A Very Brief Introduction to Machine Learning With Applications to Communication Systems

Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works

Learning Internal Representations by Error Propagation

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501 · see citing works

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 · see citing works

Untitled work

(1999) · Cited by 12,854 · see citing works

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works

Untitled work

(2012) · Cited by 9,062 · see citing works

Reinforcement Learning: An Introduction

Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,821 · see citing works

Loihi: A Neuromorphic Manycore Processor with On-Chip Learning

Davies, M., Srinivasa, N., Lin, T.-H., Chinya, G. N., Cao, Y., Choday, S. H., Dimou, G. D., Joshi, P., Imam, N., Jain, S., et al. (2018) · IEEE Micro · doi:10.1109/mm.2018.112130359 · Cited by 3,846 · see citing works

An Introduction to Signal Detection and Estimation

Poor, H. V. (1988) · Springer texts in electrical engineering · doi:10.1007/978-1-4757-3863-6 · Cited by 3,194 · see citing works

Understanding Machine Learning: From Theory to Algorithms

Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115 · see citing works

Untitled work

(2015) · Cited by 2,857 · see citing works

Pattern recognition and machine learning

(2007) · Choice Reviews Online · doi:10.5860/choice.44-5091 · Cited by 2,782 · see citing works

An Introduction to Signal Detection and Estimation

Poor, H. V. (1994) · Springer texts in electrical engineering · doi:10.1007/978-1-4757-2341-0 · Cited by 2,664 · see citing works

A survey on clustering algorithms for wireless sensor networks

Abbasi, A. A., & Younis, M. (2007) · Computer Communications · doi:10.1016/j.comcom.2007.05.024 · Cited by 2,398 · see citing works

Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning

Sun, Y., Babu, P., & Palomar, D. P. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2601299 · Cited by 1,820 · see citing works

NIPS 2016 Tutorial: Generative Adversarial NetworksPreprint

Goodfellow, I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.00160 · Cited by 1,735 · see citing works

Unsupervised Learning

Barlow, H. B. (1989) · Neural Computation · doi:10.1162/neco.1989.1.3.295 · Cited by 1,677 · see citing works

A survey of techniques for internet traffic classification using machine learning

Nguyen, T. T. T., & Armitage, G. (2008) · IEEE Communications Surveys &amp; Tutorials · doi:10.1109/surv.2008.080406 · Cited by 1,664 · see citing works

The Book of Why: The New Science of Cause and Effect

Goldberg, L. R. (2019) · Notices of the American Mathematical Society · doi:10.1090/noti1912 · Cited by 1,654 · see citing works

Deep Joint Source-Channel Coding for Wireless Image Transmission

Bourtsoulatze, E., Kurka, D. B., & Gündüz, D. (2019) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2019.2919300 · Cited by 1,518 · see citing works

A Closer Look at Memorization in Deep Networks

Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al. (2017) · arXiv · doi:10.48550/arxiv.1706.05394 · Cited by 1,307 · see citing works

Deep Learning for Massive MIMO CSI Feedback

Wen, C.-K., Shih, W.-T., & Jin, S. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2018.2818160 · Cited by 1,232 · see citing works

What can machine learning do? Workforce implications

Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131 · see citing works

Machine Learning Paradigms for Next-Generation Wireless Networks

Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K.-C., & Hanzo, L. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2016.1500356wc · Cited by 1,095 · see citing works

The Book of Why: The New Science of Cause and Effect

Pearl, J., & Mackenzie, D. N. (2018) · Cited by 1,001 · see citing works

Elements of Information Theory

Vitetta, G. M., Taylor, D. P., Colavolpe, G., Pancaldi, F., & Martin, P. A. (2013) · doi:10.1002/9781118576618.ch7 · Cited by 999 · see citing works

Machine learning in wireless sensor networks: Algorithms, strategies, and applications

Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys &amp; Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965 · see citing works

Deep Learning in Physical Layer Communications

Qin, Z., Ye, H., Li, G. Y., & Juang, B.-H. F. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1800601 · Cited by 692 · see citing works

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci, F., Rottondi, C., Nag, A., Irene Macaluso, B., Zibar, D., Ruffini, M., & Tornatore, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2880039 · Cited by 644 · see citing works

Book Review: Pattern Recognition and Machine Learning

Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Alsheikh, M. A., Lin, S., Niyato, D. T., et al. (2014) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · doi:10.48550/arxiv.1405.4463 · Cited by 581 · see citing works

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 574 · see citing works

Deep Learning Methods for Improved Decoding of Linear Codes

Nachmani, E., Marciano, E., Lugosch, L., Gross, W. J., Burshtein, D., & Be'ery, Y. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2788405 · Cited by 539 · see citing works

Unsupervised Learning

Hastie, T., Tibshirani, R. J., & Friedman, J. (2008) · Springer series in statistics · doi:10.1007/978-0-387-84858-7_14 · Cited by 482 · see citing works

Exact Recovery in the Stochastic Block ModelPreprint

Hall, E. G. (2016) · Cited by 441 · see citing works

End-to-End Deep Learning of Optical Fiber Communications

Karanov, B., Chagnon, M., Thouin, F., Eriksson, T. A., Bülow, H., Lavery, D., Bayvel, P., & Schmalen, L. (2018) · Journal of Lightwave Technology · doi:10.1109/jlt.2018.2865109 · Cited by 411 · see citing works

Learning to Explain: An Information-Theoretic Perspective on Model InterpretationPreprint

Chen, J., Song, L., Wainwright, M. J., & Jordan, M. I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.07814 · Cited by 403 · see citing works

Neural Network Detection of Data Sequences in Communication Systems

Farsad, N., & Goldsmith, A. J. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2868322 · Cited by 350 · see citing works

Wireless caching: technical misconceptions and business barriers

Paschos, G. S., Baştuǧ, E., Land, I., Caire, G., & Debbah, M. A. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.7537172 · Cited by 321 · see citing works

Learning the MMSE Channel Estimator

Neumann, D., Wiese, T., & Utschick, W. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2799164 · Cited by 315 · see citing works

Learning to optimize: Training deep neural networks for wireless resource management

Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2017) · 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc.2017.8227766 · Cited by 308 · see citing works

An Iterative BP-CNN Architecture for Channel Decoding

Liang, F., Shen, C., & Wu, F. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2018.2794062 · Cited by 272 · see citing works

Towards Optimal Power Control via Ensembling Deep Neural Networks

Liang, F., Shen, C., Yu, W., & Wu, F. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2957482 · Cited by 268 · see citing works

On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control

Tang, F., Mao, B., Fadlullah, Z. M., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2017.1700244 · Cited by 247 · see citing works

Channel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN

Ye, H., Li, G. Y., Juang, B.-H. F., & Sivanesan, K. (2018) · 2018 IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/glocomw.2018.8644250 · Cited by 244 · see citing works

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 234 · see citing works

Indoor Location System Based on Discriminant-Adaptive Neural Network in IEEE 802.11 Environments

Fang, S., & Lin, T.-N. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005494 · Cited by 218 · see citing works

A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio

Tumuluru, V. K., Wang, P., & Niyato, D. T. (2010) · International Conference on Communications · doi:10.1109/icc.2010.5502348 · Cited by 205 · see citing works

Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning

Cammerer, S., Gruber, T., Hoydis, J., & ten Brink, S. (2017) · Globecom · doi:10.1109/glocom.2017.8254811 · Cited by 203 · see citing works

Deep Learning-Aided SCMA

Kim, M., Kim, N.-I., Lee, W., & Cho, D. (2018) · IEEE Communications Letters · doi:10.1109/lcomm.2018.2792019 · Cited by 201 · see citing works

Model-Aided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks for Wireless System Optimization

Zappone, A., Renzo, M. D., Debbah, M. A., Tu, L., & Qian, X. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921627 · Cited by 191 · see citing works

Neural Variational Inference and Learning in Belief NetworksPreprint

Mnih, A., & Gregor, K. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.0030 · Cited by 187 · see citing works

Clustering and Resource Allocation for Dense Femtocells in a Two-Tier Cellular OFDMA Network

Abdelnasser, A., Hossain, E., & Kim, D. I. (2014) · IEEE Transactions on Wireless Communications · doi:10.1109/tw.2014.011614.131163 · Cited by 185 · see citing works

Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural NetworksPreprint

Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. A. (2017) · arXiv (Cornell University) · Cited by 184 · see citing works

Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users

Chen, M., Saad, W., Yin, C., & Debbah, M. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2017.2683482 · Cited by 176 · see citing works

Neural offset min-sum decoding

Lugosch, L., & Gross, W. J. (2017) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2017.8006751 · Cited by 172 · see citing works

Common Sense, the Turing Test, and the Quest for Real AI

Levesque, H. J. (2017) · The MIT Press eBooks · doi:10.7551/mitpress/10909.001.0001 · Cited by 158 · see citing works

A Brief Introduction to Machine Learning for Engineers

Simeone, O. (2018) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000102 · Cited by 153 · see citing works

Deepcode: Feedback Codes via Deep Learning

Kim, H., Jiang, Y., Kannan, S., Oh, S., & Viswanath, P. (2020) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2020.2986752 · Cited by 145 · see citing works

Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks

O’Shea, T. J., Roy, T., & West, N. (2019) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2019.8685573 · Cited by 137 · see citing works

3-D BLE Indoor Localization Based on Denoising Autoencoder

Xiao, C., Yang, D., Chen, Z., & Tan, G. (2017) · IEEE Access · doi:10.1109/access.2017.2720164 · Cited by 123 · see citing works

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

Zorzi, M., Zanella, A., Testolin, A., Grazia, M. D. F. D., & Zorzi, M. (2015) · IEEE Access · doi:10.1109/access.2015.2471178 · Cited by 117 · see citing works

Generative Adversarial Learning for Spectrum Sensing

Davaslıoğlu, K., & Sagduyu, Y. E. (2018) · International Conference on Communications · doi:10.1109/icc.2018.8422223 · Cited by 117 · see citing works

Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks

Balatsoukas‐Stimming, A. (2018) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc.2018.8445987 · Cited by 116 · see citing works

Machine Learning at the Edge: A Data-Driven Architecture With Applications to 5G Cellular Networks

Polese, M., Jana, R., Kounev, V., Zhang, K., Deb, S., & Zorzi, M. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.2999852 · Cited by 115 · see citing works

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A TutorialPreprint

Chen, M., Challita, U., Saad, W., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.02913 · Cited by 91 · see citing works

An Introduction to Machine Learning Communications Systems.Preprint

O’Shea, T. J., & Hoydis, J. (2017) · arXiv (Cornell University) · Cited by 83 · see citing works

A Statistical Learning Approach to Ultra-Reliable Low Latency Communication

Angjelichinoski, M., Trillingsgaard, K. F., & Popovski, P. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2907241 · Cited by 79 · see citing works

Small Sample Learning in Big Data EraPreprint

Shu, J., Xu, Z., & Meng, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.04572 · Cited by 78 · see citing works

Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G

Strodthoff, N., Göktepe, B., Schierl, T., Hellge, C., & Samek, W. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2934001 · Cited by 64 · see citing works

Redundancy Techniques for Straggler Mitigation in Distributed Optimization and LearningPreprint

Karakus, C., Sun, Y., Diggavi, S. N., & Yin, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.05397 · Cited by 51 · see citing works

Neural net-based continuous phase modulation receivers

de Veciana, G., & Zakhor, A. (1992) · IEEE Transactions on Communications · doi:10.1109/26.156644 · Cited by 40 · see citing works

Modulation classification of MIMO-OFDM signals by Independent Component Analysis and Support Vector Machines

Agirman-Tosun, H., Liu, Y., Haimovich, A. M., Simeone, O., Su, W., Dabin, J. A., & Kanterakis, E. G. (2011) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2011.6190354 · Cited by 40 · see citing works

A supervised learning approach for routing optimizations in wireless sensor networks

Wang, Y., Martonosi, M., & Peh, L.-S. (2006) · doi:10.1145/1132983.1132997 · Cited by 39 · see citing works

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 · see citing works

A Survey on Application of Machine Learning Techniques in Optical NetworksPreprint

Musumeci, F., Rottondi, C., Nag, A., Irene Macaluso, B., Zibar, D., Ruffini, M., & Tornatore, M. (2018) · arXiv (Cornell University) · Cited by 25 · see citing works

Equalisation of satellite mobile channels with neural network techniques

Bouchired, S., Roviras, D., & Castanie, F. (1999) · Space Communications · doi:10.3233/scc-1999-207 · Cited by 19 · see citing works

Channel Agnostic End-to-End Learning based Communication Systems with Conditional GANPreprint

Ye, H., Li, G. Y., Juang, B.-H. F., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00447 · Cited by 16 · see citing works

Supervised and Semi-Supervised Deep Neural Networks for CSI-Based AuthenticationPreprint

Wang, Q., Li, H., Chen, Z., Zhao, D., Ye, S., & Cai, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.09469 · Cited by 14 · see citing works

Model-Aided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks Towards Wireless Systems OptimizationPreprint

Zappone, A., Renzo, M. D., Debbah, M. A., Tu, L., & Qian, X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.01672 · Cited by 14 · see citing works

A Taxonomy for Semi-Supervised Learning Methods

Matthias, S. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262033589.003.0002 · Cited by 11 · see citing works

Estimating the Number of Receiving Nodes in 802.11 Networks via Machine Learning Techniques

Testa, D. D., Danieletto, M., Nunzio, G. M. D., & Zorzi, M. (2016) · Globecom · doi:10.1109/glocom.2016.7841821 · Cited by 11 · see citing works

Training Probabilistic Spiking Neural Networks with First- To-Spike Decoding

Bagheri, A., Simeone, O., & Rajendran, B. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462410 · Cited by 7 · see citing works

Deep Learning Detection Networks in MIMO Decode-Forward Relay ChannelsPreprint

Jin, X., & Kim, H.-N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.09571 · Cited by 5 · see citing works

A Statistical Learning Approach to Ultra-Reliable Low Latency CommunicationPreprint

Angjelichinoski, M., Trillingsgaard, K. F., & Popovski, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.05515 · Cited by 3 · see citing works

Introducing Information Measures via Inference [Lecture Notes]

Simeone, O. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2766239 · Cited by 2 · see citing works

Machine-Learning-Based Future Received Signal Strength Prediction Using Depth Images for mmWave Communications.

Okamoto, H., Nishio, T., Nakashima, K., Koda, Y., Yamamoto, K., Morikura, M., Asai, Y., & Miyatake, R. (2018) · Cited by 2 · see citing works

An Overview on Application of Machine Learning Techniques in Optical NetworksPreprint

Musumeci, F., Rottondi, C., Nag, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.07976 · Cited by 2 · see citing works

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys &amp; Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565 · see citing works

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos, L. T., Tagarakis, A. C., Dolias, G., Berruto, R., Katerıs, D., & Bochtis, D. (2021) · Sensors · doi:10.3390/s21113758 · Cited by 988 · see citing works

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Pham, Q., Fang, F., Ha, V. N., Piran, M. J., Le, M., Le, L. B., Hwang, W., & Ding, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3001277 · Cited by 887 · see citing works

Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?

Zappone, A., Renzo, M. D., & Debbah, M. A. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2924010 · Cited by 680 · see citing works

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Azari, M. M., Solanki, S., Chatzinotas, S., Kodheli, O., Sallouha, H., Colpaert, A., Montoya, J. F. M., Pollin, S., Haqiqatnejad, A., Mostaani, A., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3199901 · Cited by 699 · see citing works

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909490 · Cited by 658 · see citing works

6G Wireless Communications Networks: A Comprehensive Survey

Alsabah, M. Q., Naser, M. A., Mahmmod, B. M., Abdulhussain, S. H., Eissa, M. R., Al-Baidhani, A., Noordin, N. K., Sait, S. M., Al-Utaibi, K. A., & Hashim, F. (2021) · IEEE Access · doi:10.1109/access.2021.3124812 · Cited by 593 · see citing works

Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction

Chataut, R., & Akl, R. (2020) · Sensors · doi:10.3390/s20102753 · Cited by 567 · see citing works

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Hussain, F. A., Hassan, S. A., Hussain, R., & Hossain, E. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2964534 · Cited by 402 · see citing works

IEEE 802.11be Wi-Fi 7: New Challenges and Opportunities

Deng, C., Fang, X., Xiao, H., Wang, X., Yan, L., He, R., Long, Y., & Guo, Y. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3012715 · Cited by 309 · see citing works

Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems

Brik, B., Ksentini, A., & Bouaziz, M. (2020) · IEEE Access · doi:10.1109/access.2020.2981430 · Cited by 258 · see citing works

White Paper on Broadband Connectivity in 6G

Rajatheva, N., Atzeni, I., Björnson, E., Bourdoux, A., Buzzi, S., Doré, J., Erküçük, S., Fuentes, M., Guan, K., Hu, Y., et al. (2020) · e-space (Manchester Metropolitan University) · doi:10.48550/arxiv.2004.14247 · Cited by 324 · see citing works

Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective

Rodrigues, T. K., Suto, K., Nishiyama, H., Liu, J., & Kato, N. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2943405 · Cited by 246 · see citing works

Applications of artificial intelligence in engineering and manufacturing: a systematic review

Nti, I. K., Adekoya, A. F., Weyori, B. A., & Nyarko‐Boateng, O. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01771-6 · Cited by 243 · see citing works

Machine Learning in the Air

Gündüz, D., de Kerret, P., Sidiropoulos, N. D., Gesbert, D., Murthy, C. R., & van der Schaar, M. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933969 · Cited by 217 · see citing works

Machine Learning for Large-Scale Optimization in 6G Wireless Networks

Shi, Y., Lian, L., Shi, Y., Wang, Z., Zhou, Y., Fu, L., Bai, L., Zhang, J., & Zhang, W. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3300664 · Cited by 218 · see citing works

Automatic Modulation Classification Based on Deep Residual Networks With Multimodal Information

Qi, P., Zhou, X., Zheng, S., & Li, Z. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.3023145 · Cited by 202 · see citing works

Artificial intelligence for satellite communication: A review

Fourati, F., & Alouini, M. (2021) · Intelligent and Converged Networks · doi:10.23919/icn.2021.0015 · Cited by 197 · see citing works

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado, E., Behravesh, R., Subramanya, T., Fernández–Fernández, A., Siddiqui, M. S., Costa‐Pérez, X., & Riggio, R. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3212586 · Cited by 186 · see citing works

Adversarial Machine Learning in Wireless Communications Using RF Data: A Review

Adesina, D. S., Hsieh, C.-C. G., Sagduyu, Y. E., & Qian, L. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3205184 · Cited by 184 · see citing works

Deep Learning for Monitoring of Human Gait: A Review

Alharthi, A. S., Yunas, S. U., & Ozanyan, K. B. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2928777 · Cited by 175 · see citing works

ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

Shlezinger, N., Farsad, N., Eldar, Y. C., & Goldsmith, A. J. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.2972352 · Cited by 178 · see citing works

New Trends in Stochastic Geometry for Wireless Networks: A Tutorial and Survey

Hmamouche, Y., Benjillali, M., Saoudi, S., Yanıkömeroğlu, H., & Renzo, M. D. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3061778 · Cited by 169 · see citing works

Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities

Zhang, S., & Zhu, D. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107556 · Cited by 170 · see citing works

Intelligent O-RAN for Beyond 5G and 6G Wireless Networks

Niknam, S., Roy, A., Dhillon, H. S., Singh, S., Banerji, R., Reed, J. H., Saxena, N., & Yoon, S. (2022) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps56602.2022.10008676 · Cited by 157 · see citing works

A Comprehensive Survey of the Tactile Internet: State-of-the-Art and Research Directions

Promwongsa, N., Ebrahimzadeh, A., Naboulsi, D., Kianpisheh, S., Belqasmi, F., Glitho, R., Crespi, N., & Alfandi, O. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3025995 · Cited by 151 · see citing works

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

Salh, A., Audah‏, L., Shah, N. S. M., Alhammadi, A., Abdullah, Q., Kim, Y. H., Al-Gailani, S. A., Hamzah‏, S. A., Esmail, B. A. F., & Almohammedi, A. A. (2021) · IEEE Access · doi:10.1109/access.2021.3069707 · Cited by 152 · see citing works

DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection

Shlezinger, N., Fu, R., & Eldar, Y. C. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3032663 · Cited by 137 · see citing works

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

Ahmad, I., Shahabuddin, S., Malik, H. H., Harjula, E., Leppänen, T., Lovén, L., Anttonen, A., Sodhro, A. H., Alam, M. M., Juntti, M. J., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3041765 · Cited by 135 · see citing works

The Nexus Between Big Data and Decision-Making: A Study of Big Data Techniques and Technologies

Naqvi, R., Soomro, T. R., Alzoubi, H. M., Ghazal, T. M., & Alshurideh, M. T. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-76346-6_73 · Cited by 131 · see citing works

Semantic-Effectiveness Filtering and Control for Post-5G Wireless Connectivity

Popovski, P., Simeone, O., Boccardi, F., Gündüz, D., & Sahin, O. (2020) · Journal of the Indian Institute of Science · doi:10.1007/s41745-020-00165-6 · Cited by 122 · see citing works

Parametrization of physics-based battery models from input–output data: A review of methodology and current research

Andersson, M., Streb, M., Ko, J. Y., Klass, V. L., Klett, M., Ekström, H., Johansson, M., & Lindbergh, G. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230859 · Cited by 126 · see citing works

Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications

Wang, J., Wang, Y., Li, G., & Qi, Z. (2024) · Agronomy · doi:10.3390/agronomy14091975 · Cited by 126 · see citing works

A vision on the artificial intelligence for 6G communication

Ahammed, T. B., Patgiri, R., & Nayak, S. (2022) · ICT Express · doi:10.1016/j.icte.2022.05.005 · Cited by 124 · see citing works

A Generative Approach to Materials Discovery, Design, and Optimization

Menon, D., & Ranganathan, R. (2022) · ACS Omega · doi:10.1021/acsomega.2c03264 · Cited by 127 · see citing works

DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer

Zheng, S., Chen, S., & Yang, X. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.3018736 · Cited by 117 · see citing works

Learning to Demodulate From Few Pilots via Offline and Online Meta-Learning

Park, S., Jang, H., Simeone, O., & Kang, J. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3043879 · Cited by 116 · see citing works

Physics-Based Deep Learning for Fiber-Optic Communication Systems

Häger, C., & Pfister, H. D. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3036950 · Cited by 113 · see citing works

Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city

Easwaramoorthy, S. V., Shin, C., & Cho, Y. (2020) · Building Research & Information · doi:10.1080/09613218.2020.1809983 · Cited by 119 · see citing works

Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks

Sagduyu, Y. E., Shi, Y., & Erpek, T. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2950398 · Cited by 112 · see citing works

Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products

Kadam, V., Kumar, S., Bongale, A. M., Wazarkar, S., Kamat, P. V., & Patil, S. (2021) · Applied System Innovation · doi:10.3390/asi4020034 · Cited by 106 · see citing works

Industry 5.0 and its technologies: A systematic literature review upon the human place into IoT- and CPS-based industrial systems

Valette, E., El-Haouzi, H. B., & Demesure, G. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109426 · Cited by 106 · see citing works

Toward 6G: An Overview of the Next Generation of Intelligent Network Connectivity

Tera, S. P., Chinthaginjala, R., Pau, G., & Kim, T. H. (2024) · IEEE Access · doi:10.1109/access.2024.3523327 · Cited by 105 · see citing works

6G: The Intelligent Network of Everything

Pennanen, H., Hänninen, T., Tervo, O., Tölli, A., & Latva‐aho, M. (2024) · IEEE Access · doi:10.1109/access.2024.3521579 · Cited by 116 · see citing works

Comparing the performance of machine learning algorithms using estimated accuracy

Gupta, S., Saluja, K., Goyal, A., Vajpayee, A., & Tiwari, V. (2022) · Measurement Sensors · doi:10.1016/j.measen.2022.100432 · Cited by 100 · see citing works

Scoring the Terabit/s Goal:Broadband Connectivity in 6GPreprint

Rajatheva, N., Atzeni, I., Bicaïs, S., Björnson, E., Bourdoux, A., Buzzi, S., D’Andrea, C., Doré, J., Erküçük, S., Fuentes, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.07220 · Cited by 99 · see citing works

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

Ali, M., Alqahtani, A., Jones, M. W., & Xie, X. (2019) · IEEE Access · doi:10.1109/access.2019.2958551 · Cited by 92 · see citing works

Beam Illumination Pattern Design in Satellite Networks: Learning and Optimization for Efficient Beam Hopping

Lei, L., Lagunas, E., Yuan, Y., Kibria, M. G., Chatzinotas, S., & Ottersten, B. (2020) · IEEE Access · doi:10.1109/access.2020.3011746 · Cited by 92 · see citing works

Generative AI Empowered Network Digital Twins: Architecture, Technologies, and Applications

Li, T., Long, Q., Chai, H., Zhang, S., Jiang, F., Liu, H., Huang, W., Jin, D., & Li, Y. (2025) · ACM Computing Surveys · doi:10.1145/3711682 · Cited by 95 · see citing works

A Statistical Learning Approach to Ultra-Reliable Low Latency Communication

Angjelichinoski, M., Trillingsgaard, K. F., & Popovski, P. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2907241 · Cited by 79 · see citing works

A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management

Schroeder, M., & Lodemann, S. (2021) · Logistics · doi:10.3390/logistics5030062 · Cited by 86 · see citing works

Safe-NORA: Safe Reinforcement Learning-based Mobile Network Resource Allocation for Diverse User Demands

Huang, W., Li, T., Cao, Y., Lyu, Z., Liang, Y., Yu, L., Jin, D., Zhang, J., & Li, Y. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615043 · Cited by 81 · see citing works

Edge Computing and Its Convergence With Blockchain in 5G and Beyond: Security, Challenges, and Opportunities

Bhat, S. A., Sofi, I. B., & Chi, C. (2020) · IEEE Access · doi:10.1109/access.2020.3037108 · Cited by 78 · see citing works

From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems

Simeone, O., Park, S., & Kang, J. (2020) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/6gsummit49458.2020.9083856 · Cited by 66 · see citing works

Enhancing Communication Networks in the New Era with Artificial Intelligence: Techniques, Applications, and Future Directions

El-Hajj, M. I. (2025) · Network · doi:10.3390/network5010001 · Cited by 87 · see citing works

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Dumakor-Dupey, N. K., Arya, S., & Jha, A. (2021) · Minerals · doi:10.3390/min11060601 · Cited by 64 · see citing works

Distributed Beamforming Techniques for Cell-Free Wireless Networks Using Deep Reinforcement Learning

Fredj, F., Al-Eryani, Y. F., Maghsudi, S., Akrout, M., & Hossain, E. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3165810 · Cited by 63 · see citing works

Defensive Distillation-Based Adversarial Attack Mitigation Method for Channel Estimation Using Deep Learning Models in Next-Generation Wireless Networks

Çatak, F. Ö., Kuzlu, M., Çatak, E., Cali, Ü., & Güler, Ö. (2022) · IEEE Access · doi:10.1109/access.2022.3206385 · Cited by 60 · see citing works

PSDF: Privacy-aware IoV Service Deployment with Federated Learning in Cloud-Edge Computing

Xu, X., Liu, W., Zhang, Y., Zhang, X., Dou, W., Qi, L., & Bhuiyan, M. Z. A. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501810 · Cited by 61 · see citing works

AI-ERA: Artificial Intelligence-Empowered Resource Allocation for LoRa-Enabled IoT Applications

Farhad, A., & Pyun, J.-Y. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3248074 · Cited by 58 · see citing works

Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification

Soltani, S., Sagduyu, Y. E., Hasan, R., Davaslıoğlu, K., Deng, H., & Erpek, T. (2019) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom47813.2019.9021098 · Cited by 63 · see citing works

ViterbiNet: Symbol Detection Using a Deep Learning Based Viterbi Algorithm

Shlezinger, N., Eldar, Y. C., Farsad, N., & Goldsmith, A. J. (2019) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc.2019.8815457 · Cited by 54 · see citing works

Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels

Park, S., Simeone, O., & Kang, J. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053252 · Cited by 56 · see citing works

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

Farhad, A., & Pyun, J.-Y. (2023) · Sensors · doi:10.3390/s23156851 · Cited by 55 · see citing works

Online Meta-Learning for Hybrid Model-Based Deep Receivers

Raviv, T., Park, S., Simeone, O., Eldar, Y. C., & Shlezinger, N. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3241841 · Cited by 55 · see citing works

A Hybrid Architecture for Federated and Centralized Learning

Elbir, A. M., Çöleri, S., Papazafeiropoulos, A., Kourtessis, P., & Chatzinotas, S. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3181032 · Cited by 53 · see citing works

Model-Free Adaptive Control for Nonlinear Systems Under Dynamic Sparse Attacks and Measurement Disturbances

Zhou, Q. F., Ren, Q., Ma, H., Chen, G., & Li, H. (2024) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2024.3434607 · Cited by 53 · see citing works

Data-Driven Symbol Detection Via Model-Based Machine Learning

Farsad, N., Shlezinger, N., Goldsmith, A. J., & Eldar, Y. C. (2020) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp49050.2021.9513859 · Cited by 53 · see citing works

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

Porawagamage, G. D., Dharmapala, K., Chaves, J. S., Villegas, D., & Rajapakse, A. D. (2024) · Frontiers in Smart Grids · doi:10.3389/frsgr.2024.1371153 · Cited by 52 · see citing works

RF Fingerprinting and Deep Learning Assisted UE Positioning in 5G

Butt, M. M., Rao, A., & Yoon, D. (2020) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2020-spring48590.2020.9128640 · Cited by 51 · see citing works

Clinical decision support systems for chronic diseases: A Systematic literature review

Souza-Pereira, L., Pombo, N. G. C. C., Ouhbi, S., Felizardo, V., & García, N. M. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105565 · Cited by 50 · see citing works

Terahertz Meets AI: The State of the Art

Farhad, A., & Pyun, J.-Y. (2023) · Sensors · doi:10.3390/s23115034 · Cited by 52 · see citing works

A vision towards integrated 6G communication networks: Promising technologies, architecture, and use-cases

Jain, P., Gupta, A., & Kumar, N. (2022) · Physical Communication · doi:10.1016/j.phycom.2022.101917 · Cited by 49 · see citing works

DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system

Saboor, A., Li, J., Haq, A. U., Shehzad, U., Khan, S., Alotaibi, R. M., & Alajlan, S. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-56983-6 · Cited by 48 · see citing works

End-to-End Learning for Integrated Sensing and Communication

Mateos-Ramos, J. M., Song, J., Wu, Y., Häger, C., Keskin, M. F., Yajnanarayana, V., & Wymeersch, H. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838308 · Cited by 47 · see citing works

Toward Smart Traffic Management With 3D Placement Optimization in UAV-Assisted NOMA IIoT Networks

Adam, A. B. M., Muthanna, M. S. A., Muthanna, A., Nguyen, T. N., & El‐Latif, A. A. A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3182651 · Cited by 47 · see citing works

Artificial Intelligence in Open-Radio Access Network

Masur, P. H., Reed, J. H., & Tripathi, N. D. (2022) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2022.3186966 · Cited by 46 · see citing works

Entropy to Mitigate Non-IID Data Problem on Federated Learning for the Edge Intelligence Environment

Orlandi, F. C., dos Anjos, J. C. S., Leithardt, V. R. Q., Paz, J. F. D., & Geyer, C. F. R. (2023) · IEEE Access · doi:10.1109/access.2023.3298704 · Cited by 46 · see citing works

Machine learning for predicting cardiac events: what does the future hold?

Patel, B. D., & Sengupta, P. P. (2020) · Expert Review of Cardiovascular Therapy · doi:10.1080/14779072.2020.1732208 · Cited by 47 · see citing works

Towards 6G: Evolution of Key Performance Indicators and Technology Trends

Mourad, A., Yang, R. J., Lehne, P. H., & de la Oliva, A. (2020) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/6gsummit49458.2020.9083759 · Cited by 46 · see citing works

A Deep Learning-Based Intelligent Receiver for Improving the Reliability of the MIMO Wireless Communication System

Wang, B., Xu, K., Zheng, S., Zhou, H., & Liu, Y. (2022) · IEEE Transactions on Reliability · doi:10.1109/tr.2022.3148114 · Cited by 46 · see citing works

Expansive networks: Exploiting spectrum sharing for capacity boost and 6G vision

Gür, G. (2020) · Journal of Communications and Networks · doi:10.23919/jcn.2020.000037 · Cited by 45 · see citing works

Introduction to Artificial Intelligence

Ghosh, M., & Thirugnanam, A. (2021) · Studies in big data · doi:10.1007/978-981-16-0415-7_2 · Cited by 45 · see citing works

A Study on Multi-Antenna and Pertinent Technologies with AI/ML Approaches for B5G/6G Networks

Siddiqui, M. U. A., Qamar, F., Kazmi, S. H. A., Hassan, R. B., Arfeen, A., & Nguyễn, Q. N. (2022) · Electronics · doi:10.3390/electronics12010189 · Cited by 45 · see citing works

An Introduction to Bilevel Optimization: Foundations and applications in signal processing and machine learning

Zhang, Y., Khanduri, P., Tsaknakis, I., Yao, Y., Hong, M., & Liu, S. (2024) · IEEE Signal Processing Magazine · doi:10.1109/msp.2024.3358284 · Cited by 45 · see citing works

Prediction of Drag Force on Vehicles in a Platoon Configuration Using Machine Learning

Jaffar, F., Farid, T., Sajid, M. F., Ayaz, Y., & Khan, M. J. (2020) · IEEE Access · doi:10.1109/access.2020.3035318 · Cited by 45 · see citing works

A Baseline Roadmap for Advanced Wireless Research Beyond 5G

Mourad, A., Yang, R. J., Lehne, P. H., & de la Oliva, A. (2020) · Electronics · doi:10.3390/electronics9020351 · Cited by 44 · see citing works

Machine learning-based physical layer security: techniques, open challenges, and applications

Kamboj, A. K., Jindal, P., & Verma, P. (2021) · Wireless Networks · doi:10.1007/s11276-021-02781-1 · Cited by 45 · see citing works

Machine Learning Tips and Tricks for Power Line Communications

Tonello, A. M., Letizia, N. A., Righini, D., & Marcuzzi, F. (2019) · IEEE Access · doi:10.1109/access.2019.2923321 · Cited by 41 · see citing works

Machine learning enhanced next-generation optical access networks—challenges and emerging solutions [Invited Tutorial]

Wong, E., Mondal, S., & Ruan, L.-H. (2022) · Journal of Optical Communications and Networking · doi:10.1364/jocn.470902 · Cited by 38 · see citing works

Processing, Characteristics and Composition of Umqombothi (a South African Traditional Beer)

Hlangwani, E., Adebiyi, J. A., Doorsamy, W., & Adebo, O. A. (2020) · Processes · doi:10.3390/pr8111451 · Cited by 41 · see citing works

ML-Assisted UE Positioning: Performance Analysis and 5G Architecture Enhancements

Butt, M. M., Pantelidou, A., & Kovács, I. Z. (2021) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2021.3110134 · Cited by 40 · see citing works

Random Forest Regression for Predicting Metamaterial Antenna Parameters

Kurniawati, N., Putri, D. N. N., & Ningsih, Y. K. (2020) · doi:10.1109/iciee49813.2020.9276899 · Cited by 40 · see citing works

Context-Based Semantic Communication via Dynamic Programming

Zhang, Y., Zhao, H., Wei, J., Zhang, J., Flanagan, M. F., & Xiong, J. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3173056 · Cited by 40 · see citing works

A Waveform Parameter Assignment Framework for 6G With the Role of Machine Learning

Yazar, A., & Arslan, H. (2020) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2020.2992502 · Cited by 38 · see citing works

Design of deep learning model for radio resource allocation in 5G for massive iot device

Saravanan, V., Sreelatha, P. N. K., Atyam, N. R., Madiajagan, M., Saravanan, D., Kumar, T. A., & Sultana, H. P. (2023) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2023.103054 · Cited by 40 · see citing works

IRS-enabled NOMA communication systems: A network architecture primer with future trends and challenges

Sadia, H., Hassan, A. K., Abbas, Z. H., Abbas, G., Waqas, M. M., & Han, Z. (2023) · Digital Communications and Networks · doi:10.1016/j.dcan.2023.09.002 · Cited by 38 · see citing works

Experimental Investigation of Deep Learning for Digital Signal Processing in Short Reach Optical Fiber Communications

Karanov, B., Chagnon, M., Aref, V., Ferreira, F. M., Lavery, D., Bayvel, P., & Schmalen, L. (2020) · SiPS ... design and implementation - IEEE Workshop on Signal Processing Systems · doi:10.1109/sips50750.2020.9195215 · Cited by 37 · see citing works

Introduction of machine learning with applications to communication system

Kumar, J. R. R., Chobe, S., Nikam, S., Zanwar, S. R., Borawake, M. P., & Hirolikar, D. S. (2023) · Journal of Autonomous Intelligence · doi:10.32629/jai.v6i3.1244 · Cited by 1 · see citing works

DataDriven Approaches for Communication Systems: Algorithms, Benchmark Collections, and Architectural Models

Dr.A.Swetha, D. A. S., AKSHAYA, T., PRASANNA, T., & ABHIRAM, P. (2026) · International Journal of Novel Research and Development · doi:10.56975/ijnrd.v11i4.313310

A review of applications of supervised learning to future networks

Bilson, S., & Thompson, A. (2022) · doi:10.47120/npl.ms40 · Cited by 3 · see citing works

The Convergence of Machine Learning and CommunicationsPreprint

Samek, W., Stańczak, S., & Wiegand, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.08299 · Cited by 25 · see citing works

Machine Learning for Wireless Communication

Thanki, R. M., Borisagar, K. R., & Diwan, A. (2025) · Synthesis lectures on communications · doi:10.1007/978-3-031-94117-7