Special Session

Special Session

Special Session 1: AI-based Evolution for Networking

08:30-10:10, October 21 (Wednesday), 2020

Chair :
Time Invited Talk Title Invited Speakers
08:30-10:10 1 Personalized Preference Learning for Caching: A Data Analytics Perspective Prof. Won-Yong Shin
Yonsei University, Korea
2 Improving Live Video Streming via Deep Learning-based Cellular Uplink Prediction Dr. Jinsung Lee
University of Colorado Boulder, USA
3 DRL-based resource management in network slicing for 5G and beyond Prof. Rongpeng Li
Zhejiang University, China

Invited Talk 1: ”Personalized Preference Learning for Caching: A Data Analytics Perspective”
Prof. Won-Yong Shin, Yonsei University, Korea

 
Abtract:
Equipping the communication functionality with machine learning-based or data-driven algorithms has received a considerable attention both in academia as well as in industrial communities. In this talk, we present a content-centric mobile network based on a preference learning framework. We consider a practical scenario where each user requests a content file according to its own preferences, which is motivated by the existence of heterogeneity in file preferences among different users. Under our model, we consider a device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: the personalized file preferences and the common file preferences. By assuming that the model parameters such as user activity levels, user file preferences, and file popularity are unknown and thus need to be inferred, we present a collaborative filtering (CF)-based approach to learn these parameters. Then, we propose two computationally efficient algorithms including a greedy approach to efficiently solve the cache allocation problems. Using a real-world dataset, we demonstrate that the proposed framework employing the personalized file preferences brings substantial gains over its counterpart for various system parameters.
 
Biography:

Won-Yong Shin received the Ph.D. degree in Electrical Engineering and Computer Science from Korea Advanced Institute of Science and Technology (KAIST), Republic of Korea, in 2008. In May 2009, he joined the School of Engineering and Applied Sciences, Harvard University, MA USA, as a Postdoctoral Fellow and was promoted to a Research Associate in October 2011. From 2012 to 2019, Dr. Shin was a faculty member (with tenure) in the Department of Computer Science and Engineering, Dankook University, Republic of Korea. Since March 2019, he has been with the Department of Computational Science and Engineering, Yonsei University, Republic of Korea, where he is currently an Associate Professor.
From 2014 to 2018, Dr. Shin served as an Associate Editor of the IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. He also served as an Organizing Committee for the 2015 IEEE Information Theory Workshop. He was a recipient of the Bronze Prize of the Samsung Humantech Paper Contest (2008) and the KICS Haedong Young Scholar Award (2016).

 

Invited Talk 2: ”Improving Live Video Streming via Deep Learning-based Cellular Uplink Prediction”
Dr. Jinsung Lee, University of Colorado Boulder, USA

 
Abtract:
As video calls and personal broadcasting become popular, the demand for mobile live streaming over cellular uplink channels is growing fast. However, current live streaming solutions are known to suffer from frequent uplink throughput fluctuations causing unnecessary video stalls and quality drops. As a remedy to this problem, we propose PERCEIVE, a deep learning-based uplink throughput prediction framework. PERCEIVE exploits a 2-stage LSTM (Long Short Term Memory) design and makes throughput predictions for the next 100ms. Our extensive evaluations show that PERCEIVE, trained with LTE network traces from three major operators in the U.S., achieves high accuracy in the uplink throughput prediction with only 7.67% mean absolute error and outperforms existing prediction techniques. We integrate PERCEIVE with WebRTC, a popular video streaming platform from Google, as a rate adaptation module. Our implementation on the Android phone demonstrates that it can improve PSNR by up to 6dB (4 times) over the default WebRTC while providing less streaming latency.
 
Biography:

Jinsung Lee received his B.S. and Ph.D. degrees in electrical engineering from KAIST in 2003 and 2012, respectively. From 2012 to 2017, he was a senior engineer with 5G research lab in Samsung Electronics. He is currently a postdoctoral researcher with the department of Computer Science in University of Colorado Boulder. His research interests include mobile computing and systems, video streaming, and low-latency transport-layer protocols. He received the Best Paper Awards from IEEE SECON in 2013 and ACM MobiSys in 2019. He is on the job market and interested in academia and research labs.

 

Invited Talk 3: ”DRL-based resource management in network slicing for 5G and beyond”
Prof. Rongpeng Li, Zhejiang University, China

 
Abtract:
Cellular Networks has evolved into the fundamental infrastructure of the information and communication technology (ICT) industry. Faced with the increasingly diversified service demands and heterogeneous network architecture, it is becoming essential to further integrate the artificial intelligence and cellular networks. In this talk, I will first provide a general overview of our research on service-aware provisioning, which we believe is an essential ingredient for intelligent cellular networks. Afterwards, I will dip into one of my recent works on reinforcement learning-based resource management for network slicing. Specifically, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots), and try to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, we leverage deep reinforcement learning to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement satisfaction ratio and spectrum efficiency, we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. Finally, we verify the performance of the proposed GAN -DDQN algorithms through extensive simulations.
 
Biography:

Dr. Li is now an assistant professor in College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou China. From August 2015 to September 2016, he was a research engineer with Wireless Communication Laboratory, Huawei Technologies Co. Ltd., Shanghai, China. His research interests currently focus on Reinforcement Learning, Data Mining and all broad-sense network problems (e.g., resource management, security, etc) and he has authored/coauthored tens of papers in the related fields. He serves as an Editor of China Communications.

 

Special Session 2: AI-based Evolution for Communications

14:30-16:10, October 21 (Wednesday), 2020

Chair :
Time Invited Talk Title Invited Speakers
14:30-16:10 4 Learning at the Network Edge – Intelligence and. Computation Prof. Tony Quek
Singapore University of Technology and Design (SUTD), Singapore
5 Sensory AI Software Platform for Multi-Device Environments Dr. Chulhong Min
Research Scientist, Nokia Bell Labs, UK
6 Learning-Based Cellular Softwarization for Network Optimization Dr. Jaeseong Jeong
Senior Specialist AI for Optimization, Ericsson Research AI, Sweden

Invited Talk 4: ”Learning at the Network Edge – Intelligence and. Computation”
Prof. Tony Quek, Singapore University of Technology and Design (SUTD), Singapore

 
Abtract:
The burgeoning advances from machine learning and wireless technologies are forging a new paradigm for future networks, which are expected to possess higher degree of intelligence. Due to the sheer volume of data generated by the end devices. as well as the increasing concerns about sharing private information, federated learning has emerged from the intersection of artificial intelligence and edge computing. Nevertheless, to make federated learning possible, one needs to tackle new challenges that require a fundamental departure from the standard methods designed for distributed optimization. In this talk, we will provide an overview of federated learning and share several issues associated with the deployment of federated learning in a wireless network. In addition, we will also share some of our preliminary works in this area.
 
Biography:

Tony Quek received the B.E. and M.E. degrees in Electrical and Electronics Engineering from Tokyo Institute of Technology, respectively. At MIT, he earned the Ph.D. in Electrical Engineering and Computer Science. Currently, he is the Cheng Tsang Man Chair Professor with Singapore University of Technology and Design (SUTD). He also serves as the Head of ISTD Pillar, Sector Lead for SUTD AI Program, and the Deputy Director of SUTD-ZJU IDEA. His current research topics include wireless communications and networking, big data processing, network intelligence, URLLC, and IoT. He received the 2008 Philip Yeo Prize for Outstanding Achievement in Research, the 2012 IEEE William R. Bennett Prize, the 2016 IEEE Signal Processing Society Young Author Best Paper Award, the 2017 CTTC Early Achievement Award, the 2017 IEEE ComSoc AP Outstanding Paper Award, the 2020 IEEE Communications Society Young Author Best Paper Award, the 2020 IEEE Stephen O. Rice Prize, and the 2016-2019 Clarivate Analytics Highly Cited Researcher. He is a Distinguished Lecturer of the IEEE Communications Society and a Fellow of IEEE.

 

Invited Talk 5: ”Sensory AI Software Platform for Multi-Device Environments”
Dr. Chulhong Min, Research Scientist, Nokia Bell Labs, UK

 
Abtract:
Sensory devices are now pervasive. These mobile, wearable, and IoT devices on and near our body are increasingly embracing bleeding-edge machine learning algorithms to uncover remarkable sensory applications. In this transformation, we are observing the emergence of multi-device systems as a natural course of multiple sensory devices surrounding us. The multiplicity is opening up an exciting opportunity to leverage the sensor redundancy and high availability afforded by multiple devices, thereby enabling rich, powerful, and collaborative sensing applications. However, such multiplicity comes at the expense of increasing complexity. Two key caveats that contribute to this complexity are device and data variabilities caused by runtime factors. In this talk, I will introduce the design and development of a brand-new software platform, offering best-effort inference in multi-device environments. Specifically, I will cover two key technical perspectives: multi-device model selection and device-to-device data translation. This talk will end with a discussion of the exciting applications we can begin to tackle.
 
Biography:

Chulhong Min is a research scientist in the Applications, Platforms & Software Systems (APSS) lab at Nokia Bell Labs, Cambridge, UK and a visiting fellow at University of Cambridge, UK. His current research explores the design of next-generation sensory AI systems to realise transformative multi-modal, multi-device sensing for disruptive mobile, wearable, and IoT services. Broadly, his research interests include mobile and embedded systems, Internet of things (IoT), and human-computer interaction. His work is published at ACM MobiSys, ACM SenSys, ACM UbiComp, ACM BuildSys, and prestigious journals. He won the best paper award at ACM CSCW 2014 and multiple demonstration awards. He served on a number of technical program committees and organising committees of various premier conferences and is an Associate Editor of ACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies.

 

Invited Talk 6: : “Learning-Based Cellular Softwarization for Network Optimization”
Dr. Jaeseong Jeong, Senior Specialist AI for Optimization, Ericsson Research AI, Sweden

 
Abtract:
Softwarization in cellular network provides flexibility in designing solutions towards automation and intelligence. In this talk, we introduce an instance of closed-loop solutions, AI-based cell shaping, that automatically optimizes network parameters in operation, e.g., antenna tilt. Reinforcement Learning (RL) can be used to learn an optimal tuning policy from the feedback observation, but its deployment to the real networks has significant challenges. We will discuss approaches to these challenges, and show some results of RL-based cell shaping.
 
Biography:

Jaeseong Jeong is a senior specialist AI for optimization at Ericsson Research AI. He has been leading multiple projects aimed mainly at deploying AI in real telecom networks. His research interests include reinforcement learning, large-scale optimization, telecom data analytic. He received the Ph.D. degree from Korea Advanced Institute of Science and Technology (KAIST) in 2014. Prior to joining Ericsson, he was with Automatic Control Department, KTH Royal Institute of Technology, Sweden as a postdoctoral researcher.

 

Special Session 3: New Waves

08:30-10:10, October 22 (Thueday), 2020

Chair :
Time Invited Talk Title Invited Speakers
08:30-10:10 7 Information Theory and Coding for Trustworthy and Scalable Machine Learning Prof. Kangwook Lee
University of Wisconsin-Madison, USA
8 6G Vision & Enabling Technologies Dr. Young-Jo Ko
Director, 6G Wireless Technology Research Section, ETRI, Korea
9 Satellite Communications in 5G and Beyond Prof. Jihwan Choi
DGIST, Korea

Invited Talk 7: ”Information Theory and Coding for Trustworthy and Scalable Machine Learning”
Prof. Kangwook Lee, University of Wisconsin-Madison, USA

 
Abtract:
In this talk, I will present the role of information theory and coding for enabling large-scale trustworthy ML systems. In the first part, I will talk about the role of information theory in developing a holistic framework for fair and robust machine learning. In the second part, the role of coding in building large-scale machine learning systems will be presented. I will talk about the recent developments in coded computation, a principled interface between distributed systems and coding theory. I will conclude with some future research directions on how ideas from information theory and coding can have a significant impact on future machine learning algorithms and systems.
 
Biography:

Kangwook Lee is an Assistant Professor at the Electrical and Computer Engineering department and the Computer Sciences department (by courtesy) at University of Wisconsin-Madison. Previously, he was a Research Assistant Professor at Information and Electronics Research Institute of KAIST and was a postdoctoral scholar at the same institute. He received his PhD in 2016 from the Electrical Engineering and Computer Science department at UC Berkeley. He is the recipient of The IEEE Joint Communications Society/Information Theory Society Paper Award, 2020.

 

Invited Talk 8: : “6G Vision & Enabling Technologies”
Dr. Young-Jo Ko, Director, 6G Wireless Technology Research Section, ETRI, Korea

 
Abtract:
After the world-first commercialization of 5G in Korea in 2019, world-wide research on the next generation mobile, that is, 6G is now already on the track. Outstanding players in 6G R&D initiatives include governments, industry and academia in Europe, China, US and Korea. In this presentation, we discuss the present and future societal issues, and service and technology trends that may drive the development of the future generation mobile. Then, our initial views of 6G vision are presented, describing user application scenarios, radio access use cases, and key performance indicators. From a radio access technology perspective, we envision 6G to be characterized by six usage components, namely, ultra broadband, ultra-precision positioning, ultra high reliability-low latency, ultra massive connectivity, ultra 3D coverage, and ultra low energy, each of which would be greatly enhanced by AI application and also reinforce AI application itself, providing networking capabilities going beyond 5G and extending user experience into new usage areas. For the enabling technologies, we address research areas such as THz radio access, 3D and satellite-based communication, enhanced URLLC networking and positioning etc.
 
Biography:

Dr. Young-Jo Ko is currently Director of 6G Wireless Technology Research Section in Mobile Communication Research Division at Electronics and Telecommunications Research Institute (ETRI), and serves as Chairman of the Technology Committee of 5G Forum in Korea. He received BS, MS and Ph. D. degrees in Physics from KAIST in 1992, 1994, and 1998, respectively. Since he joined ETRI in March, 1998, he has been working on mobile communications. He has also actively participated in the 3GPP standardization of LTE/LTE-Advanced. His current research interests include 5G New Radio and B5G/6G technologies, particularly focusing on the physical layer aspects of radio access

 

Invited Talk 9: “Satellite Communications in 5G and Beyond“
Prof. Jihwan Choi, DGIST, Korea

 
Abtract:
Satellites can provide global coverage and seamless service with minimal dependence on ground infrastructure. As the 3rd Generation Partnership Project (3GPP) is discussing the integration of satellite networks into 5G New Radio (NR) and the mega-constellation low-Earth orbit (LEO) satellites are being deployed, communication satellites are gaining big momentum for technical and economic success. This talk will present an overview of satellite communications, the state-of-the-art technologies of current satellite networks, and challenging issues for 5G and 6G satellite networks.
 
Biography:

Jihwan Choi (S’01-M’06-SM’17) received the B.S. degree in electrical engineering from Seoul National University, Seoul, Korea, and the S.M. and Ph.D. degrees in electrical engineering and computer science from Massachusetts Institute of Technology (MIT), Cambridge, MA. He is currently an Associate Professor at Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Korea. In 2006-2012, he was with Marvell Semiconductor Inc., Santa Clara, CA for 4G wireless modem design and standardization. In 2016-2017 he served as an ICT R&D Planner with Institute for ICT Planning and Evaluation (IITP), Korea for government R&D planning on communication satellites. In 2019 he visited Samsung SoC Lab, San Diego, CA for 5G non-terrestrial network (NTN) and deep learning R&D. He is currently an Associate Editor of IEEE Transactions on Aerospace and Electronic Systems and IEEE Access, and an Editorial Board Member of Remote Sensing. His research interests are in the cross-layer design of space and wireless networks, and the applications of machine learning and deep learning.

 

Special Session 4: Future Services and Their Enablers

10:30-12:10, October 22 (Thueday), 2020

Chair :
Time Invited Talk Title Invited Speakers
10:30-12:10 10 Now and Future in Medicine Prof. Young-Sung Lee
Chungbuk National University
11 Benefits and Risks of Sensing for IoT Security Prof. Jun Han
National University of Singapore, Singapore
12 Trend of V2X Communication Technology for Autonomous Vehicle Dr. Kitaeg Lim
Director, Korea Electronics Technology Institute (KETI), Korea

Invited Talk 10: “Now and Future in Medicine”
Prof. Young-Sung Lee, Chungbuk National University

 
Abtract:
South Korea possesses great potential to develop into a healthcare pioneer. As the world’s first to install a 5G wireless network, it has already built an excellent foundation for information infrastructure. However, these factors alone cannot revolutionize the medical system. If the South Korean government is more proactive and goal-driven for advanced healthcare, it is only a matter of time before South Korea creates a breakthrough in healthcare.
 
Biography:

He is the ex-president of National Evidence-based Healthcare Collaborating Agency(NECA). Also he is a professor of Health Informatics and Management at the College of Medicine, Chungbuk National University. He has contributed to development of the medical informatics in Korea over the last two decades. And he was a member of Committee on Infrastructure Technologies, National Science and Technology Council, the Nation’s highest decision making body on science and technology policies under the President of Republic of Korea

 

Invited Talk 11: “Benefits and Risks of Sensing for IoT Security”
Prof. Jun Han, National University of Singapore, Singapore

 
Abtract:
With the emergence of the Internet-of-Things (IoT), we witness many applications that enable devices to interact with the physical world via a large number of sensors and actuators. However, such interactions pose new challenges to traditional approaches of security and privacy. In this talk, I will present how my research group utilize sensor data may be utilized to provide security and privacy protections for IoT/CPS scenarios. In addition, I will emphasize on introducing several novel security threats arising from similar sensor data. Specifically, I will highlight some of our recent projects that leverage sensor data for attacks in various IoT applications including smart homes. I will also present an overview of ongoing projects in my research group.
 
Biography:

Jun Han is an Assistant Professor at the National University of Singapore with an appointment in the Department of Computer Science, School of Computing. His research interests lie at the intersection of sensing systems and security, and focuses on utilizing contextual information for security applications in the Internet-of-Things and Cyber-Physical Systems. He publishes across various research communities spanning security, sensing systems, and mobile computing (including S&P/Oakland, CCS, IPSN, TOSN, and HotMobile). He received his Ph.D. from the Electrical and Computer Engineering Department at Carnegie Mellon University as a member of Mobile, Embedded, and Wireless (MEWS) Group. He received his M.S. and B.S. degrees in Electrical and Computer Engineering also at Carnegie Mellon University. Jun also worked as a software engineer at Samsung Electronics.

 

Invited Talk 12: Trend of V2X Communication Technology for Autonomous Vehicle
Dr. Kitaeg Lim, Director, Korea Electronics Technology Institute (KETI), Korea

 
Abtract:
I briefly introduce various V2X communication technologies for autonomous vehicles, and explain the development trends and commercialization of each V2X communication technology.
-Overview of V2X communication technology
-Development of V2X communication technology
-Trend of commercialization of V2X communication technology
 
Biography:

February 2013 HANYANG UNIVERSITY
Ph.D. Candidate – Electronics and Computer Engineering
February 1996 HANYANG UNIVERSITY
Master of Science – Electronics Engineering

February 1994 HANYANG UNIVERSITY
Bachelor degree description – Department of Electronics

 

Special Session 5: Toward New Spectrum

08:30-10:10, October 23 (Friday), 2020

Chair :
Time Invited Talk Title Invited Speakers
08:30-10:10 13 Cross-technology Communication: A Protocol Stack View Prof. Xiaolong Zheng
Beijing University of Posts and Telecommunications (BUPT), China
14 Sub-THz RF Component and System Design Considerations for Wireless Backhaul Links Dr. Seok-Bong Hyun
Director, Communication RF Research Section, ETRI, Korea
15 New Opportunities and Challenges in Unlicensed Spectrum Dr. Jin Sam Kwak
CEO, WILUS Inc., Korea

Invited Talk 13: “Cross-technology Communication: A Protocol Stack View”
Prof. Xiaolong Zheng, Beijing University of Posts and Telecommunications (BUPT), China

 
Abtract:
The proliferation of Internet of Things (IoT) applications calls for ubiquitous connections among various IoT devices (things). Interconnecting the heterogeneous devices that operate in the shared medium is a crucial but challenging task, because different technologies are essentially incompatible with each other. Emerging Cross-technology Communcition (CTC) is a promissing technique to enable the direct communcation between heterogenous IoT devices. In this talk, I will present our recent progress on this topic, from a bottom-up view of the protocol stack. First, I will introduce two work in PHY layer of CTC, including ZigFi (IEEE INFOCOM’18) that leverage Channel State Information to eanble CTC from ZigBee to WiFi and c-Chirp (IEEE SECON’20) that tries to establish symmetric CTC over the intrinsic asymmetric CTC channels. Then I will talk about our work in link layer, C-LQI (IEEE INFOCOM’20) that estimates the CTC link quality. Finally, I will present our recent work Portal (ACM Mobihoc’20) that ultilizes CTC in network layer. I will also discuss my personal understanding and vision on the research trends of CTC.
 
Biography:

Dr. Xiaolong Zheng is an Associate Professor with the School of Computer Science and Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, China. Before this, he was a Postdoctoral Research Associate with the School of Software, Tsinghua University, China. Dr. Zheng received his Ph.D. degree in the Department of Computer Science and Engineering from Hong Kong University of Science and Technology in 2015, and his B.E. degree from the Dalian University of Technology in 2011. Dr. Zheng’s research interests include IoT, wireless networking, and ubiquitous computing. He has published over 40 research papers in top-tier journals and conferences, including IEEE/ACM Transactions on Networking, IEEE Transactions on Mobile Computing, IEEE Transactions on Computer, IEEE INFOCOM, ACM Mobihoc, ACM SenSys. He is the recipient of the Best Paper Runner-up Award of IEEE SECON 2020 and the Best Student Paper Award of IEEE ICPADS 2017. He was also awarded as Young Elite Scientist Sponsorship Program of China Association for Science and Technology.

 

Invited Talk 14: “Sub-THz RF Component and System Design Considerations for Wireless Backhaul Links”
Dr. Seok-Bong Hyun, Director, Communication RF Research Section, ETRI, Korea

 
Abtract:
In this session, we present a sub-THz (100~300GHz) RF system design and measurement result including channel characteristics for future wireless backhaul links. Design issues of terahertz band RF components such as high-gain antenna, power amplifier and ADC data converters are provided. We describe a prototype system comprised of those components, channel modelling, and measured performance of wireless data transmission at 260-GHz band.
 
Biography:

Seok-Bong Hyun received the BS, MS, and PhD degrees in physics from the Korea Advanced Institute of Science and Technology (KAIST) in 1991, 1993, and 1998, respectively. In 1999 he joined Electronics and Telecommunications Research Institute (ETRI), Korea, where he has been involved in the design of low-power RF and analog integrated circuits for short-range wireless communications. His research interests include the design of millimeter waves and very high-speed wireless network system.

 

Invited Talk 15: : “New Opportunities and Challenges in Unlicensed Spectrum”
Dr. Jin Sam Kwak, CEO, WILUS Inc., Korea

 
Abtract:
Due to the ever-increasing demand for unlicensed spectrum, in April 2020, the FCC finally adopted rules that make 1,200 MHz of spectrum in the 6GHz band (5.925-7.125 GHz) available for unlicensed spectrum. Opening the 6GHz band for unlicensed use has promoted the 5G NR and Wi-Fi standards development to take the initiative of the new spectrum. In this talk, the relevant key standards in 3GPP and IEEE 802.11 are introduced – especially, 5G NR in unlicensed (NR-U), IEEE 802.11ax (Wi-Fi 6), and next generation of Wi-Fi (IEEE 802.11be EHT). We summarize the core PHY/MAC technologies, which enabling the multi-RAT coexistence, bandwidth-intensive and/or latency-sensitive applications. Mainly, we focus on the licensed assisted access/standalone 5G NR-based access and next generation Wi-Fi supporting multi-band/link/AP features in the unlicensed bands. Future works to evolve the standards in 3GPP & IEEE 802.11 are also discussed.
 
Biography:

Dr. JIN SAM KWAK received his B.S., M.S., and Ph.D. degrees in Electrical Engineering and Computer Science from Seoul National University, Seoul, Korea, in 1998, 2000, and 2004, respectively. From 2004 to 2005, he was a postdoctoral research associate in the School of Electrical and Computer Engineering, Georgia Institute of Technology. During 2006, he was also with the Wireless Networks and Communications Group (WNCG), the University of Texas at Austin as a post-doctoral research fellow. From 2007 to 2012, he was with LG Electronics as a chief research engineer. During this time, he carried out research tasks on the IMT-Advanced and also led the standards activities for wireless communications in IEEE 802 (especially, IEEE 802.11/15/16/19), Wi-Fi Alliance (served as an alternative board member), and WiMAX Forum. Since 2013, he has been with WILUS Institute of Standards and Technology, INC, where he is currently CEO and co-founder. His main research interests focus on advanced & enabling technologies for next generation wireless communications & immersive multimedia coding standards including 3GPP 5G NR, IEEE 802.11, MPEG.

 

Special Session 6: Softwarized Radio

10:30-12:10, October 23 (Friday), 2020

Chair :
Time Invited Talk Title Invited Speakers
10:30-12:10 16 미정 Mr. Valeriy Cherepennikov
Director for Intelligent Computing, Huawei Russia Lab, Russia
17 Commercializing Software Defined User Equipment for Beyond 5G Systems Prof. Juyeop Kim
Sookmyung Women’s University, Korea
18 5G SDR Prototype Based on MATLAB and QualNet Dr. Kyeongjun Ko
Senior Researcher, Korea Railroad Research Institute (KRRI), Korea

Invited Talk 16: ”미정”
Mr. Valeriy Cherepennikov, Director for Intelligent Computing, Huawei Russia Lab, Russia

 
Abtract:
”미정”
 
Biography:
”미정”
 

Invited Talk 17: “Commercializing Software Defined User Equipment for Beyond 5G Systems”
Prof. Juyeop Kim, Sookmyung Women’s University, Korea

 
Abtract:
Software-Defined Radio (SDR) technology has been conceptualized and realized for past 20 years, but has failed to be applied to commercial markets. However, the SDR technology becomes to be valued as 5G services consider communications in new industrial environment and requires various types of UEs. In this talk, I’ll review the history of SDR technology development and introduce which factors are needed for the SDR technology to be applied to commercial 5G UEs. I also introduce recent research results which can contribute to make the SDR technology’s debut in commercial markets.
 
Biography:

Juyeop Kim is an assistant professor in the Department of Electronics Engineering, Sookmyung Women’s University, Seoul, South Korea. He received his M.S. and Ph.D. in electrical engineering and computer science from KAIST in 2010. He has involved to the commercialization projects for developing 2G/3G/LTE multimode modem chip solutions in Samsung Electronics. He has also involved to national R&D projects for applying LTE to railway and public safety in Korea Railroad Research Institute (KRRI). His current research interests is the applied wireless communications including mission critical communications, internet of things and software-defined 5G baseband processing.

 

Invited Talk 18: “5G SDR Prototype Based on MATLAB and QualNet”
Dr. Kyeongjun Ko, Senior Researcher, Korea Railroad Research Institute (KRRI), Korea

 
Abtract:
The existing communication systems have mainly been based on hardware such as ASIC, but they have some big problems with respect to development difficulty such as large development cost and low flexibility. In order to overcome these problems, Software Defined Radio (SDR) prototypes have actively studied recently, which have lower development and higher flexibility compared to prototypes based on hardware. However, most of SDRs are very insufficient yet in terms of completeness, which are usually realized only partial functions in total systems. In these current conditions, we need to development communication prototypes which is as similar as possible with real communication systems. In this talk, we propose a 5G SDR prototype using MATLAB (PHY) and QualNet (High layers).
 
Biography:

Kyeongjun Ko received the B.S. and Ph.D. degrees in electrical engineering from Seoul National University, Seoul, Korea, in 2006 and 2012. He also was a postdoctoral researcher from 2012 to 2013 in Wireless Signal Processing Lab in Seoul National University. Then, he has worked in Korea Railroad Research Institute as a Senior Researcher since 2013. His current research interests include wireless communication system with deep learning, positioning, mission critical communications