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As Wi-Fi "strikes again" with 802.11be, this forum will host a discussion on its evolution, the ongoing 802.11be standardization, the opportunities created by the progressive adoption of the 6 GHz spectrum, and the increased interest in supporting not only higher capacity but also reliable and low latency applications using Wi-Fi. Experts from industry and academia will share their experience in driving standard and product development, spectrum and technology regulations, and research visions.
A burgeoning second quantum revolution promises powerful applications of quantum mechanical phenomena discovered and understood throughout the last century. While the biggest impacts seem confined to an undetermined future time frame, some quantum technologies are achieving maturation. We ask a panel of experts about the current and near-term applications of quantum technologies in information and sensing.
This industry keynote is on Modern AI Meets Cell Phone Network Optimization. Bio: Gregory Dudek is a Professor with the School of Computer Science and a member of the McGill Research Centre for Intelligent Machines (CIM) and an Associate member of the Dept. of Electrical Engineering at McGill University. In 9/2008 he became the Director of the McGill School of Computer Science. Since 2012 he has been the Scientific Director of the NSERC Canadian Field Robotics Network (NCFRN): http://ncfrn.mcgill.ca He is the former Director of McGill's Research Center for Intelligent Machines, a 25 year old inter-faculty research facility. In 2002 he was named a William Dawson Scholar. In 2008 he was made James McGill Chair. In 2010 he was awarded the Fessenden Professorship in Science Innovation. In 2010 he was also awarded the Canadian Image Processing and Pattern Recognition Award for Research Excellence and also for Service to the Research Community. He directs the McGill Mobile Robotics Laboratory. He has been on the organizing and/or program committees of Robotics: Systems and Science, the IEEE International Conference on Robotics and Automation (ICRA), the IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS), the International Joint Conference on Artificial Intelligence (IJCAI), Computer and Robot Vision, IEEE International Conference on Mechatronics and International Conference on Hands-on Intelligent Mechatronics and Automation among other bodies. He is president of CIPPRS, the Canadian Information Processing and Pattern Recognition Society, an ICPR national affiliate. He was on leave in 2000-2001 as Visiting Associate Professor at the Department of Computer Science at Stanford University and at Xerox Palo Alto Research Center (PARC). During his sabbatical in 2007-2008 he visited the Massachusetts Institute of technology and co-founded the company Independent Robotics Inc. He obtained his PhD in computer science (computational vision) from the University of Toronto, his MSc in computer science (systems) at the University of Toronto and his BSc in computer science and physics at Queen's University. He has published over 200 research papers on subjects including visual object description and recognition, robotic navigation and map construction, distributed system design and biological perception. This includes a book entitled "Computational Principles of Mobile Robotics" co-authored with Michael Jenkin and published by Cambridge University Press. He has chaired and been otherwise involved in numerous national and international conferences and professional activities concerned with Robotics, Machine Sensing and Computer Vision. His research interests include perception for mobile robotics, navigation and position estimation, environment and shape modelling, computational vision and collaborative filtering. He grew up in Montreal and favors light food. With his children he is re-discovering model rocketry, rollerblading, and has discovered he's not good at surfing but loves it.
There will be two industry panel sessions to facilitate discussion about 6G, i.e., Part 1 entitled “6G Use Cases, Requirements, and Roadmap” and Part 2 entitled “The Road to 6G - Key Technology Enablers and Their Impact on 6G Architecture”. This proposed panel is for Part 1 and its discussion topics will focus on technical and social trends that would motivate further evolution beyond 5G, representative use cases of 6G, and initial views about vision, requirements, and roadmap of standardization and commercialization for 6G. Considering that the mobile industry will continue the enhancement of 5G networks for about 10 years before the start of deploying 6G networks, it would also be worth discussing how to define the relationship between 5G evolution and 6G. In this proposed panel, we will bring together leading experts from the mobile industry as well as the academia. The proposed panel can serve as a good opportunity to share the technology leaders’ views and can provide a bridge between academia and industry.
This workshop aims at bringing together academic and industrial researchers in an effort to identify and discuss the major technical challenges, recent breakthroughs, and new applications related to OTFS.
COVI-COM intends to leverage technological advancements and techniques in communications and AI to address disruptive, as well as regular challenges arising due to the global COVID-19 pandemic. The present-day world urgently needs resilient and sustainable solutions that can address the challenges arising out of the mandated need for periodic sanitization, intermittent quarantines and lockdowns, and social distancing. This workshop aims to mobilize the global communications and networking community for enabling long-term solutions for alleviating the social and economic constraints put in place to battle COVID-19 infections and arrest its spread. The impact of these solutions are projected to be long-term as with no definite cure or treatment in sight, human society is expected to adapt to the new social norms of intermittent lockdowns, quarantine, and social distancing. This workshop encourages the use of machine learning techniques, data mining, network science, communication technologies, and other similar technologies to counter the challenges arising out of the present COVID-19 pandemic.
As the number of communication devices and the data demands are growing at an exponential rate, awareness of the value of wireless communication gadgets has also tremendously increased. The need for higher agricultural productivity, industrial automation, clean air and clean water, convenient and safe city life, city as well as border surveillance are some burning aspects that call for deployment of multitudes of IoT devices that can automatically collect information and actuate desired control actions.Realization of large-scale deployment and affordable usage hinge upon energy-sustainable operation of these devices. Fast-paced global warming further calls for solutions that will take the mankind to more technology advancement without adversely impacting the environment. It is also possible that, smart usage of the IoT technologies could even aid in reversing the global warming process.In this framework, the SAGE workshop aims to draw together researchers and practitioners engaged in the progress and continued endeavors on such green and energy-sustainable technology solutions. It thus focuses on the energy sustainability aspects of IoT and, in general, on machine-type communications, actuation, and control automation in smart environments, which is a major theme also of 5G+ and 6G technologies. Beyond theoretical proposals, the interest is on technology viability of green and energy-sustainable communication solutions ranging from low-rate telemetric communications to highly-reliable, ultra-low latency, and bandwidth-intensive communications
Artificial intelligence (AI) and big data are both viewed as the cornerstone to build beyond-5G (B5G) zero-touch automated wireless networks. To harness the full potential of automation, AI algorithms should be driven by the distributed nature of datasets across the network. This distribution is sometimes due to the network topology itself, where performance data collection is performed per domain or node (e.g., radio access, edge cloud) but also produced by the applications running on scattered user devices. In such a case, opting for a centralized data collection system would result in high network bandwidth and energy consumption as well as a significant delay to transfer the data to the classical operational subsystem (OSS). The centralization would also breach the privacy and security of end-user applications. In this context, standardization efforts have been made to decentralize AI algorithms. In ETSI’s zero-touch architecture, for instance, each network domain is endowed with a data collection element that feeds a local AI analytics and decision entity. The central entity plays only the role of a coordinator/model aggregator without having access to the distributed raw datasets. A successful AI deployment should therefore be distributed in space-ranging from user devices to core network-and evolving in time-from collaborative AI to advanced federated learning. In this intent, active research works have been carried out to come up with efficient distributed AI architectures. The main challenges faced by researchers reside in the cost incurred due to the bidirectional communication between the locally trained models and the global one. This cost is indeed determined by the number of iterations until convergence as well as the underlying energy consumption per channel use. Additionally, deploying AI at edge devices would require the adoption of low-complexity models intended to run on optimized dedicated hardware to preserve battery lifetime. A decentralized solution with complex models is therefore not viable. Decentralized AI has multi-fold use cases. User devices with dedicated AI chips might benefit from a higher degree of security and privacy since they would prevent the exchange of any raw data with centralized cloud servers. They might also present a quick reaction time with locally taken decisions, which is adequate for low-latency applications as well as for mitigating security risks. On the other hand, the density of network nodes or the exponential increase in user devices would induce no significant complexity since network intelligence is scattered among a massive number of nodes and user equipments offering thereby a high degree of scalability.
Unmanned aerial vehicles (UAVs) have found fast growing applications during the past few years. As such, it is imperative to develop innovative communication technologies for supporting reliable UAV command and control (C&C), as well as mission-related payload communication. However, traditional UAV systems mainly rely on the simple direct communication between the UAV and the ground pilot over unlicensed spectrum (e.g., ISM 2.4GHz), which is typically of low data rate, unreliable, insecure, vulnerable to interference, difficult to legitimately monitor and manage, and can only operate within the visual line of sight (LoS) range. To overcome the above limitations, there has been significant interest in integrating UAVs into cellular communication systems. On the one hand, UAVs with their own missions could be connected into cellular networks as new aerial users. Thanks to the advanced cellular technologies and almost ubiquitous accessibility of cellular networks, cellular-connected UAVs are expected to achieve orders-of-magnitude performance improvement over the existing point-to-point UAV communications. It also offers an effective option to strengthen the legitimate UAV monitoring and management, and achieve more robust UAV navigation by utilizing cellular signals as a complement to GPS (Global Position System). On the other hand, dedicated UAVs could be deployed as aerial base stations (BSs), access points (APs), or relays, to assist terrestrial wireless communications from the sky, leading to another paradigm known as UAV-assisted communications. UAV-assisted communications have several promising advantages, such as the ability to facilitate on-demand deployment, high flexibility in network reconfiguration, high chance of having LoS communication links, and enable numerous applications such as BS traffic offloading, information dissemination and collection for Internet of Things (IoTs). UAV communications are significantly different from conventional communication systems, due to the high altitude and high mobility of UAVs, the unique channel of UAV-ground links, the asymmetric quality of service (QoS) requirements for downlink C&C and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the additional design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.
Artificial intelligence (AI) and big data are both viewed as the cornerstone to build beyond-5G (B5G) zero-touch automated wireless networks. To harness the full potential of automation, AI algorithms should be driven by the distributed nature of datasets across the network. This distribution is sometimes due to the network topology itself, where performance data collection is performed per domain or node (e.g., radio access, edge cloud) but also produced by the applications running on scattered user devices. In such a case, opting for a centralized data collection system would result in high network bandwidth and energy consumption as well as a significant delay to transfer the data to the classical operational subsystem (OSS). The centralization would also breach the privacy and security of end-user applications. In this context, standardization efforts have been made to decentralize AI algorithms. In ETSI’s zero-touch architecture, for instance, each network domain is endowed with a data collection element that feeds a local AI analytics and decision entity. The central entity plays only the role of a coordinator/model aggregator without having access to the distributed raw datasets. A successful AI deployment should therefore be distributed in space-ranging from user devices to core network-and evolving in time-from collaborative AI to advanced federated learning. In this intent, active research works have been carried out to come up with efficient distributed AI architectures. The main challenges faced by researchers reside in the cost incurred due to the bidirectional communication between the locally trained models and the global one. This cost is indeed determined by the number of iterations until convergence as well as the underlying energy consumption per channel use. Additionally, deploying AI at edge devices would require the adoption of low-complexity models intended to run on optimized dedicated hardware to preserve battery lifetime. A decentralized solution with complex models is therefore not viable. Decentralized AI has multi-fold use cases. User devices with dedicated AI chips might benefit from a higher degree of security and privacy since they would prevent the exchange of any raw data with centralized cloud servers. They might also present a quick reaction time with locally taken decisions, which is adequate for low-latency applications as well as for mitigating security risks. On the other hand, the density of network nodes or the exponential increase in user devices would induce no significant complexity since network intelligence is scattered among a massive number of nodes and user equipments offering thereby a high degree of scalability.
KEYNOTE 1: DISTRIBUTED MACHINE LEARNING AT THE WIRELESS EDGE SPEAKER: PROF. DENIZ GÜNDÜZ IMPERIAL COLLEGE LONDON, UK Abstract: IoT devices collect significant amount of data at the wireless edge, opening up new potentials for machine learning applications. Current approach to edge intelligence is to offload all the collected data to a cloud server for central processing. This approach is not sustainable considering the expected growth in the number of IoT devices and the traffic they generate. Moreover, it creates significant privacy risks for the users, and introduces delays that cannot be tolerated by most applications. The alternative is to bring the intelligence to the edge, by distributing both the training and the inference tasks across edge devices and servers. In this talk, I will present recent results on efficient distributed inference and training over wireless channels taking into account channel impairments as well as power and bandwidth limitations of wireless devices. This will involve bringing together novel communication and coding techniques with distributed learning algorithms. SPEAKER: JULIEN FORGEAT, ARTIFICIAL INTELLIGENCE, ERICSSON RESEARCH Bio: Julien Forgeat is an artificial intelligence principal researcher at Ericsson Research. He joined Ericsson in 2010 after spending several years working on network analysis and optimization. He holds an M.Eng. in computer science from the National Institute of Applied Sciences in Lyon, France. At Ericsson, Julien has worked on mobile learning, Internet of Things and big data analytics before specializing in machine learning and AI infrastructure. His current research focuses on the software components required to run AI and machine learning workloads on distributed infrastructures as well as the algorithmic approaches that are best suited for complex distributed and decentralized use-cases.
The new generation of Internet of Things involves Internet of Mobile Things (IoMT) which lets increasingly moving objects make better operational decisions through pooling data and resources from other connected vehicles and devices. Due to the enormous research and commercial potential, a lot of companies and researchers are attracted to this area. This workshop aims to bring researchers working on Future IoMTs under one roof to discuss the implementation, applications, and possible standardization efforts. We expect that the authors can together bring about significant impacts within this domain and share their knowledge and experiences with members of the research community, commercial sector and wider audiences.
This workshop is dedicated to the theory and applications of general and powerful transmission frameworks based on Rate-Splitting Multiple Access (RSMA). RSMA consists in decoding part of the interference and in treating the remaining part of the interference as noise. This enables RSMA to softly bridge and therefore reconcile the two extreme strategies of fully decode interference and treat interference as noise and provide room for spectral efficiency, energy efficiency and QoS enhancements in a wide range of network loads and user deployments, robustness against imperfect Channel State Information at the Transmitter (CSIT), as well as feedback overhead and complexity reduction.
In this panel we will explore Digital Human as a new form of media and future way of representing digitally human beings in all its forms (creation, editing, identity, image right, standard & format, social impact…)
This academic keynote is on Perspectives On Innovations and Reality - What Next? PANELISTS: Jonathan Davidson JONATHAN DAVIDSON Cisco Bio: Jonathan was named Senior Vice President and General Manager of Cisco's Mass-Scale Infrastructure Group in March 2020. He leads an organization that builds silicon, optics, hardware, software, and systems innovations for the largest and most advanced networks in the world. Prior to this role, Jonathan was named Senior Vice President and General Manager of Cisco's Service Provider Business in August 2018. He led the team to deliver industry leading technologies for the Internet and 5G (routing systems, IOS XR software, automation, and solutions for fixed, cable, and mobile providers). Jonathan re-joined Cisco in March 2017 as Sr. Vice President and General Manager of Service Provider Networking. In that role, he drove Cisco's leadership position in next-generation routing and network automation. Prior to rejoining Cisco, Jonathan served as Executive Vice President and General Manager at Juniper Networks leading its Engineering and Product Management. In that role, he was responsible for driving strategy, development and business growth for the company's entire portfolio including routing, switching and security, as well as leading the ongoing evolution of silicon technology and the Junos operation system. Before Juniper, Jonathan held a variety of leadership positions at Cisco over the course of 15 years. During that time, he developed service provider solutions and led the enterprise routing product management team and service provider Layer 4 through Layer 7 services team. Jonathan is co-author of the best-selling book, "Voice-over IP Fundamentals," and is a frequent speaker at high-profile industry events. Active on social media, he frequently shares his observations and insights about the industry through Twitter and blogs.Ibrahim GedeonIBRAHIM GEDEON CTO, TELUS Bio: Ibrahim Gedeon is one of the global telecommunications industry’s eminent thought leaders. He has carved out an international career by combining insight and skill as an applied scientist with a lighthearted approach to leadership. As Chief Technology Officer for TELUS, a leading national telecommunications company in Canada, he is responsible for all technology development and strategy, security, service and network architecture, service delivery and operational support systems, as well as service and network convergence, and network infrastructure strategies and evolution. Under his leadership the TELUS wireless broadband network has become one of the best in the world. Ibrahim serves on the board of the Next Generation Mobile Networks Alliance, the Alliance for Telecommunications Industry Solutions and the Institute for Communication Technology Management. In addition to his industry leadership roles, he has been awarded with IEEE Communications Society’s prestigious Distinguished Industry Leader Award and elected a Fellow of the Canadian Academy of Engineering (CAE) for his significant contributions to the field of engineering. Ibrahim has also been named one of the 100 most powerful and influential people in the telecoms industry in Global Telecoms Business magazine’s GTB Power 100. Ibrahim holds a Bachelor's degree in Electrical Engineering from the American University of Beirut, a Master’s in Electronics Engineering from Carleton University and an Honourary Doctor of Laws degree from the University of British Columbia and is passionate about supporting engaged, high-performing teams.