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

Maximum Likelihood from Incomplete Data Via the EM 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

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268

Book Review: Pattern Recognition and Machine Learning

Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066

Learning Internal Representations by Error Propagation

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

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

Untitled work

(1999) · Cited by 12,854

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

Untitled work

(2012) · Cited by 9,062

Reinforcement Learning: An Introduction

Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,825

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 4,186

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 & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565

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 1,048

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

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 703

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 767

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

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 622

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 590

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 411

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

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

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

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 251

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 255

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 222

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 239

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 203

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

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 203

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 205

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

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 188

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

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 176

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

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

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

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 142

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 140

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

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

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

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 153

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 131

A Generative Approach to Materials Discovery, Design, and Optimization

Menon, D., & Ranganathan, R. (2022) · ACS Omega · doi:10.1021/acsomega.2c03264 · Cited by 135

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 119

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

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 121

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

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

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

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

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 113

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 117

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

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

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 96

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

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 106

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 82

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

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

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

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 67

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 95

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

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

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 69

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 63

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

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 65

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 55

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

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

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

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 57

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

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 58

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 71

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

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 54

Terahertz Meets AI: The State of the Art

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

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

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

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

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

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

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 54

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 48

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

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

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 46

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

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

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 79

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 47

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

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

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 44

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

Wong, E., Mondal, S., & Ruan, L. (2022) · Journal of Optical Communications and Networking · doi:10.1364/jocn.470902 · Cited by 47

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

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

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

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 41

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 40

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

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

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

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

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

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

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