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 millimetre-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 cantered 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 every aspect of our lives from face recognition and medical diagnosis to 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 centre 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 the beyond, role ML in unlocking the full benefits 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
- Digital twin
Workshop Organizers:
- Xianfu Chen, VTT Technical Research Centre of Finland, Finland, Oulu, Email: xianfu.chen@vtt.fi
- Mehdi Bennis, University of Oulu, Finland, Email: mehdi.bennis@oulu.fi
- Rui Yin, Zhejiang University City College, China, Email: yinrui@zju.edu.cn
Submission Guideline:
Submission link: https://edas.info/newPaper.php?c=27475&track=102662
Important Dates:
Paper Submission Deadline: June 30, 2020
Notification of Acceptance: July 20, 2020
Camera-Ready Papers Due: July 27, 2020