited by 11

Deepmod: An Over-the-Air Trainable Machine Modem for Resilient PHY Layer Communications

Anderson, A., Young, S. R., Karnowski, T. P., & Vann, J. M. (2018) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom.2018.8599807 · Cited by 11

Learning to Modulate for Non-coherent MIMO

Wang, Y., & Koike–Akino, T. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9149438 · Cited by 9

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

Arunachalam, G., & Sureshkumar, P. (2022) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.031001 · Cited by 10

Standardization and technology trends of artificial intelligence for mobile systems

Choongil, Y., Choi, Y.-S., Ko, Y., & Kim, I. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.11.004 · Cited by 9

Learning Physical-Layer Communication With Quantized Feedback

Song, J., Peng, B., Häger, C., Wymeersch, H., & Sahai, A. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2951563 · Cited by 9

3-Party Adversarial Cryptography

Meraouche, I., Dutta, S., & Sakurai, K. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-39746-3_27 · Cited by 9

A Mixture Density Channel Model for Deep Learning-Based Wireless Physical Layer Design

Martí, D. G., Palacios, J., Lacruz, J. O., & Widmer, J. (2020) · ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM) · doi:10.1145/3416010.3423229 · Cited by 9

What can Machine Learning do for Radio Spectrum Management?

Almazrouei, E., Gianini, G., Almoosa, N. I., & Damiani, E. (2020) · ACM Symposium on QoS and Security for Wireless and Mobile Networks (Q2SWinet) · doi:10.1145/3416013.3426443 · Cited by 8

NeuroRAN Rethinking Virtualization for AI‐native Radio Access Networks in 6G

Carbone, P., Dán, G., Gross, J., Göransson, B., & Petrova, M. (2022) · Insight · doi:10.1002/inst.12416 · Cited by 9

Beamforming design with fully connected analog beamformer using deep learning

Jeyakumar, P., Tharanitaran, N. M., Malar, E., & Muthuchidambaranathan, P. (2022) · International Journal of Communication Systems · doi:10.1002/dac.5109 · Cited by 9

An Artificial Intelligence-based Error Correction for Optical Camera Communication

Pham, T. L., Nguyen, T., Thieu, M. D., Nguyen, H. Q., Nguyen, H., & Jang, Y. M. (2019) · International Conference on Ubiquitous and Future Networks · doi:10.1109/icufn.2019.8806169 · Cited by 9

Noncoherent MIMO Codes Construction Using Autoencoders

ElMossallamy, M. A., Han, Z., Pan, M., Jäntti, R., Seddik, K. G., & Li, G. Y. (2019) · Globecom · doi:10.1109/globecom38437.2019.9013199 · Cited by 9

Neural Estimators for Conditional Mutual Information Using Nearest Neighbors Sampling

Molavipour, S., Bassi, G., & Skoglund, M. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3050564 · Cited by 9

Deep Learning for MIMO Channel Estimation: Interpretation, Performance, and ComparisonPreprint

Hu, Q., Feifei, G., Zhang, H., Jin, S., & Ye, L. G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.01918 · Cited by 9

Neural Network Equalizer in Visible Light Communication: State of the Art and Future Trends

Shi, J., Huang, O., Ha, Y., Niu, W., Jin, R., Qin, G., Xu, Z., & Chi, N. (2022) · Frontiers in Communications and Networks · doi:10.3389/frcmn.2022.824593 · Cited by 8

GANCE: Generative Adversarial Network Assisted Channel Estimation for Unmanned Aerial Vehicles Empowered 5G and Beyond Wireless Networks

Gupta, C., Das, R. K., Barik, R. K., Qurashi, S. N., Roy, D. S., & Yadav, S. S. (2024) · IEEE Access · doi:10.1109/access.2024.3522847 · Cited by 8

Representation-Based Continual Learning for Channel Estimation in Dynamic Wireless Environments

Kong, L., Liu, X., Zhang, X., Xiong, J., Zhao, H., & Wei, J. (2025) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2025.3552836 · Cited by 8

Multiple-Masks Error Correction Code Transformer for Short Block Codes

Park, S., Kwak, H.-Y., Kim, S., Kim, S., Kim, Y., & No, J. (2025) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2025.3559154 · Cited by 8

Automatically Resolving Intertrack Interference With Convolution Neural Network Detection Channel in TDMR

Qin, Y., & Zhu, J.-G. (2020) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2020.3017373 · Cited by 8

Cooperative Beamforming With Nonlinear Power Amplifiers: A Deep Learning Approach for Distributed Networks

Jee, J., Kwon, G., & Park, H. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3226799 · Cited by 8

Deep Learning-Based Hybrid System for Multiuser MIMO Systems

Kumari, P. R., Chaturvedi, A., Juyal, A., Pant, B., Mydhili, S. K., & Yadav, R. (2022) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i56241.2022.10073464 · Cited by 8

A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Design

Ninković, V., Vukobratović, D., Pastore, A., & Antón‐Haro, C. (2023) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc53906.2023.10304541 · Cited by 8

Deep Deterministic Policy Gradient for End-to-End Communication Systems without Prior Channel Knowledge

Zhang, B., & Huynh, N. V. (2023) · Globecom · doi:10.1109/globecom54140.2023.10436824 · Cited by 8

Image Super Resolution-Based Channel Estimation for Orthogonal Chirp Division Multiplexing on Shallow Water Underwater Acoustic Communications

Liu, H., He, C., Yu, Y., Bai, Y., & Han, Y. (2024) · Sensors · doi:10.3390/s24092846 · Cited by 9

Performance Improvement in Rayleigh Faded Channel using Deep Learning

Ganesh, S., Sunder, V. S., & Thakre, A. (2018) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2018.8554830 · Cited by 8

On deep learning-based communication over the air

Dörner, S., Cammerer, S., Hoydis, J., & ten Brink, S. (2017) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2017.8335670 · Cited by 65

Deep Learning Based Over-the-Air Training of Wireless Communication Systems without Feedback

Davey, C. P., Shakeel, I., Deo, R. C., & Salcedo‐Sanz, S. (2024) · Sensors · doi:10.3390/s24102993 · Cited by 2

A Deep Learning Wireless Transceiver with Fully Learned Modulation and Synchronization

Schmitz, J., von Lengerke, C., Airee, N., Behboodi, A., & Mathar, R. (2019) · IEEE International Conference on Communications workshops · doi:10.1109/iccw.2019.8757051 · Cited by 11

End-to-End Learning of Communications Systems

DE JESUS TORRES, A. (2019) · Electronic Theses and Dissertations Repository (University of Pisa)

Demonstrating Deep Learning Based Communications Systems Over the Air In Practice

O’Shea, T. J., Roy, T., West, N., & Hilburn, B. C. (2018) · IEEE International Symposium on Dynamic Spectrum Access Networks · doi:10.1109/dyspan.2018.8610491 · Cited by 10