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5G rollouts have stimulated new demand that cannot be met by 5G itself. That's where 5G-Advanced comes into play, delivering enhanced capabilities. Without a doubt, 5G-Advanced will further stimulate more new demands that only 6G can address. Looking into these new demands will be crucial to defining 6G. ITU-R is leading the consortium effort to study future technology trend (FTT) and 6G vision, aiming to issue the FTT report and vision recommendation by the end of 2022 and in the middle of 2023, respectively. 6G will go far beyond communications. 6G will serve as a distributed neural network that provides communication links to fuse the physical, cyber, and biological worlds, truly ushering in an era in which everything will be sensed, connected, and intelligent. In addition to connected people and things, we predict that 6G will be the platform for connected intelligence, where the mobile network connects vast amounts of intelligent devices and connects them intelligently. This talk will first start with 5G-advanced as an introduction, then present an overall vision for 6G with drivers, use cases, KPIs, roadmap and key capabilities. Six key capabilities: (1) Extreme connectivity, (2) Native AI, (3) Networked sensing, (4) Integrated Non-terrestrial network, (5) Native trustworthiness and (6) Sustainability, will be further discussed, including potential technologies/research directions and associated challenges.
TCP/IP is not secure, a fundamental change is required. One owner environments (VPN and firewalls) do not support shared operations and devices. This presentation will examine the fundamental weaknesses of TCP/IP and why can we not fix the existing infrastructure. Also, what will be the protocol requirements for TCP/IP replacement taking into account security and efficiency considerations and how digital rights can be defined, managed and protected.
Barely seen in action movies until a decade ago, the progressive blending of UAVs into our daily lives will greatly impact labor and leisure activities alike. Most stakeholders regard reliable connectivity as a must-have for the UAV ecosystem to thrive, and the wireless research community has been rolling up its sleeves to drive a native and long-lasting support for UAVs in 5G and beyond. Moving up, the recent introduction of more affordable insertions into the low orbit is luring new players to the space race, making a marriage between the satellite and cellular industries more likely than ever. In this talk, we will navigate from 5G to 6G use cases, requirements, and enablers involving aerial and spaceborne communications, also acting as a catalyst for much-needed new research.
Exploiting the frequency ranges above 6 GHz has become a hallmark of modern wireless systems. The use of 20-100 GHz spectrum was a key characteristic of 5G systems, and the 100-500 GHz frequency range will be an important component in 6G. This talk will first discuss the characteristics of wireless propagation channels in those frequency bands, reviewing the fundamentals, and then discussing our recent measurement results in outdoor environments, including ones in the larger than 100 GHz frequency range that show feasibility of high-rate data links at distances up to 100 m in both line-of-sight and many non-line-of-sight situations; yet at the same time these measurements also indicate that many common assumptions about such high-frequency channels, e.g., with respect to sparsity, might not hold under all circumstances. Based on the discussions of the channels, the talk will then investigate single- and multi-user capacity, signaling methods and transceiver structures that are especially suitable for ultra-high data rates at these high frequency bands.
Research activities in academia and industry worldwide towards the 6th generation (6G) mobile communication system have recently considerably gained momentum. In this overview we will highlight the anticipated 6G timeline and technology concepts which have to fulfil even more stringent requirements in comparison to 5G, such as ultra-high data rates, energy efficiency, global coverage and connectivity as well as extremely high reliability and low latency. One of the 6G technologies are sub-Terahertz and terahertz (THz) waves which have frequencies extending from 0.1 THz up to 10 THz and fall in the spectral region between microwave and optical waves. The prospect of offering large contiguous frequency bands to meet the demand for highest data transfer rates up to the terabit/sec range make it a key research area of 6G mobile communication. These efforts require an interdisciplinary approach, with close interaction of high-frequency semiconductor technology for RF electronics but also including alternative approaches using photonic technologies. The THz region also shows great promise for many applications areas ranging from imaging to spectroscopy and sensing. To fully exploit the potential of this frequency range it is also crucial to understand the propagation characteristics for the development of the future communication standards by performing channel measurements. We will highlight the characteristics of channel propagation in this frequency region and present new results from channel measurements at 158 GHz and 300 GHz.
Sitting at the intersection of wireless communication and ML, the talk will focus on two important aspects of wireless edge AI. First, we will discuss and demonstrate the application of ML in wireless communication for understanding, orchestrating, securing and maximizing the use of spectrum resources through learning. ML techniques can provide significant leaps in performance and efficiency of key L1 functions surrounding channel sensing, channel modeling, modulation and receiver design, and spatial re-use, as well as improving access and coordination schemes. We will explore how some of these ideas are advancing the 5G RAN today and how they can evolve to enable 6G.Second, we describe the role of Distributed Edge AI in the wireless environment. Owing to the distributed nature of data arising from sensors, base stations, and so forth, the goal in edge AI is to train privacy-preserving machine learning models under resource constraints. We provide an overview of recent techniques such as federated learning, distillation and split learning. We will also explore how to harness over-the-air computing and analog communication to provide scalable and privacy-preserving over-the-air model training. The talk will conclude by shedding light onto the next frontier of edge AI sitting at the confluence of semantic communication and ML.
This session will discuss the evolution of radio access network to open and virtualized cloud native RAN. It will focus on the RAN networks today for 5G, current ecosystem landscape, emerging trends in this space for 5G and beyond. It will also talk about the opportunities and challenges as well as how our network will become scalable specifically in the context of virtualization. The session with discussion some of evolving trends towards 6G and how cloud native technologies and ubiquitous computing will play a crucial role going forward. Why is this topic important: Analysts are getting more bullish on ORAN/vRAN – Dell ORO recently increased the ORAN/vRAN adoption from 10 to 14% by 2025. There are lot of innovations and investments being done in both hardware and software associated with ORAN/VRAN. Multiple partnerships/consortiums are being formed across the RAN ecosystem. The usage will also extend from Macro network to enterprise & IOT networks as well. What industry challenges are you addressing / solving? This will address dynamic scalability of a network and availability of multiple ecosystem options that will deliver TCO benefits for the end customer.
Spectrum regulation challenges grow for both unlicensed (e.g., Wi-Fi) and licensed (e.g., cellular) opportunities, particularly those created by 10's to 100's of billions of connected, communicating devices. Dynamic, cognitive solutions just begin to find field use and initiate the inevitable march towards increasingly artificially intelligent allocation of spectra and space. This talk reviews some multiuser fundamentals, their complexity of solution, and how they may find future application to magnify spectral efficiency by orders of magnitude.
Today, channel codes are among the fundamental parts of any communication system, including cellular, WiFi, and deep space, among others, enabling reliable communications in the presence of noise. Decades of research have led to breakthrough inventions of various families of channel codes. Yet no unified approach exists in answering these two fundamental questions: Given a channel, how do we efficiently construct the best possible code? And given a channel code, how do we design an efficient and optimal decoder? In this talk, we will discuss how the remarkable advancements in data-driven machine learning (ML) can be leveraged toward answering these questions. In particular, we will focus on a class of codes rooting in Plotkin recursive construction. This class includes Reed–Muller (RM) codes as the state-of-the art binary algebraic codes, as well as polar codes, the first capacity-achieving codes with explicit, i.e., non-randomized, constructions. In the first part of this talk, we will present an efficient and close-to-optimal decoder obtained for RM codes by learning a pruning process applied to an exponentially complex decoder. In the second part, we will tackle the fundamental problem of designing new channel codes. In particular, we will demonstrate KO codes, a new class of channel codes designed by training neural networks while preserving Plotkin-like structures. KO codes beat both of their RM and polar code counterparts, under the successive cancellation decoding, in the challenging short-to-medium blocklength regime. We will also discuss various challenges that should be overcome to pave the way for adopting such ML-aided channel coding strategies in practice.
Edge devices collect massive amounts of data, opening up new potentials for machine learning applications. Machine learning at the edge can benefit from exploiting both data and processing power distributed across many wireless devices, but this brings about many new challenges including the low latency requirements of learning applications, privacy concerns preventing data sharing, and the impact of noise and interference on the convergence of the learning process. Overcoming these challenges while meeting the requirements of the machine learning tasks calls for a new paradigm of semantic-oriented communication network design tailored for learning applications. In this talk, I will present recent results on efficient distributed inference and training over wireless networks taking into account channel impairments and power and bandwidth limitations of wireless devices, as well as the semantics of the underlying learning tasks. This will involve bringing together novel communication and coding techniques with distributed learning and inference algorithms.
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.
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.