Future Network Artificial Intelligence and Machine Learning Workshop

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27-29 September 2021 | Virtual & Free
Registration is now open

 

Overview
This workshop will explore Artificial Intelligence and Machine Learning (AI/ML) algorithms, techniques, systems and standards that can be utilized to optimize performance of 5G and Future Networks. The objective of this workshop is to bring together experts from the globe and create a joint platform for information exchanges, presentation of results, and fruitful discussions to identify gaps and future directions for the IEEE Future Network Initiative (FNI) Artificial Intelligence and Machine Learning Group.

Call for Speaker Proposals
Submit your speaking proposals today.
If you do not have an EDAS account, create one here.
Deadline: 10 September 2021

Each proposal (maximum 2 pages) must include:

  • Title and Abstract
  • Specific track (see descriptions below): Research Challenges, Industry Applications, Standards & Architecture
  • A short biography (no more than 200 words) of each speaker
  • An explanation of the motivation, background, objective, description of the challenges to be covered, relevance, and timeliness

The proposal will be reviewed based on its relevance, the topic’s importance to the workshop, and the diversity it offers in terms of the problem, proposed solution, and evaluation approach.

Track Descriptions
Research Challenges
Many of the AI/ML techniques are focused on application areas that are not directly linked to 5G networks. For example, AI/ML for computer Vision has been a hot research area since it applies directly to Smart Cars and autonomous driving. Here, Deep Neural Networks (DNNs) are used to detect and classify different objects in an image scene by using spatial correlation and processing. On the other hand, Natural Language Processing (NLP) and similar applications use Recurrent Neural Networks (RNN) to capture the temporal relationships of the input signal. However, 5G networks have a richer set of technology needs that include network automation, security, physical layer optimizations and more. The goal of this track is to describe new approaches and techniques that are targeted for 5G performance improvement, autonomy, etc.

Invited research areas of interest include, but are not limited to:

  • Application of AI/ML architectures such as DNN, RNN and Transfer Learning (TL)
  • Data collection, sources and potential sharing of such for AI/ML systems
  • Autonomous verification methods to ensure that AI/ML models are effective and have not been compromised
  • Methods and use-cases for the use of AI/ML in network automation
  • Techniques for the use of AI/ML in 5G security and privacy to solve problems such as network intrusion detection and prevention, botnet detection etc.
  • Methods to secure the AI/ML models in a distributed environment
  • Advances in AI/ML for the physical layer including spectrum awareness, Dynamic Spectrum Access (DSA) and resource allocation.
  • Description and analysis of current supervised and/or unsupervised learning methods and the potential application to 5G systems, e.g. the application of network data to existing DNN architectures, such as GoogleNet, AlexNet, YOLO, etc.
  • Autonomous orchestration and optimization of virtualized and NFV as applicable for 5G and beyond architectures, use-cases and market verticals (uRLLC, mMTC, eMBB, MEC).
  • AI/ML architectures for lower power and complexity devices such as Internet of Things
  • Future advances in AI/ML frameworks to support 5G market verticals
  • AI/ML techniques and implementations for 5G network slicing and market verticals

Industry Applications
In the Industry Applications track, we invite speakers to discuss the industrial deployments of security paradigms and their adoption by stakeholders in the value chain, such as operators, vendors, integrators, and the like. The objective of this track is to understand stakeholders' strategies and plans around 5G AI/ML specifically to support the evolution of 5G deployments and the different 5G market verticals. We encourage contributors to share their experiences and how it may be beneficial to 5G and Future Networks. Descriptions of current work on pre-market 5G testbeds, applications, etc. that have been developed through organizations such as ITU, ETSI, 3GPP 5G PPP, and others are also welcomed in this track.

Invited contributions include but are not limited to the following:

  • Description of 5G testbeds and applications that have been developed or are currently under development through organizations such as 5G PPP
  • The effect, challenges and options with regard to AI/ML of deploying open systems such as Open RAN, Open Core, etc.
  • Existing and under-development AI/ML technologies that are relevant and could benefit 5G and future networks
  • Practical approaches for scalable, distributed AI/ML implementation and orchestration across the same and different 5G networks
  • Interoperability AI/ML-based security challenges and approaches considering evolution of deployments, interconnectivity with public/private cloud providers
  • Results and reports of recent studies and pilot projects considering 5G AI/ML

Standards & Architecture
5G technologies provide ubiquitous connectivity while also addressing the demands of both individual consumers and businesses. In order to support various 5G use-cases and applications, there is a critical need to design autonomous systems using AI/ML. 5G networks need to be flexible, adaptive, scalable and able to dynamically react to the changes in the network quite rapidly which will require advanced automation technologies in future networks. Various standards bodies including 3GPP, IEEE, and ETSI have been looking into AI/ML for 5G networks. To that end, the IEEE FNI AI/ML Working Group is calling presenters to provide and share insights around progress and gaps in 5G AI/ML standards and architectures as relevant to 5G deployments and use-cases. It is the intent of the FNI AI/ML WG to develop a roadmap that will evolve into a set of standards to address the technology gaps in 5G and Future Network using AI/ML architectures and techniques.

To that end, this area will investigate current work that includes:

  • Standards bodies (IEEE, ITU, ETSI, 3GPP, etc.) AI/ML results and developments for 5G and future networks
  • Discussion of national and international organization plans to implement AI/ML for different applications - security, spectrum efficiency and awareness, etc.
  • Standards for AI/ML automation and orchestration that addresses the different components of the 5G architecture and 5G enabled market verticals
  • Different AI/ML implementation architectures, options and trade-offs in across the 5G network use-cases and relevant market verticals

Workshop Co-Chairs
Dr. Michael A. Enright, Quantum Dimension, Inc., USA - This email address is being protected from spambots. You need JavaScript enabled to view it.
Dr. Deepak Kataria, IPJunction, USA - This email address is being protected from spambots. You need JavaScript enabled to view it.