AI and Machine Learning for Wireless Networks

This page provides key resources on the research results developed by our group with respect to machine learning and artificial intelligence (AI). The papers cover both tutorial papers and advanced applications of ML/AI in wireless networks. Example of the tools used include deep learning/deep reinforcement learning, federated learning, echo state networks, liquid state machines, long short-term memory (LSTM) cells, and multi-armed bandits, among others

Tutorials on ML/AI for Wireless

Federated Learning and Wireless Networks

    1. N. Yang, S. Wang, M. Chen, C. G. Brinton, C. Yin, W. Saad, and S. Cui, "Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Mobile and Wireless Networks Symposium, Rio de Janeiro, Brazil, December 2022.

    2. Y. Oh, Y.-S. Jeon, M. Chen, and W. Saad, "Vector Quantized Compressed Sensing for Communication-Efficient Federated Learning", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Workshop on Wireless Communications for Distributed Intelligence, Rio de Janeiro, Brazil, December 2022.

    3. M. Chehimi and W. Saad, "Quantum Federated Learning with Quantum Data", in Proc. of International Conference on Acoustics, Speech, & Signal Processing (ICASSP), Singapore, May 2022.

    4. M. Kim, W. Saad, M. Mozaffari, and M. Debbah, "On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks", in Proc. of the IEEE International Conference on Communications (ICC), Green Communication Systems and Networks Symposium, Seoul, South Korea, May 2022. Best Paper Award.

    5. Q. Zhang, W. Saad, and M. Bennis, "Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning", IEEE Transactions on Wireless Communications, vol. 21, no. 3, pp. 1836 - 1850, March 2022.

    6. L. U. Khan, W. Saad, Z. Han, and C. S. Hong, "Dispersed Federated Learning: Vision, Taxonomy, and Future Directions", IEEE Wireless Communications Magazine, vol. 28, no. 5, pp. 192 - 198, October 2021.

    7. M. Chen, D. Gunduz, K. Huang, W. Saad, M. Bennis, A. V. Feljan, and H. V. Poor, "Distributed Learning in Wireless Networks: Recent Progress and Future Challenges", IEEE Journal on Selected Areas in Communications (JSAC), Special Issue on Distributed Learning over Wireless Edge Networks, to appear, 2021.

    8. H. Tong, Z. Yang, S. Wang, Y. Hu, O. Semiari, W. Saad, and C. Yin, "Federated Learning for Audio Semantic Communication", Frontiers in Communications and Networks, Data Science for Communications, September 2021.

    9. M. Chen, H. V. Poor, W. Saad, and S. Cui, "Convergence Time Optimization for Federated Learning over Wireless Networks", IEEE Transactions on Wireless Communications, April, 2021.

    10. M. N. H. Nguyen, S. R. Pandey, K. Thar, N. H. Tran, M. Chen, W. Saad, and C. S. Hong, "Distributed and Democratized Learning: Philosophy and Research Challenges", IEEE Computational Intelligence Magazine, vol. 16, no. 1, pp. 49 - 62, February 2021.

    11. A. El-Dosouky, W. Saad, and N. B. Mandayam, "Resilient Critical Infrastructure: Bayesian Network Analysis and Contract-Based Optimization", Reliability Engineering and System Safety, vol. 205, January 2021.

    12. M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, "A Joint Learning and Communications Framework for Federated Learning over Wireless Networks", IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 269 - 283, January 2021.

    13. M. Chen, H. V. Poor, W. Saad, and S. Cui, "Wireless Communications for Collaborative Federated Learning", IEEE Communications Magazine, Special Issue on Communication Technologies for Efficient Edge Learning, vol. 58, no. 2, pp. 48 - 54, December 2020.

    14. Y. Xiao, G. Shi, Y. Li, W. Saad, and H. V. Poor, "Towards Self-learning Edge Intelligence in 6G", IEEE Communications Magazine, Special Issue on Communication Technologies for Efficient Edge Learning, vol. 58, no. 12, pp. 34 - 40, December 2020.

    15. S. Wang, M. Chen, W. Saad, C. Yin, S. Cui, and H. V. Poor, "Reinforcement Learning for Minimizing Age of Information under Realistic Physical Dynamics", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Green Communications Systems and Networks Symposium, Taipei, Taiwan, December 2020.

    16. L. U. Khan, S. R. Pandey, N. H. Tran, W. Saad, Z. Han, M. N. H. Nguyen, and C. S. Hong, "Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism", IEEE Communications Magazine, Data Science/Artificial Intelligence Series, vol. 58, no. 10, pp. 88 - 93, October 2020.

    17. Z. Yang, M. Chen, W. Saad, C. S. Hong, M. Shikh-Bahaei, H. V. Poor and S. Cui, "Delay Minimization for Federated Learning Over Wireless Communication Networks", in Proc. of International Conference on Machine Learning, Workshop on Federated Learning for User Privacy and Data Confidentiality (ICML-FL), Vienna, Austria, July 2020.

    18. M. Chen, H. V. Poor, W. Saad, and S. Cui, "Convergence Time Minimization of Federated Learning over Wireless Networks", in Proc. of the IEEE International Conference on Communications (ICC), Cognitive Radio and AI-Enabled Networks Symposium, Dublin, Ireland, June 2020. Best Paper Award.

    19. S. Wang, M. Chen, W. Saad, and C. Yin, "Federated Learning for Energy-Efficient Task Computing in Wireless Networks", in Proc. of the IEEE International Conference on Communications (ICC), Wireless Communications Symposium, Dublin, Ireland, June 2020.

    20. A. Ferdowsi and W. Saad, "Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Private Datasets", arXiv:2002.00306.

    21. T. Zeng, O. Semiari, M. Mozaffari, M. Chen, W. Saad, and M. Bennis, "Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms", in Proc. of the IEEE International Conference on Communications (ICC), Next-Generation Networking and Internet Symposium, Dublin, Ireland, June 2020.

    22. M. Chen, H. V. Poor, W. Saad, and S. Cui, "Performance Optimization of Federated Learning over Mobile Wireless Networks", invited, in Proc. of 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, May 2020.

    23. M. Chen, O. Semiari, W. Saad, X. Liu, and C. Yin, "Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks", IEEE Transactions on Wireless Communications, vol. 19, no. 1, pp. 177-191, Jan. 2020.

    24. M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, "Performance Optimization of Federated Learning over Wireless Networks", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Cognitive Radio and AI-Enabled Networks Symposium, Waikoloa, HI, USA, December 2019.

    25. M. Chen, O. Semiari, W. Saad, X. Liu, and C. Yin, "Federated Deep Learning for Immersive Virtual Reality over Wireless Networks', in Proc. of the IEEE Global Communications Conference (GLOBECOM), Cognitive Radio and AI-Enabled Networks Symposium, Waikoloa, HI, USA, December 2019.

    26. S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, "Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications", IEEE Transactions on Communications, vol. 68, no. 2, pp. 1146-1159, Feb. 2020.

    27. S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, "Federated Learning for Ultra-Reliable Low-Latency V2V Communications", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Selected Areas in Communications Symposium - Tactile Internet Track, Abu Dhabi, UAE, December 2018.

AI for Wireless Resource Management and Optimization

    1. Y. Wang, Y. Hu, Z. Yang, W. Saad, K.-K. Wong, and V. Friderikos,"Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks", IEEE Transactions on Mobile Computing, to appear, 2022.

    2. M. N. H. Nguyen, S. R. Pandey, T. N. Dang, E.-N. Huh, C. S. Hong, M. H. Tran, and W. Saad, "Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems", IEEE Transactions on Neural Networks and Learning Systems, to appear, 2022.

    3. S. Wang, M Chen, Z. Yang, C. Yin, W. Saad, S. Cui, and H. V. Poor, "Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems", IEEE Journal on Selected Topics in Signal Processing (JSTSP), Special Issue on Distributed Machine Learning for Wireless Communication, vol. 16, no. 3, pp. 501 - 515, April 2022.

    4. S. Wang, M. Chen, C. Yin, W. Saad, C. S. Hong, S. Cui, and H. V. Poor, "Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks", IEEE Internet of Things Journal, May, 2021.

    5. S. Munir, N. H. Tran, W. Saad, and C. S. Hong, "Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems", IEEE Transactions on Network and Service Management, September, 2021

    6. Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. Shikh-Bahaei, "Energy Efficient Federated Learning Over Wireless Communication Networks", IEEE Transactions on Wireless Communications, March, 2021.

    7. M. Chen, W. Saad, and C. Yin, "Liquid State Machine Learning for Resource and Cache Management in LTE-U Unmanned Aerial Vehicle (UAV) Networks", IEEE Transactions on Wireless Communications, vol. 18, no. 3, pp. 1504-1517, March 2019.

    8. Q. Zhang, W. Saad, and M. Bennis, "Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Next Generation Networking and Internet Symposium, Taipei, Taiwan, December 2020.

    9. Q. Zhang, W. Saad, and M. Bennis, "Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Next Generation Networking and Internet Symposium, Taipei, Taiwan, December 2020.

    10. L. U. Khan, S. R. Pandey, N. H. Tran, W. Saad, Z. Han, M. N. H. Nguyen, and C. S. Hong, "Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism", IEEE Communications Magazine, Data Science/Artificial Intelligence Series, vol. 58, no. 10, pp. 88 - 93, October 2020.

    11. S. Ali, A. Ferdowsi, W. Saad, N. Rajatheva, and J. Haapola, "Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications", IEEE Transactions on Communications, vol. 68, no. 8, pp. 5072 - 5086, August 2020.

    12. X. Zhang, Y. Xiao, Q. Li, and W. Saad, "Deep Reinforcement Learning for Fog Computing-based Vehicular System with Multi-operator Support", in Proc. of the IEEE International Conference on Communications (ICC), SAC Cloud & Fog Computing, Networking, and Storage Track, Dublin, Ireland, June 2020.

    13. M. Naderi Soorki, W. Saad, and M. Bennis, "Ultra-Reliable Millimeter-Wave Communications using an Artificial Intelligence-Powered Reflector", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Wireless Communications Symposium, Waikoloa, HI, USA, December 2019.

    14. A. T. Z. Kasgari, W. Saad, and M. Debbah, "Human-in-the-Loop Wireless Communications: Machine Learning and Brain-Aware Resource Management", IEEE Transactions on Communications, vol. 67, no. 11, pp. 7727-7743, November. 2019.

    15. U. Challita, L. Dong, and W. Saad, "Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective", IEEE Transactions on Wireless Communications, vol. 17, no. . 7, pp. 4674 - 4689, July 2018.

    16. Q. Zhang, M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, "Machine Learning for Predictive On-Demand Deployment of UAVs for Wireless Communications", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Green Communications Systems and Networks Symposium, Abu Dhabi, UAE, December 2018.

    17. S. Ali, W. Saad, and N. Rajatheva, "A Directed Information Learning Framework for Event-Driven M2M Traffic Prediction", IEEE Communications Letters, vol. 22, no. 11, pp. 2378 - 2381, November 2018.

    18. U. Challita, L. Dong, and W. Saad, "Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective", IEEE Transactions on Wireless Communications, vol. 17, no. 7, pp. 4674 - 4689, July 2018.

    19. A. Taleb Zadeh Kasgari, B. Maham, H. Kebriaei, and W. Saad, "Dynamic Learning for Distributed Power Control in Underlaid Cognitive Radio Networks", in Proc. 14th International Wireless Communications & Mobile Computing Conference (IWCMC), Limasol, Cyprus, June 2018.

    20. K. Hamidouche, A. Taleb Zadeh Kasgari, W. Saad, M. Bennis, and M. Debbah, "Collaborative Artificial Intelligence (AI) for User-Cell Association in Ultra-Dense Cellular Systems", in Proc. of the IEEE International Conference on Communications (ICC), Workshop on Promises and Challenges of Machine Learning in Communication Networks, Kansas City, MO, USA, May 2018.

    21. M. Chen, W. Saad, and C. Yin, "Echo State Learning for Wireless Virtual Reality Resource Allocation in UAV-enabled LTE-U Networks", in Proc. of the IEEE International Conference on Communications (ICC), Cognitive Radio Networking Symposium, Kansas City, MO, USA, May 2018.

    22. K. Hamidouche, A. Taleb Zadeh Kasgari, W. Saad, M. Bennis, and M. Debbah, "Collaborative Artificial Intelligence (AI) for User-Cell Association in Ultra-Dense Cellular Systems", in Proc. of the IEEE International Conference on Communications (ICC), Workshop on Promises and Challenges of Machine Learning in Communication Networks, Kansas City, MO, USA, May 2018.

    23. A. Taleb Zadeh Kasgari, W. Saad, and M. Debbah, "Brain-Aware Wireless Networks: Learning and Resource Management", in Proc. of the 51st Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, November 2017.

    24. M. Chen, W. Saad, C. Yin, and M. Debbah, "Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users", IEEE Transactions on Wireless Communications, vol. 16, no. 6, pp. 3520 - 3535, June 2017.

    25. U. Challita, L. Dong, and W. Saad, "Deep Learning for Proactive Resource Allocation in LTE-U Networks", in Proc. of European Wireless, Dresden, Germany, May 2017.

    26. A. Hajijamali Arani, A. Mehbodniya, M. J. Omidi, F. Adachi, W. Saad, and I. Guvenc, "Distributed Learning for Energy-Efficient Resource Management in Self-Organizing Heterogeneous Networks", IEEE Transactions on Vehicular Technology, vol. 66, no. 10, pp. 9287 - 9303, October 2017.

    27. M. Chen, W. Saad, and C. Lin, "Echo State Networks for Self-Organizing Resource Allocation in LTE-U with Uplink-Downlink Decoupling", IEEE Transactions on Wireless Communications, vol. 16, no. 1, pp. 3-16, January 2017.

    28. M. Chen, W. Saad, C. Yin, and M. Debbah, "Echo State Networks for Proactive Caching and Content Prediction in Cloud Radio Access Networks," in Proc. of the IEEE Global Communications Conference (GLOBECOM), Workshop on 5G RAN Design, Washington, DC, USA, December 2016.

AI for UAV Communications

AI for Virtual Reality over Wireless Networks

    1. O. Hashash, C. Chaccour, and W. Saad, "Edge Continual Learning for Dynamic Digital Twins over Wireless Networks", in Proc. 23rd IEEE International Workshop on Signal Processing Advances in Wireless Communications, Special Session on Intelligence and Processing at the Edge for Next Generation Networks, Oulu, Finland, June 2022.

    2. Y. Wang, M. Chen, Z. Yang, W. Saad, T. Luo, H. V. Poor, and S. Cui, "Meta-Reinforcement Learning for Immersive Virtual Reality over THz/VLC Wireless Networks", Proc. of the IEEE International Conference on Communications (ICC), Mobile and Wireless Networks Symposium, Montreal, Canada, June 2021.

    3. M. Chen, O. Semiari, W. Saad, X. Liu, and C. Yin, "Federated Deep Learning for Immersive Virtual Reality over Wireless Networks", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Cognitive Radio and AI-Enabled Networks Symposium, Waikoloa, HI, USA, December 2019.

    4. M. Chen, W. Saad, and C. Yin, "Echo-Liquid State Deep Learning for 360∘ Content Transmission and Caching in Wireless VR Networks with Cellular-Connected UAVs", IEEE Transactions on Communications, vol. 67, no. 9, pp. 6386-6400, Sept. 2019.

    5. M. Chen, W. Saad, C. Yin, and M. Debbah, "Data Correlation-Aware Resource Management in Wireless Virtual Reality (VR): An Echo State Transfer Learning Approach", IEEE Transactions on Communications, vol. 67, no. 6, pp. 4267-4280, June 2019.

    6. M. Chen, W. Saad, and C. Yin, "Liquid State based Transfer Learning for 360 Image Transmission in Wireless VR Networks", in Proc. of the IEEE International Conference on Communications (ICC), Cognitive Radio and Networks Symposium, Shanghai, China, May 2019.

    7. M. Chen, W. Saad, and C. Yin, "Deep Learning for 360 Content Transmission in UAV-Enabled Virtual Reality", in Proc. of the IEEE International Conference on Communications (ICC), Next-Generation Networking and Internet Symposium, Shanghai, China, May 2019.

    8. M. Chen, W. Saad, and C. Yin, "Virtual Reality over Wireless Networks: Quality-of-Service Model and Learning-Based Resource Management", IEEE Transactions on Communications, vol. 66, no. 11, pp. 5621 - 5635, November 2018.

    9. M. Chen, W. Saad, and C. Yin, "Resource Management for Wireless Virtual Reality: Machine Learning Meets Multi-Attribute Utility", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Mobile and Wireless Networks Symposium, Singapore, December 2017.

    10. M. Chen, W. Saad, C. Yin, and M. Debbah, "Echo State Transfer Learning for Data Correlation Aware Resource Allocation in Wireless Virtual Reality", in Proc. of the 51st Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, November 2017.

AI for Ultra Reliable Low Latency Communication (URLLC)

    1. A. T. Z. Kasgari, W. Saad, M. Mozaffari, and H. V. Poor, "Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication", IEEE Transactions on Communications, vol. 69, no. 2, pp. 884 - 899, February 2021.

    2. A. T. Z. Kasgari and W. Saad, "Model-Free Ultra Reliable Low Latency Communication (URLLC): a Deep Reinforcement Learning Framework", in Proc. of the IEEE International Conference on Communications (ICC), Next-Generation Networking and Internet Symposium, Shanghai, China, May 2019.

    3. S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, "Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications", IEEE Transactions on Communications, vol. 68, no. 2, pp. 1146-1159, Feb. 2020.

    4. M. Abdel-Aziz, S. Samarakoon, M. Bennis, and W. Saad, "Ultra-Reliable and Low-Latency Vehicular Communication:An Active Learning Approach", IEEE Communications Letters, vol. 24, no. 2, pp. 367-370, Feb. 2020.

    5. S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, "Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications", IEEE Transactions on Communications, vol. 68, no. 2, pp. 1146-1159, Feb. 2020.

    6. A. T. Z. Kasgari and W. Saad, "Model-Free Ultra Reliable Low Latency Communication (URLLC): a Deep Reinforcement Learning Framework", in Proc. of the IEEE International Conference on Communications (ICC), Next-Generation Networking and Internet Symposium, Shanghai, China, May 2019.

    7. S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, "Federated Learning for Ultra-Reliable Low-Latency V2V Communications", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Selected Areas in Communications Symposium - Tactile Internet Track, Abu Dhabi, UAE, December 2018.

    8. M. Abdel-Aziz, C.-F. Liu, S. Samarakoon, M. Bennis, and W. Saad, "Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail", in Proc. of the IEEE Global Communications Conference (GLOBECOM), Next Generation Networking and Internet Symposium, Abu Dhabi, UAE, December 2018.

AI for Internet of Things

AI for Autonomous Systems

AI for Security