Machine Learning and AI 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

  1. M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, "Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial", IEEE Communications Surveys and Tutorials, to appear, 2019 (longer, pre-print version found here: "https://arxiv.org/pdf/1710.02913v1.pdf").
  2. A. Ferdowsi, U. Challita, and W. Saad, "Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems", IEEE Vehicular Technology Magazine, Special Issue on Mobile Edge Computing for Vehicular Networks, to appear, 2018.
  3. U. Challita, A. Ferdowsi, M. Chen, and W. Saad, "Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs", IEEE Wireless Communications Magazine, Special Issue on Integrating UAVs into 5G and Beyond, to appear, 2018.

Federated Learning and Wireless Networks

  1. 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", arXiv:1909.07972, 2019.
  2. 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, to appear, 2019.
  3. 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.
  4. 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.
  5. S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, "Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications", arXiv:1807.08127, 2018.
  6. 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. 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, to appear, 2019.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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

  1. U. Challita, W. Saad, and C. Bettstetter, "Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management",  IEEE Transactions on Wireless Communications, vol. 18, no.4, pp. 2125-2140, April 2019.
  2. 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, to appear, 2019.
  3. Q Zhang, W Saad, M Bennis, X Lu, M Debbah, W Zuo, "Predictive Deployment of UAV Base Stations in Wireless Networks: Machine Learning Meets Contract Theory", arXiv preprint arXiv:1811.01149
  4. U. Challita, W. Saad, and C. Bettstetter, "Deep Reinforcement Learning for Interference-Aware Path Planning of Cellular-Connected UAVs", in Proc. of the IEEE International Conference on Communications (ICC), Wireless Networking Symposium, Kansas City, MO, USA, May 2018.
  5. 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.
  6. S. Ali, A. Ferdowsi, W. Saad, and N. Rajatheva, "Sleeping Multi-Armed Bandits for Fast Uplink Grant Allocation in Machine Type Communications", in Proc. of  the IEEE Global Communications Conference (GLOBECOM), Workshop on Ultra-high speed, low latency and massive connectivity communication for 5G/B5G,  Abu Dhabi, UAE, December 2018.
  7. M. Chen, M. Mozaffari, W. Saad, C. Yin, M. Debbah, and C. S. Hong, "Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience", IEEE Journal on Selected Areas in Communications (JSAC), Special Issue on Human-In-The-Loop Mobile Networks, vol. 35, no. 5, pp. 1046 - 1061, May 2017.
  8. D. Athukoralage, I. Guvenc, W. Saad, and M. Bennis, "Regret Based Learning for UAV assisted LTE-U/WiFi Public Safety Networks," in Proc. of the IEEE Global Communications Conference (GLOBECOM), Mobile and Wireless Networks Symposium, Washington, DC, USA, December 2016.
  9. M. Chen, W. Saad, and C. Yin, "Optimized Uplink-Downlink Decoupling in LTE-U Networks: An Echo State Approach," in Proc. of the IEEE International Conference on Communications (ICC), Mobile and Wireless Networks Symposium, Kualalumpur, Malaysia, May 2016.

AI for Virtual Reality over Wireless Networks

  1. 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, to appear, 2019.
  2. 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, to appear, 2019.
  3. 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.
  4. 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.
  5. 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.
  6. 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,” arXiv:1804.03284, 2018.
  7. 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.
  8. 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 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.
  2. S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, "Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications", arXiv:1807.08127, 2018.
  3. 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 Internet of Things

  1. A. Ferdowsi and W. Saad, "Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things",  in Proc. of  the IEEE Global Communications Conference (GLOBECOM), Communication & Information System Security Symposium, Waikoloa, HI, USA, December 2019.
  2. T. Park, N. Abuzainab, and W. Saad, "Learning How to Communicate in the Internet of Things: Finite Resources and Heterogeneity", IEEE Access, Special Issue on Optimization for Emerging Wireless Networks: IoT, 5G and Smart Grid Communication Networks, vol. 4, November 2016.
  3. T. Park and W. Saad, "Resource Allocation and Coordination for Critical Messages using Finite Memory Learning," in Proc. of the IEEE Global Communications Conference (GLOBECOM), Workshop on Self-organization Networks for 5G Wireless Communications and Internet of Things, Washington, DC, USA, December 2016.
  4. T. Park and W. Saad, "Learning with Finite Memory for Machine Type Communication," in Proc. of the IEEE 50th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, March 2016.

AI for Autonomous Systems

  1. A. Ferdowsi, S. Ali, W. Saad, and N. B. Mandayam, "Cyber-Physical Security and Safety of Autonomous Connected Vehicles: Optimal Control Meets Multi-Armed Bandit Learning", IEEE Transactions on Communications, to appear, 2019.
  2. A. Ferdowsi, U. Challita, W. Saad, and N. B. Mandayam, "Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems", in Proc. 21st IEEE International Conference on Intelligent Transportation Systems, Maui, HI, USA, November 2018.

AI for Security

  1. A. Ferdowsi and W. Saad, "Deep Learning for Signal Authentication and Security in Massive Internet of Things Systems", IEEE Transactions on Communications, to appear, 2018.
  2. Y. Sharaf-Dabbagh and W. Saad, "Authentication of Everything in the Internet of Things: Learning and Environmental Effects", arXiv:1805.00969, 2018.
  3. Y. Sharaf-Dabbagh and W. Saad, "Transfer Learning for Device Fingerprinting with Application to Cognitive Radio Networks," in Proc. of IEEE 26th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Hong Kong, September 2015. The paper received a "Best Paper Award" at PIMRC'15.