Scope and Topics
This workshop proposal sits at the confluence of two transformational technologies, namely the fifth generation of wireless communication systems, known as 5G, and machine learning (ML) or artificial intelligence (AI). On the one hand while the evolutionary part of 5G, enhanced mobile broadband (eMBB), focusing mainly on millimeter-wave transmissions has made significant progress fundamentals of ultra-reliable and low-latency communication (URLLC), one of the major tenets of the 5G revolution, are yet to be fully understood. In essence, URLLC warrants a departure from average-based system design towards a clean-slate design centered on tail, risk, and scale. While risk is encountered when dealing with decision making under uncertainty, scale is driven by the sheer amount of devices, antennas, sensors, and actuators, all of which pose unprecedented challenges in network design, optimization, and scalability. On the other hand in just a few years, breakthroughs in ML and particularly deep learning have transformed ever aspect of our lives from face recognition, medical diagnosis, and natural language processing. This progress has been fuelled mainly by the availability of more data and more computing power. However, the current premise in classical ML is based on a single node in a centralized and remote data center with full access to a global dataset and a massive amount of storage and computing power, sifting through this data for inference. Nevertheless the advent of a new breed of intelligent devices and high-stake applications ranging from drones to augmented/virtual reality (AR/VR) applications, and self-driving vehicles, makes cloud-based ML inadequate. These applications are real-time, cannot afford latency, and must operate under high reliability, even when network connectivity is lost.
This workshop aims at discussing the latest innovations and challenges in 5G and beyond, role of machine learning in unlocking the full benefit of 5G. Topics of interest include:
- Data-driven ultra-reliable and low-latency communication
- resource slicing, eMBB and URLLC multiplexing/slicing
- Data-driven control and computing for edge and fog
- Deep learning for channel coding and transceiver design
- Deep reinforcement learning radio resource management
- Federated and transfer Learning over the wireless edge
- Intelligent fog computing and control
- Unmanned aerial vehicles, V2X and AI-driven mobility
- Cloud AI, edge AI
- AI for wireless big data analysis
Submission Guideline
The submitted papers should be original, not published or currently under review for publications in any other journal or conference. All submission must be formatted in standard IEEE camera-ready format and must be written in English and be at most six (6) printed papers in length, including figures. Papers should be submitted through EDAS System.
EDAS submission link: https://edas.info/newPaper.php?c=25822&track=96936
Important Dates
- Paper submission deadline: 15 May 2019
- Notification of acceptance: 25 June 2019
Workshop Organizers
- Mehdi Bennis (Associate Professor with the Centre for Wireless Communications (CWC) at the University of Oulu, Finland), Email: mehdi.bennis@oulu.fi
- Xianfu Chen (Senior Scientist with VTT Technical Research Centre of Finland, Finland), Email: xianfu.chen@vtt.fi
- Celimuge Wu (Associate Professor with the Graduate School of Informatics and Engineering at the University of Electro-Communications, Japan), Email: clmg@is.uec.ac.jp
- Dr. Honggang Zhang (Full Professor with the College of Information Science and Electronic Engineering at the Zhejiang University, China), Email: honggangzhang@zju.edu.cn