International Workshop on 5G Long Term Evolution and Intelligent Communication
“5G From Lab to Life – Where Theory Meets Practice”
As mobile network operators across the world have started deploying various scale commercial trial of 5G and the first 5G commercialized user equipment has also been released to market, it is time to re-think the original technical objectives of 5G and study any existing gaps between theories and measurements. There is much benefit from the lessons learnt and experimental results derived from deploying these 5G trial experiments. Hence it is important to have a workshop that will provide an opportunity to allow developers, technical experts, and scientific researchers to communicate and discuss the different viewpoints through peer-reviewed papers in addition to invited papers. The objective of this workshop is to share experiences of 5G network trials in all aspects and advance the state-of-the-art of 5G networks or beyond. Towards, there are many open issues should be discussed and rethink here, e.g.,
How have eMBB/V2X test-beds and trials performed against technology claims? Could new technologies meet the gap if there is any?
What have we learned since 5G NR Stand-Alone and Non-Stand-Alone architectures were agreed? Any opportunities, challenges, roadblocks to mass deployment?
What does the industry need to do to meet the evolving needs for 5G going forward?
How does the industry prioritize development efforts to ensure sustainable success for 5G?
Is new architecture needed for 5G to support new technology, e.g., machine learning?
SCOPE AND OBJECTIVES
This workshop aims to gather researchers, industry partners, and users to present and debate these challenges through the aspects of 5G new technologies, trial measurements, etc. Specifically, but not exclusively, the workshop addresses the following issues:
- 5G trial implementation and deployment including real-world lessons learnt
- Tools and services for testbed users and operators
- Experimentation with future wireless platforms including: Machine Learning, mmWave, V2X, etc
- Experimentation with integrated Cloud/MEC/Fog computing
- Support for infrastructure slicing and isolation
- Data-driven wireless network architecture with AI
- AI for mobile edge computing
- Artificial neural networks for wireless networking
- Information theoretic limits for 5G and beyond
- Advanced modulation and coding schemes
- Cooperative communications in AI empowered HetNets
- Machine learning for V2X networks
- Machine learning for 5G and beyond
- Prof. Geoffrey Ye Li, Georgia Institute of Technology, USA, email@example.com
Title: Deep Learning in Communications.
Abstract: It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communications. In this talk, we present our recent work in DL in communications, including physical layer processing and resource allocation. DL can improve the performance of each individual (traditional) block in the conventional communication systems or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN).
The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. Deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving and can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to reduce the complexity of mixed integer non-linear programming (MINLP). We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.
Biography: Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications. In these areas, he has published around 500 referred journal and conference papers in addition to over 40 granted patents. His publications have cited by around 40,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year since 2001. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTSJames Evans Avant Garde Award, 2014IEEE VTS Jack Neubauer Memorial Award, 2017IEEE ComSoc Award for Advances in Communication,2017IEEE SPS Donald G. Fink Overview Paper Award, and 2019 IEEE ComSoc Edwin Howard Armstrong Achievement Award. He also won the 2015Distinguished Faculty Achievement Awardfrom the School of Electrical and Computer Engineering, Georgia Tech.
- Dr. Zhibo Pang, ABB Corporate Research, Sweden, firstname.lastname@example.org
Title: Wireless for critical control in industrial systems: requirements, gaps, and research directions.
Abstract: Coming soon.
- Na Yi, Univerisity of Surrey, United Kingdom, email@example.com
- Tao Chen, VTT Technical Research Centre, Finland, firstname.lastname@example.org
- Uwe Herzog, Eurescom, Germany, email@example.com
- Wei Deng, China Mobile, China, firstname.lastname@example.org
- Songyan Xue, University of Surrey, United Kingdom, email@example.com
Paper submission deadline: February 1, 2020 (Firm Extended Deadline)
Acceptance notification: February 20, 2020
Camera-ready: March 6, 2020
Workshop: June 7, 2020
Submission link: https://edas.info/N26810
Workshop Website: http://info.ee.surrey.ac.uk/CCSR/IWSDN/