| Artificial neural networks-based machine learning for wireless networks: A tutorial M Chen, U Challita, W Saad, C Yin, M Debbah IEEE Communications Surveys & Tutorials 21 (4), 3039-3071, 2019 | 937 | 2019 |
| Interference management for cellular-connected UAVs: A deep reinforcement learning approach U Challita, W Saad, C Bettstetter IEEE Transactions on Wireless Communications 18 (4), 2125-2140, 2019 | 333 | 2019 |
| Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks M Chen, U Challita, W Saad, C Yin, M Debbah arXiv preprint arXiv:1710.02913 9, 2017 | 300 | 2017 |
| Proactive resource management for LTE in unlicensed spectrum: A deep learning perspective U Challita, L Dong, W Saad IEEE transactions on wireless communications 17 (7), 4674-4689, 2018 | 238 | 2018 |
| Machine learning for wireless connectivity and security of cellular-connected UAVs U Challita, A Ferdowsi, M Chen, W Saad IEEE Wireless Communications 26 (1), 28-35, 2019 | 202 | 2019 |
| Deep learning for reliable mobile edge analytics in intelligent transportation systems: An overview A Ferdowsi, U Challita, W Saad ieee vehicular technology magazine 14 (1), 62-70, 2019 | 198 | 2019 |
| Deep reinforcement learning for interference-aware path planning of cellular-connected UAVs U Challita, W Saad, C Bettstetter 2018 IEEE international conference on communications (ICC), 1-7, 2018 | 153 | 2018 |
| Robust deep reinforcement learning for security and safety in autonomous vehicle systems A Ferdowsi, U Challita, W Saad, NB Mandayam 2018 21st International Conference on Intelligent Transportation Systems …, 2018 | 135 | 2018 |
| Network formation in the sky: Unmanned aerial vehicles for multi-hop wireless backhauling U Challita, W Saad GLOBECOM 2017-2017 IEEE Global Communications Conference, 1-6, 2017 | 121 | 2017 |
| Cellular-connected UAVs over 5G: Deep reinforcement learning for interference management U Challita, W Saad, C Bettstetter arXiv preprint arXiv:1801.05500, 2018 | 85 | 2018 |
| When machine learning meets wireless cellular networks: Deployment, challenges, and applications U Challita, H Ryden, H Tullberg IEEE Communications Magazine 58 (6), 12-18, 2020 | 79 | 2020 |
| Noise learning-based denoising autoencoder WH Lee, M Ozger, U Challita, KW Sung IEEE Communications Letters 25 (9), 2983-2987, 2021 | 54 | 2021 |
| Deep learning for proactive resource allocation in LTE-U networks U Challita, L Dong, W Saad European wireless technology conference, 2017 | 44 | 2017 |
| QoE optimization for live video streaming in UAV-to-UAV communications via deep reinforcement learning LA binti Burhanuddin, X Liu, Y Deng, U Challita, A Zahemszky IEEE Transactions on Vehicular Technology 71 (5), 5358-5370, 2022 | 39 | 2022 |
| On LTE-WiFi coexistence and inter-operator spectrum sharing in unlicensed bands: Altruism, cooperation and fairness C Hasan, MK Marina, U Challita Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc …, 2016 | 33 | 2016 |
| Artificial intelligence for wireless connectivity and security of cellular-connected UAVs U Challita, A Ferdowsi, M Chen, W Saad arXiv preprint arXiv:1804.05348, 2018 | 26 | 2018 |
| Deep reinforcement learning for dynamic spectrum sharing of LTE and NR U Challita, D Sandberg ICC 2021-IEEE International Conference on Communications, 1-6, 2021 | 20 | 2021 |
| A chance constrained approach for LTE cellular network planning under uncertainty U Challita, Z Dawy, G Turkiyyah, J Naoum-Sawaya Computer Communications 73, 34-45, 2016 | 14 | 2016 |
| On LTE cellular network planning under demand uncertainty U Challita, L Al-Kanj, Z Dawy 2014 IEEE Wireless Communications and Networking Conference (WCNC), 2079-2084, 2014 | 14 | 2014 |
| Holistic Small Cell Traffic Balancing across Licensed and Unlicensed Bands U Challita, MK Marina Proceedings of the 19th ACM International Conference on Modeling, Analysis …, 2016 | 13 | 2016 |
Walid SaadProfessor, Electrical and Computer Engineering, Virginia TechDirección de correo verificada de vt.edu
