明尼苏达大学 钱风教授给我院师生做学术报告

发布者:杨淳沨发布时间:2021-12-20浏览次数:259

应东南大学智慧物联网实验室邀请,1223日(周四)早上 10:00 11:00,明尼苏达大学 钱风教授给我院师生做学术报告,具体情况如下: 

报告题目:A Comprehensive Examination of Emerging 5G Services: Challenges and Opportunities

报告人:明尼苏达大学 钱风 教授 

北京时间:20211223日(周四)早上 10:00 11:00

地点:线上报告

腾讯会议 链接: 

会议主题:学术报告 -- 明尼苏达大学 钱风 教授

会议时间:2021/12/23 10:00-11:00 (GMT+08:00) 中国标准时间 - 北京

点击链接入会,或添加至会议列表:

https://meeting.tencent.com/dm/P5ZzvhkUwYLP

腾讯会议ID806-638-725

 

摘要:

5G is expected to support sub-millisecond latency as well as ultra-high throughput of 20 Gbps that is a 100x improvement compared to its predecessor 4G/LTE. However, there exists a vacuum in understanding how 5G as a technology performs in the wild and whether it can fulfill its promises. To fill these voids, we examine the commercial 5G landscape across several key dimensions -- network performance, radio power characteristics, and quality-of-experience implications for mobile applications -- to provide key insights for interested stakeholders, and propose intelligent mechanisms for application developers to better leverage 5G technology. To this end, using tools we developed in-house, we conducted a detailed measurement study to provide a first impression of the network performance characteristics of 5G services (including that of the much-anticipated mmWave). Subsequently, we expanded its scope and conducted the first comprehensive measurement study to investigate the power consumption of 5G radio on commodity smartphones. Overall, our findings reveal key characteristics of commercial 5G services in terms of throughput, latency, performance bottlenecks, coverage, radio state transitions, and radio power consumption under diverse scenarios, application usage (file download, web browsing, video streaming) with detailed comparisons to 4G/LTE networks. Furthermore, we also quantitatively reveal critical trade-offs (e.g., ultra-high vs. stable network performance, performance vs. energy) that get amplified with 5G. Leveraging the above insights, we further proposed a data-driven deep-learning-based framework for mmWave 5G through

 

报告人简介:


I am an associate professor in the Computer Science and Engineering Department at University of Minnesota - Twin Cities. My research interests cover the broad areas of mobile systems, AR/VR, mobile networking (including 5G), wearable computing, real-world system measurements, and system security. I am honored to receive several awards including the AT&T Key Contributor Award (KCA) (2014), NSF CRII Award (2016), Google Faculty Award (2016), ACM CoNEXT Best Paper Award (2016,2018), AT&T VURI Award (2017), NSF CAREER Award (2018), IU Trustees Teaching Award (2018), DASH-IF Excellence Award (2019), Cisco Research Award (2021), and ACM SIGCOMM Best Student Paper Award (2021).


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