为保证疫情防控期间学院国际交流相关工作顺利开展，有效提升教师科研能力，增强师生国际化视野，学院特别邀请加拿大工程研究院院士、加拿大工程院院士、加拿大皇家科学院院士、加拿大滑铁卢大学Xuemin Shen教授、Weihua Zhuang教授及日本工程院院士、日本东北大学Nei Kato教授线上举办6G无线智能网络国际研讨会。现将相关事宜通知如下：
一、 会议安排：
1. 会议主题：6G无线智能网络
2. 会议时间：
2020年11月25日 09:30 -11:30 学术报告
13:30-15:30 学术交流
3. 会议地点：
线上Zoom会议链接：https://zoom.com.cn/j/68721132831?pwd=N0ZxT2puNWs5MGYweW1hQXJNNWJGZz09
会议 ID：687 2113 2831
密码：969121
线下会场：南湖校区一教304
二、 特邀嘉宾及主讲内容：
1. Title: Next Generation Wireless Networks: Holistic Network Virtualization and Edge Intelligence.
Abstract: Next-generation wireless networks should have scalable network architecture, provide automated network management, and perform intelligent and flexible resource allocation in order to support diversified applications with various service requirements. Through network virtualization and edge intelligence, this presentation will give potential solutions to achieve the objective.
Xuemin (Sherman) Shen：加拿大工程研究院院士、加拿大工程院院士、加拿大皇家学会院士、中国工程院外籍院士、滑铁卢大学教授；
2. Title: Channel Access Management for Cellular IoT Applications
Abstract: Massive machine communication is an emerging technology to enable various Internet of Things (IoT) applications in future communication networks. The IoT applications generate uplink-heavy data traffic that is composed of a large number of small data packets with different service quality requirements. The unique characteristics requires new channel access mechanisms for cellular systems to support a massive number of devices with limited computational capabilities. In this presentation, we propose a novel two-hop energy efficient and resource preserving channel access solution, exploiting node clustering, data aggregation, and non-orthogonal multiple access (NOMA). Further, we incorporate user association to maximize the number of supported devices while minimizing the overall device energy consumption. Numerical results demonstrate the cost-effectiveness of the proposed channel access solution and the usefulness of data aggregation.
Weihua Zhuang：加拿大工程研究院院士、加拿大工程院院士、加拿大皇家学会院士、滑铁卢大学教授；
3. Title: Ten Challenges in Advancing Machine Learning Technologies towards 6G
Abstract: As the 5G standard is being completed, the academia and industry have begun to consider more developed cellular communication technique, 6G, which is expected to achieve high data rate up to 1Tb/s and broad frequency bands of 100GHz to 3THz. Besides the significant upgrade of the key communication metrics, Artificial Intelligence (AI) has been envisioned by many researchers as the most important feature of 6G, since the state-of-the-art machine learning technique has been adopted as the top solution in many extremely complex scenarios. Network intelligentization will be the new trend to address the challenges of exponentially increasing number of connected heterogeneous devices. However, compared with the application of machine learning in other fields, such as computer games, current research on intelligent networking still has a long way to go for realizing the automatically-configured cellular communication systems. Various problems in terms of communication system, machine learning architectures, and computation efficiency should be addressed for the full use of this technique in 6G. In this talk, I will introduce ten most critical challenges in advancing the intelligent 6G system. These challenges are analyzed from the perspectives of 6G service requirements, AI algorithm design, practical deployment, and future standardization.
Nei Kato：日本工程院院士、IEEE/IEICE Fellow、日本东北大学信息与科学研究院副院长、IEEE TVT现任主编。
三位主讲人分享后，欢迎与会师生分享自己的研究提案，也可以针对自己在本次报告中或科研工作上遇到的问题进行交流。
