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

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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

Online Label Recovery for Deep Learning-based Communication through Error Correcting Codes

Schibisch, S., Cammerer, S., Dörner, S., Hoydis, J., & ten Brink, S. (2018) · International Symposium on Wireless Communication Systems · doi:10.1109/iswcs.2018.8491189 · Cited by 30

Deep Learning-Based Constellation Optimization for Physical Network Coding in Two-Way Relay Networks

Matsumine, T., Koike–Akino, T., & Wang, Y. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761963 · Cited by 29

DNN assisted Sphere Decoder

Askri, A., & Othman, G. R.-B. (2019) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2019.8849786 · Cited by 27

WGAN-based Autoencoder Training Over-the-air

Dörner, S., Henninger, M., Cammerer, S., & ten Brink, S. (2020) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc48557.2020.9154335 · Cited by 29

Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information

Barua, A., Zhang, Z., Al-Turjman, F., & Yang, X. (2020) · IEEE Access · doi:10.1109/access.2020.3000599 · Cited by 29

Deep Learning Based Precoding for the MIMO Gaussian Wiretap Channel

Zhang, X., & Vaezi, M. (2019) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps45667.2019.9024579 · Cited by 27

Recurrent neural networks achieving MLSE performance for optical channel equalization

Kalla, S. C. K., Gagné, C., Zeng, M., & Rusch, L. A. (2021) · Optics Express · doi:10.1364/oe.423103 · Cited by 30

End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model

Neskorniuk, V., Carnio, A., Bajaj, V., Marsella, D., Turitsyn, S. K., Prilepsky, J. E., & Aref, V. (2021) · European Conference on Optical Communication · doi:10.1109/ecoc52684.2021.9605928 · Cited by 28

Innovative Variational AutoEncoder for an End-to-End Communication System

Alawad, M. A., Hamdan, M. Q., & Hamdi, K. A. (2022) · IEEE Access · doi:10.1109/access.2022.3224922 · Cited by 28

Decentralized DNN Task Partitioning and Offloading Control in MEC Systems With Energy Harvesting Devices

Wang, F., Cai, S., & Lau, V. K. N. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3221850 · Cited by 28

Detection and Channel Equalization with Deep Learning for Low Resolution MIMO Systems

Junior, A. B. D. R. K., Prelcic, N. G., Mezghani, A., & Heath, R. W. (2018) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2018.8645551 · Cited by 28

An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning

Wen, D., Li, X., Zeng, Q., Ren, J., & Huang, K. (2019) · Journal of Communications and Information Networks · doi:10.23919/jcin.2019.9005429 · Cited by 28

Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration

Zhang, J., Xia, W., You, M., Zheng, G., Lambotharan, S., & Wong, K. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.2977340 · Cited by 29

Federated Reinforcement Learning for Wireless Networks: Fundamentals, Challenges and Future Research Trends

Das, S. K., Mudi, R., Rahman, M. S., Rabie, K. M., & Li, X. (2024) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2024.3466858 · Cited by 27

Predictive UAV Base Station Deployment and Service Offloading With Distributed Edge Learning

Zhao, Z., Pacheco, L., Santos, H., Liu, M., Maio, A. D., Rosário, D., Cerqueira, E., Braun, T. I., & Cao, X. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3123216 · Cited by 27

SRLoRa: Neural-enhanced LoRa Weak Signal Decoding with Multi-gateway Super Resolution

Du, J., Ren, Y., Zhu, Z., Li, C., Cao, Z., Ma, Q., & Liu, Y. (2023) · ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) · doi:10.1145/3565287.3610254 · Cited by 27

A Deep Learning Approach to Universal Binary Visible Light Communication Transceiver

Lee, H., Quek, T. Q. S., & Lee, S. H. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2950026 · Cited by 27

Deep Autoencoder Learning for Relay-Assisted Cooperative Communication Systems

Lu, Y., Cheng, P., Chen, Z., Li, Y., Mow, W. H., & Vucetic, B. S. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.2998538 · Cited by 25

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

Park, J., Samarakoon, S., Elgabli, A., Kim, J., Bennis, M., Kim, S.-L., & Debbah, M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3055679 · Cited by 25

Autoencoder-Based Optical Wireless Communications Systems

Soltani, M., Fatnassi, W., Aboutaleb, A., Rezki, Z., Bhuyan, A., & Titus, P. (2018) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/glocomw.2018.8644104 · Cited by 26

Deep Learning Methods in Communication Systems: A Review

Liao, F., Wei, S., & Zou, S. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1617/1/012024 · Cited by 26

Residual-Aided End-to-End Learning of Communication System Without Known Channel

Jiang, H., Bi, S., Dai, L., Wang, H., & Zhang, J. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3161936 · Cited by 25

Toward intelligent wireless communications: Deep learning - based physical layer technologies

Liu, S., Wang, T., & Wang, S. (2021) · Digital Communications and Networks · doi:10.1016/j.dcan.2021.09.014 · Cited by 26

Deep Neural Network-Aided Soft-Demapping in Coherent Optical Systems: Regression Versus Classification

Freire, P. J., Prilepsky, J. E., Osadchuk, Y., Turitsyn, S. K., & Aref, V. (2022) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3213284 · Cited by 27

Error Analysis of Free Space Communication System Using Machine Learning

Altalbe, A. A., Khan, M. N., & Tahir, M. (2023) · IEEE Access · doi:10.1109/access.2023.3238099 · Cited by 26

A Learning-Based End-to-End Wireless Communication System Utilizing a Deep Neural Network Channel Module

An, Y., Wang, S., Li, Z., Ji, Z., & Ганчев, И. (2023) · IEEE Access · doi:10.1109/access.2023.3245330 · Cited by 26

Deep Neural Network Assisted Approach for Antenna Selection in Untrusted Relay Networks

Yao, R., Zhang, Y., Wang, S., Qi, N., Miridakis, N. I., & Tsiftsis, T. A. (2019) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2019.2933392 · Cited by 27

A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications

Zhang, T., Liu, S., Xiang, W., Xu, L., Qin, K., & Yan, X. L. (2019) · Sensors · doi:10.3390/s19163541 · Cited by 26

Learning to Communicate and Energize: Modulation, Coding, and Multiple Access Designs for Wireless Information-Power Transmission

Varasteh, M., Hoydis, J., & Clerckx, B. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.3017020 · Cited by 26

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency ApplicationsPreprint

Wong, L. J., Clark, W. H., Flowers, B., Buehrer, R. M., Michaels, A. J., & Headley, W. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00432 · Cited by 30

Gradient-free training of autoencoders for non-differentiable communication channels

Jovanovic, O., Yankov, M. P., Ros, F. D., & Zibar, D. (2021) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · Cited by 25

Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments

Song, J., Häger, C., Schröder, J., Amat, A. G. I., & Wymeersch, H. (2022) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2022.3163474 · Cited by 24

End-to-End Learning-Based Full-Duplex Amplify-and-Forward Relay Networks

Gupta, A. K., Sellathurai, M., & Ratnarajah, T. (2022) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3225460 · Cited by 24

Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks

Mao, C., Mu, Z., Liang, Q., Schizas, I. D., & Pan, C. (2023) · IET Communications · doi:10.1049/cmu2.12669 · Cited by 28

A Deep Learning Approach to Radio Signal Denoising

Almazrouei, E., Gianini, G., Almoosa, N. I., & Damiani, E. (2019) · IEEE Wireless Communications and Networking Conference Workshops (WCNCW) · doi:10.1109/wcncw.2019.8902756 · Cited by 24

Deep Learning-Aided Optical IM/DD OFDM Approaches the Throughput of RF-OFDM

Luong, T. V., Zhang, X., Xiang, L., Hoang, T. M., Xu, C., Petropoulos, P., & Hanzo, L. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126080 · Cited by 23

Deep-Unfolding Neural-Network Aided Hybrid Beamforming Based on Symbol-Error Probability Minimization

Shi, S., Cai, Y., Hu, Q., Champagne, B., & Hanzo, L. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3201961 · Cited by 24

A DNN-based OTFS Transceiver with Delay-Doppler Channel Training and IQI Compensation

Naikoti, A., & Chockalingam, A. (2021) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc50174.2021.9569634 · Cited by 23

Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal Multiple Access System

Gaballa, M., Abbod, M., & Aldallal, A. S. (2022) · Sensors · doi:10.3390/s22103666 · Cited by 23

Deep-Learning Based Signal Detection for MIMO-OTFS Systems

Enku, Y. K., Bai, B., Li, S., Liu, M., & Tiba, I. N. (2022) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops53468.2022.9814608 · Cited by 23

Underwater Wireless Optical Communication With One-Bit Quantization: A Hybrid Autoencoder and Generative Adversarial Network Approach

Zou, C., Yang, F. C., Song, J., & Han, Z. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3243212 · Cited by 23

Deep learning and expert knowledge based underwater acoustic OFDM receiver

Zhang, Y., Chang, J., Liu, Y., Xing, L., & Shen, X. (2023) · Physical Communication · doi:10.1016/j.phycom.2023.102041 · Cited by 24

Deep Neural Network Augmented Wireless Channel Estimation for Preamble-Based OFDM PHY on Zynq System on Chip

Haq, S. A. U., Gizzini, A. K., Shrey, S., Darak, S. J., Saurabh, S., & Chafii, M. (2023) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2023.3274555 · Cited by 24

Low Complexity Deep Learning Augmented Wireless Channel Estimation for Pilot-Based OFDM on Zynq System on Chip

Sharma, A., Haq, S. A. U., & Darak, S. J. (2024) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2024.3371780 · Cited by 23

CWGAN-Based Channel Modeling of Convolutional Autoencoder-Aided SCMA for Satellite-Terrestrial Communication

Li, D., Liu, X., Yin, Z., Cheng, N., & Liu, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3425470 · Cited by 23

DeepSense: Enabling Carrier Sense in Low-Power Wide Area Networks Using Deep LearningPreprint

Chan, J., Wang, A., Krishnamurthy, A., & Gollakota, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.10607 · Cited by 23

Learning to Continuously Optimize Wireless Resource in Episodically Dynamic Environment

Sun, H., Pu, W., Zhu, M., Fu, X., Chang, T.-H., & Hong, M. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9413503 · Cited by 21

FluidNet-Lite: Lightweight convolutional neural network for pore-scale modeling of multiphase flow in heterogeneous porous media

Yaqoob, M., Ansari, M. Y., Ansari, M. I., Ashraf, U., Pavuluri, S., Rabbani, A., Rabbani, H. S., & Seers, T. (2025) · Advances in Water Resources · doi:10.1016/j.advwatres.2025.104952 · Cited by 22

Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing

Karanov, B., Liga, G., Aref, V., Lavery, D., Bayvel, P., & Schmalen, L. (2019) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2019.8919850 · Cited by 21

Data-Rate Driven Transmission Strategies for Deep Learning-Based Communication Systems

Chen, X., Cheng, J., Zhang, Z., Wu, L., Dang, J., & Wang, J. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.2968314 · Cited by 22

Model-Driven DNN Decoder for Turbo Codes: Design, Simulation, and Experimental Results

He, Y., Zhang, J., Jin, S., Wen, C.-K., & Li, G. Y. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.3010964 · Cited by 22

Blind identification of convolutional codes based on deep learning

Wang, J., Tang, C., Huang, H., Wang, H., & Li, J. (2021) · Digital Signal Processing · doi:10.1016/j.dsp.2021.103086 · Cited by 26

Learning Joint Detection, Equalization and Decoding for Short-Packet Communications

Dörner, S., Clausius, J., Cammerer, S., & ten Brink, S. (2022) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3228648 · Cited by 22

Deep-Learning-Based Robust Channel Estimation for MIMO IoT Systems

Kang, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3324667 · Cited by 23

Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration

Zhang, J., Xia, W., You, M., Zheng, G., Lambotharan, S., & Wong, K. (2020) · Figshare · Cited by 22

A Novel DNN Based Channel Estimator for Underwater Acoustic Communications with IM-OFDM

Zhou, M., Wang, J., Sun, H., Qi, J., Feng, X., & Esmaiel, H. (2020) · IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) · doi:10.1109/icspcc50002.2020.9259486 · Cited by 21

Deep Learning Techniques for OFDM Systems

Meenalakshmi, M., Chaturvedi, S., & Dwivedi, V. K. (2021) · IETE Journal of Research · doi:10.1080/03772063.2021.1986154 · Cited by 21

Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training

Manoj, B. R., Sadeghi, M., & Larsson, E. G. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3147203 · Cited by 22

A ResNet-DNN based Channel Estimation and Equalization Scheme in FBMC/OQAM Systems

Cheng, X., Liu, D., Zhu, Z., Shi, W., & Li, Y. (2018) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2018.8555649 · Cited by 21

Data-Driven Network Simulation for Performance Analysis of Anticipatory Vehicular Communication Systems

Sliwa, B., & Wietfeld, C. (2019) · IEEE Access · doi:10.1109/access.2019.2956211 · Cited by 20

Deep Neural Network Aided Low-Complexity MPA Receivers for Uplink SCMA Systems

Cheng, H., Xia, Y., Huang, Y., Lu, Z., & Yang, L. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3099640 · Cited by 21

Free Space Optical Semantic Communication for Satellite Remote Sensing Image Transmission

Chen, W., Ju, C., Yuan, T., Zhan, Y., Zhang, M., & Wang, D. (2025) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3562356 · Cited by 21

Using AutoEncoders for Radio Signal Denoising

Almazrouei, E., Gianini, G., Mio, C., Almoosa, N. I., & Damiani, E. (2019) · ACM Symposium on QoS and Security for Wireless and Mobile Networks (Q2SWinet) · doi:10.1145/3345837.3355949 · Cited by 20

DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems

Wiesmayr, R., Dick, C., Hoydis, J., & Studer, C. (2022) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf56349.2022.10051944 · Cited by 20

Efficient Autoencoder-Based Human Body Communication Transceiver for WBAN

Ali, A., Inoue, K., Shalaby, A., Sayed, M. S., & Ahmed, S. M. (2019) · IEEE Access · doi:10.1109/access.2019.2936796 · Cited by 21

Turbo Detection Aided Autoencoder for Multicarrier Wireless Systems: Integrating Deep Learning Into Channel Coded Systems

Xu, C., Luong, T. V., Xiang, L., Sugiura, S., Maunder, R. G., Yang, L., & Hanzo, L. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3168725 · Cited by 23

Deep-learning Autoencoder for Coherent and Nonlinear Optical CommunicationPreprint

Uhlemann, T., Cammerer, S., Span, A., Dörner, S., & ten Brink, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15027 · Cited by 19

Learning Automata Based Competition Scheme to Train Deep Neural Networks

Guo, H., Li, S., Qi, K., Guo, Y., & Xu, Z. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2868474 · Cited by 20

Channel Estimation and Hybrid Precoding for Millimeter Wave Communications: A Deep Learning-Based Approach

Lu, Q., Lin, T. R., & Zhu, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3108625 · Cited by 20

SVD-Embedded Deep Autoencoder for MIMO Communications

Zhang, X., Vaezi, M., & O’Shea, T. J. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838265 · Cited by 19

A data-driven deep learning network for massive MIMO detection with high-order QAM

Yu, Y., Ying, J., Wang, P., & Guo, L. (2023) · Journal of Communications and Networks · doi:10.23919/jcn.2022.000055 · Cited by 19

Open-Set Signal Recognition Based on Transformer and Wasserstein Distance

Zhang, W., Huang, D. Y., Zhou, M., Lin, J., & Wang, X. (2023) · Applied Sciences · doi:10.3390/app13042151 · Cited by 19

Deep Learning-based Frame and Timing Synchronization for End-to-End Communications

Wu, H., Sun, Z., & Zhou, X. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1169/1/012060 · Cited by 17

Conditional Mutual Information Neural Estimator

Molavipour, S., Bassi, G., & Skoglund, M. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053422 · Cited by 19

Optimizing Blockchain Networks with Artificial Intelligence: Towards Efficient and Reliable IoT Applications

Jameel, F., Javaid, U., Sikdar, B., Khan, I., Mastorakis, G. N., & Mavromoustakis, C. X. (2020) · Internet of things · doi:10.1007/978-3-030-44907-0_12 · Cited by 19

Deep learning based wiretap coding via mutual information estimation

Fritschek, R., Schaefer, R. F., & Wunder, G. (2020) · ACM Workshop on Wireless Security and Machine Learning (WiseML) · doi:10.1145/3395352.3402654 · Cited by 19

Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles

Lu, Y., Cheng, P., Chen, Z., Mow, W. H., Li, Y., & Vucetic, B. S. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3036943 · Cited by 20

An Efficient Privacy-preserving Deep Learning Scheme for Medical Image Analysis

Andrew, J., & Karthikeyan, J. (2020) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.22059/jitm.2020.79191 · Cited by 19

An Efficient Heterogeneous Edge-Cloud Learning Framework for Spectrum Data Compression

Wu, G., Zhou, F., Ding, G., Wu, Q., & Li, X. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3153049 · Cited by 19

Deep unfolding of the weighted MMSE beamforming algorithmPreprint

Pellaco, L., Bengtsson, M., & Jaldén, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08448 · Cited by 19

One-Bit Precoding Constellation Design via Autoencoder-Based Deep Learning

Sohrabi, F., & Yu, W. (2019) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf44664.2019.9048810 · Cited by 18

Aggregate Preamble Sequence Design and Detection for Massive IoT With Deep Learning

Mostafa, A. E., Wong, V. W. S., Zhou, Y., Schober, R., Luo, Z., Liao, S., & Ding, M. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3064868 · Cited by 18

Short Blocklength Wiretap Channel Codes via Deep Learning: Design and Performance Evaluation

Rana, V., & Chou, R. A. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3237259 · Cited by 18

Symbol Detection and Channel Estimation for Space Optical Communications Using Neural Network and Autoencoder

Elfikky, A., & Rezki, Z. (2023) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2023.3346811 · Cited by 18

Deep Reinforcement Learning Enables Joint Trajectory and Communication in Internet of Robotic Things

Luo, R., Tian, H., Ni, W., Cheng, J., & Chen, K. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3462450 · Cited by 18

Supervised-Learning for Multi-Hop MU-MIMO Communications With One-Bit Transceivers

Kim, D., Hong, S.-N., & Lee, N. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933965 · Cited by 18

Syndrome-Enabled Unsupervised Learning for Neural Network-Based Polar Decoder and Jointly Optimized Blind Equalizer

Teng, C.-F., & Chen, Y.-L. (2020) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2020.2992593 · Cited by 18

Deep Learning Based Nonlinear Signal Detection in Millimeter-Wave Communications

Liu, H., Xu, Y., Chen, P., Sun, M., Li, B., & Zhao, C. (2020) · IEEE Access · doi:10.1109/access.2020.3020544 · Cited by 18

HybridDeepRx: Deep Learning Receiver for High-EVM Signals

Pihlajasalo, J., Korpi, D., Honkala, M., Huttunen, J. M. J., Riihonen, T., Talvitie, J., Brihuega, A., Uusitalo, M. A., & Valkama, M. (2021) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc50174.2021.9569393 · Cited by 20

Machine learning and deep learning in FSO communication: A comprehensive survey

Al-Imran, Chowdhury, M. Z., Mofidul, R. B., & Jang, Y. M. (2025) · ICT Express · doi:10.1016/j.icte.2025.10.007 · Cited by 17

Bit Error and Block Error Rate Training for ML-Assisted Communication

Wiesmayr, R., Marti, G., Dick, C., Song, H., & Studer, C. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095841 · Cited by 17

Attention-assisted autoencoder neural network for end-to-end optimization of multi-access fiber-terahertz communication systems

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