Machine Learning for Artificially Intelligent Wireless Networks: Challenges and Opportunities

Tuesday, 18 June 2019, 14:00-18:00, room R3
Speaker:
  • Walid Saad (Viginia Tech, USA)

 

Motivation and Context

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real- time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. AI will play major roles in next-generation wireless networks ranging from data analytics to AI-powered self-organizing networks. To this end, the goal of this tutorial is to provide a holistic on the topic of machine learning for AI-powered wireless networks. In particular, we will first provide a comprehensive treatment of the fundamentals of machine learning and artificial neural networks, which are one of the most important pillars of machine learning. Then, we introduce a classification of the various types of neural networks that include feed-forward neural networks, recurrent neural networks, spiking neural networks, and deep neural networks. For each type, we provide an introduction on their basic components, their training processes, and their wireless use cases. Then, we overview a broad range of wireless applications that can make use of neural network designs. This range of applications includes ultra-reliable low latency communications (URLLC), wireless virtual reality, mobile edge caching, drone-based communications, Internet of Things, and vehicular networks.
For each application, we first outline the main rationale for applying machine learning while pinpointing illustrative scenarios. Then, we overview the challenges and opportunities brought forward by the use of neural networks in the specific wireless application. We complement this overview with a detailed example drawn from the state-of-the-art. Finally, we conclude by shedding light on the potential future works within each specific area and within the overall area of AI for wireless networks.

 

Structure and Content

Introduction to Machine Learning:

  • What is machine learning?
  • Machine learning and artificial
  • Brief overview on the basics of machine

 

Artificial Neural Networks (ANNs) Preliminaries:

  • Brief introduction to machine learning and motivation behind artificial neural
  • Introduction to the architecture of artificial neural networks and their training

 
Classification of Artificial Neural Networks:

  • Introduction to recurrent neural networks: fundamentals, training, and specific examples such as echo state networks.
  • Introduction to spiking neural networks: fundamentals, training, and specific examples such as liquid-state machines.
  • Introduction to deep neural networks: fundamentals, training, and specific examples such as long short term memory and convolutional neural
  • Brief review of other types of artificial neural
  • Collaborative learning and neural

 
Reinforcement Learning:

  • Introduction to reinforcement
  • Q-learning: basics and state of the
  • Boltzmann-Gibbs learning: basics and state of the
  • Reinforcement learning with artificial neural
  • Deep reinforcement learning: basics and state of the

 
AI for URLLC

  • Brief introduction to URLLC and the need for AI
  • Overview of the applications for reinforcement learning and artificial neural networks for URLLC
  • Step-by-step explanation of an example application that uses deep reinforcement learning for model-free URLLC
  • Future works and opportunities for ANNs in URLLC

 
AI for Virtual Reality (VR) over Wireless Networks:

  • Brief introduction to VR and explanation on the role of VR over wireless in future
  • Overview of the applications for reinforcement learning and artificial neural networks in wireless
  • Step-by-step explanation of an example application that uses recursive neural networks for wireless
  • Future works and opportunities for ANNs in the wireless VR application domain

 
AI for Mobile Edge Caching and Computing:

  • Brief introduction to the basic notions in mobile edge caching and
  • Overview on potential applications of AI for mobile edge caching and
  • Step-by-step overview on a sample application that uses AI for mobile edge
  • Step-by-step overview on an illustrative application that uses AI for mobile edge
  • Future works and opportunities for optimizing edge caching and computing using machine learning

 
AI for Wireless Communications using Drones:

  • Overview on the role of drones in wireless communications and
  • Overview on potential applications of AI for drone-based communications.
  • Step-by-step overview on a sample application that uses recurrent and spiking neural networks for optimizing the performance of drone-based wireless
  • Step-by-step overview on an illustrative application that uses deep reinforcement learning for path planning in cellular-connected drone .
  • Future works and opportunities for optimizing drone communications using machine

 
AI for the Internet of Things (IoT):

  • Overview on the Internet of Things and its
  • Overview on potential applications of machine learning and AI within the Internet of
  • Step-by-step overview on a sample application that uses machine learning for big data analytics in the
  • Step-by-step overview on an illustrative application that uses reinforcement learning for optimizing IoT communications.
  • Step-by-step overview on the use of deep learning for IoT security and
  • Future works and opportunities for optimizing the IoT performance using machine

 
AI for Vehicular Networks:

  • Overview on vehicular networks and their
  • Overview on potential applications of machine learning and AI within vehicular
  • Step-by-step overview on a sample application that uses deep learning for optimized vehicular communications and security.

 
Federated Learning:

  • What is federated learning? why is it different from classical machine learning?
  • Principles of federated
  • Step-by-step overview of selected use cases (e.g., V2V)

 

Conclusions and future directions:

  • Brief overview on other applications of AI and machine learning, such as smart

Discussion of future directions and open opportunities: toward AI-inspired wireless networks.

Future works and opportunities for using AI in vehicular networks.

Walid Saad

Walid Saad (S’07, M’10, SM’15, F’19) received his Ph.D degree from the University of Oslo in 2010. He is currently an Associate Professor at the Department of Electrical and Computer Engineering at Virginia Tech, where he leads the Network Science, Wireless, and Security laboratory. His research interests include wireless networks, machine learning, game theory, security, unmanned aerial vehicles, cyber-physical systems, and network science. Dr. Saad is a Fellow of the IEEE and an IEEE Distinguished Lecturer. He is also the recipient of the NSF CAREER award in 2013, the AFOSR summer faculty fellowship in 2014, and the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He was the author/co-author of seven conference best paper awards at WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, and IEEE GLOBECOM in 2018. He is the recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, of the 2017 IEEE ComSoc Best Young Professional in Academia award, and of the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award. From 2015-2017, Dr. Saad was named the Stephen O. Lane Junior Faculty Fellow at Virginia Tech and, in 2017, he was named College of Engineering Faculty Fellow. He currently serves as an editor for the IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, IEEE Transactions on Cognitive Communications and Networking, and IEEE Transactions on Information Forensics and Security. He is an Editor-at-Large for the IEEE Transactions on Communications.