AI/ML Track

Artificial Intelligence (AI) and Machine Learning (ML) is a perfect match for 5G. While 5G offers capabilities to support low latency and very high speeds (e.g., eMBB), massive number of devices (e.g., mMTC), heterogenous mix of traffic types from a diverse and demanding suite of applications (e.g., URLLC), AI/ML complements by learning from complex patterns to provide scope for autonomous operation, transforming 5G into a scalable real-time network that is data-driven.

AI/ML is being used for 5G network planning, automation of network operations (e.g., provisioning, optimization, fault prediction, security, fraud detection), network slicing, reducing operating costs, and improving both the quality of service and customer experience based on chatbots, recommender systems, and techniques such as robotic process automation (RPA). Further, AI and ML is being used across all layers – from disaggregated radio access layer (5G RAN), to integrated access backhaul (IAB) to the distributed cloud layer (5G Edge/Core) to fine tune performance.

For 5G distributed cloud layer, AI and ML is being used for optimizing use of system resources, autoscaling, anomaly detection, predictive analytics, prescriptive policies, and so on. Further, 5G distributed cloud layer provides acceleration technologies for AI/ML workloads to support federated and distributed learning.

Besides the above, AI/ML is also being used for customer experience management and business support systems to support a multitude of emerging applications (e.g., AR/VR, Industrial IoT, autonomous vehicles, drones, Industry 4.0 initiatives, Smart Cities, Smart Ports).

In all the above cases, aspects of data integrity, legal rights to data, data collection, data pipeline management, data lake design, and data science project cycles are considered. Further, aspects of model development, model training, model validation, model deployment, model monitoring and life cycle management including model libraries and model update/upgrade in service are also considered.

Several learning approaches such as supervised learning, unsupervised learning, reinforcement learning, federated learning, distributed learning, transfer learning, and deep learning based on algorithms such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) are utilized to train data models based on target use cases.

Motivated by this progress and to further advance related discussions, the AI/ML topical will bring together leading experts from telecom service providers, system OEMs, software service providers, silicon vendors, open source network automation projects as well as leading researchers from academia to share their perspectives on opportunities and challenges to the operation of 5G using AI/ML. It will provide a unique forum for practitioners and researchers to share perspectives on recent developments, evolving landscape of AI/ML technologies, deployment use cases in various 5G scenarios and business benefits. Architects, Developers, Engineers, Testers, and Business Leaders as well as Students and Researchers from academia will surely find it useful to listen and have an opportunity to network with experts and innovators from industry and academia.

Workshop Co-Chairs:

Dr. Deepak Kataria
Chair,  IEEE Princeton Central Jersey Section (Region 1)

Dr. Anwar Walid
Director of Network Intelligence and Distributed Systems Research, Nokia Bell Labs