IEEE Global Communications Conference
8–10 December 2020 // In-person (Taipei, Taiwan)
7-11 December 2020 // Virtual
Communications for Human and Machine Intelligence

Tutorials

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TECHNICAL TUTORIALS

ON-DEMAND ACCESS STARTING AT 09:00 MONDAY, 7 DECEMBER (LOCAL TIME IN
TAIPEI). Q&A WITH PRESENTERS ON FRIDAY, 11 DECEMBER

09:00 – 17:30 (Local time in Taipei)

TUT 01: 5G Security and Privacy:Issues, Potential Solutions and Future Directions
TUT 02: Age of Information: Theory, Applications, and Testbed Implementation
TUT 03: AoI, QoE, and Beyond: Optimizing Emerging Network Metrics through Second-Order Analysis
TUT 04: Beyond Massive MIMO: User-Centric Cell-Free Massive MIMO
TUT 05: Deep Learning for Wireless Communications
TUT 06: Distributed Deep Learning: Concepts, Methods & Applications in Wireless Networks
TUT 07: Distributed Machine Learning over Wireless Networks: Challenges and Opportunities
TUT 08: Establishing Trust in the Air: Wireless Communications and Sensing Approaches for Safe UAVs
TUT 09: Federated Learning at the Network Edge: Fundamentals, Key Technologies, and Future Trends
TUT 10: Introducing IEEE 802.11be - The Wi-Fi of the Future
TUT 11: Machine Learning-Enabled and Ultra-Low Latency Connected Transportation
TUT 12: Massive Machine-Type Communications for IoT: Recent Progress and Future Directions
TUT 13: Millimeterwave and Terahertz Propagation Channels - Measurement, Modeling, and Simulation
TUT 14: New IP: Rethinking a Future Internet with New Service Capabilities
TUT 15: Non-Terrestrial Networks (NTN): The Next 20 Years
TUT 16: Quantum Communications - A Glimpse Beyond Moore's Law
TUT 17: Reconfigurable Metasurfaces for Intelligent 6G Wireless Networking
TUT 18: Scalable, Optimal, Yet Explainable Gaussian Process Models for Data-Driven Wireless Systems
TUT 19: Softwarization and Virtualization in 5G and Beyond Mobile Networks
TUT 20: Spectrum Sharing for Inter-Technology Coexistence
TUT 21: Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network
TUT 22: Ultra-Reliable and Low-Latency Communications for Industry 4.0: Industrial and Academic Perspectives
TUT 23: What Physical Layer Security Can Do for 6G
TUT 24: Wireless Channel Charting for Massive MIMO


MONDAY, 7 DECEMBER 2020, 09:00-17:30 (Local time in Taipei)

(on-demand access starting at 09:00 on Monday, 7 December (local time in Taipei.) Q&A with presenters on Friday, 11 December)

 

TUT 01: 5G Security and Privacy:Issues, Potential Solutions and Future Directions

Presenters: Madhusanka Liyanage (University College Dublin, Ireland & University of Oulu, Finland)

Abstract: In present digital societies, telecommunication networks provide connectivity, which is crucial for the operation and management of many other critical infrastructures and services such as healthcare, industrial operations and public safety. This situation is expected to be exacerbated with the incorporation of the Fifth Generation (5G) wireless services such as enhanced mobile broadband (eMBB), ultra-reliable low latency communication (URLLC), and massive machine-type communications (mMTC). Security and Privacy have become the primary concern in 5G and Beyond 5G (B5G) network as risks can have high consequences. Thus, this tutorial will explains the potential security attacks and breaches of privacy that the emerging 5G networking paradigm is facing. It will present a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Moreover, tutorial will explain the possible ways of developing novel security and privacy solutions to protect the 5G telecommunication networks to strengthen critical infrastructures. Finally, a future directions and open challenges will be discussed to encourage future research.

 

TUT 02: Age of Information: Theory, Applications, and Testbed Implementation

Presenters: Nikolaos Pappas (Linköping University, Sweden); He Chen (The Chinese University of Hong Kong, Hong Kong)

Abstract: This tutorial aims to present recent efforts on the analysis, optimization, applications, and testbeds of the age of information (AoI) metric for quantifying and evaluating the information freshness in wireless IoT networks. In addition, we will provide comprehensive coverage including the definition and promising applications of AoI, queueing theory-based AoI analysis, AoI for energy harvesting wireless networks, age-oriented multiuser scheduling in single-/multi-antenna systems. Representative works in these areas will be discussed during the tutorial. Finally, we will introduce two prototyping testbeds, built on software-defined radio platforms and off-the-shelf WiFi systems respectively, for validating and evaluating AoI-oriented analysis, design, and optimization in real office environments.

 

TUT 03: AoI, QoE, and Beyond: Optimizing Emerging Network Metrics through Second-Order Analysis

Presenters: I-Hong Hou (Texas A&M University, USA); Ping-Chun Hsieh (National Chiao Tung University, Taiwan)

Abstract: Many emerging and future network applications demand service guarantees that cannot be properly characterized by traditional first-order quality of service metrics. As a result, while there have been growing interests in new network performance metrics such as age of information, quality of experience, and timely-throughput, the problem of optimizing these new network metrics remain largely open. A new theory that explicitly address higher-order network behaviors is needed.

This tutorial describes a general framework for studying the second-order network behaviors. It will cover the model, the analysis, and the optimization, of second-order network performance metrics. In addition, it includes several case studies on emerging applications, including real-time remote sensing and real-time video streaming.

 

TUT 04: Beyond Massive MIMO: User-Centric Cell-Free Massive MIMO

Presenters: Emil Björnson (Linköping University, Sweden); Luca Sanguinetti (University of Pisa, Italy); Özlem Tuğfe Demir (Linköping University, Sweden)

Abstract: Massive MIMO (multiple-input multiple-output) is no longer a promising concept for cellular networks-in 2019 5G it became a reality, with 64-antenna fully digital base stations being commercially deployed in many countries. However, this is not the final destination in a world where ubiquitous wireless access is in demand by an increasing population. It is, therefore, time for MIMO and mmWave communication researchers to consider new multi-antenna technologies that might lay the foundations for beyond 5G networks. In particular, we need to focus on improving the uniformity of the service quality.

Suppose all the base station antennas are distributed over the coverage area instead of co-located in arrays at a few elevated locations, so that the mobile terminals are surrounded by antennas instead of having a few base stations surrounded by mobile terminals. How can we operate such a network? The ideal solution is to let each mobile terminal be served by coherent joint transmission and reception from all the antennas that can make a non-negligible impact on their performance. That effectively leads to a user-centric post-cellular network architecture, called ""User-Centric Cell-Free Massive MIMO"". Recent papers have developed innovative signal processing and radio resource allocation algorithms to make this new technology possible, and the industry has taken steps towards implementation. Substantial performance gains compared to small-cell networks (where each distributed antenna operates autonomously) and cellular Massive MIMO have been demonstrated in numerical studies, particularly, when it comes to the uniformity of the achievable data rates over the coverage area.

 

TUT 05: Deep Learning for Wireless Communications

Presenters: Geoffrey Li (Imperial College London, UK); Zhijin Qin (Queen Mart University of London, UK)

Abstract: In the tutorial, we will provide a comprehensive overview on DL for wireless communications, including physical layer processing, resource allocation, and semantic communications.

We first present progress in DL in physical layer communications. we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we introduce joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems.

In the second part of this tutorial, we will present recent progress in deep learning based wireless resource allocation. Deep learning can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first introduce how to use deep learning to solve optimization problems for resource allocation. We will then discuss deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.

Enabled by deep learning, semantic communications are promising to further improve the communication system efficiency, which is regarded as the second level of communications by Shannon and Weaver in addition to typical communications focusing on successful transmission of symbols. Semantic communications aim to realize the successful semantic information exchange rather than receive the transmitted bit sequences or symbols accurately. In this part, we will first introduce the concept of the semantic communication. We then detail the principles and performance metrics of semantic communications. Afterwards, we will present the initial work on deep learning enabled semantic communications.

 

TUT 06: Distributed Deep Learning: Concepts, Methods & Applications in Wireless Networks

Presenters: Wojciech Samek (Fraunhofer Heinrich Hertz Institute, Germany); Deniz Gündüz (Imperial College London, United Kingdom (Great Britain)

Abstract: Deep learning provides a unique opportunity to revolutionize applications in communications. However, due to limited resources (e.g., bandwidth and power), latency constraints, and data privacy concerns, centralized training schemes, which require all the data to reside at a central location, are no longer available in the wireless network setting. Thus, these training schemes are increasingly substituted by distributed deep learning, which allows multiple parties to jointly train a model on their combined data, without any of the participants having to reveal their local data to other parties, or to a centralized server. This new form of collaborative training concentrates learning in locations where the models are actually used (i.e., on the network edge), and thus minimizes latency and resource consumption. The objective of the tutorial is to introduce the most important concepts and methods in distributed deep learning, and to systematically discuss the challenges and advantages of their application in wireless networks. The tutorial will not only provide a theoretical understanding of the distributed learning problem (e.g., distributed SGD, federated averaging, convergence results) and teach the related concepts from information theory, optimization and wireless communications, but also discuss the small tricks (e.g., error accumulation, synchronization, client clustering) to make distributed learning schemes work in practice. Furthermore, we will present the recent developments and trends, in particular the applications of distributed learning to wireless networks, and give a first-hand summary of the relevant standardization activities (e.g., ITU FG ML5G, MPEG AHG CNNMCD).

 

TUT 07: Distributed Machine Learning over Wireless Networks: Challenges and Opportunities

Presenters: Walid Saad (Virginia Tech, USA); Mehdi Bennis (Centre of Wireless Communications, University of Oulu, Finland); Mingzhe Chen (Princeton University)

Abstract: Machine learning approaches have recently received considerable attention as key enablers for next-generation intelligent networks. Currently, most existing learning solutions for wireless networks require centralizing the training data at a data center. However, due to privacy constraints and communication overhead, it is impractical for all devices to transmit their data to a data center for centralized learning. To solve this problem, distributed learning (DL) is needed, where devices collaboratively train a shared learning model using their generated data. The avoidance of data uploading to a central data center preserves privacy and reduces network traffic congestion. However, distributed training also requires devices and the data center exchange significant amount of information via wireless transmission. Therefore, wireless impairments will affect DL training process and performance. DL can be used solving communication problems. Given the role of DL in future wireless networks, it is imperative to provide an overview on their fundamentals, their deployment over wireless networks, and their applications in wireless networks. The goal of this tutorial is to provide a tutorial on the topic of DL over wireless networks. In particular, we will first introduce the DL fundamentals and explain why DL and wireless networks must be jointly considered. Then, we introduce a classification of the various types of distributed learning algorithms. We then overview wireless communication techniques for improving DL performance. We overview the use of DL for wireless networks. Finally, we conclude by shedding light on the potential future works within the overall areas of wireless communication and DL.

 

TUT 08: Establishing Trust in the Air: Wireless Communications and Sensing Approaches for Safe UAVs

Presenters: Evgenii Vinogradov and Sofie Pollin (KU Leuven, Belgium)

Abstract: Small drones are becoming a part of our everyday life. They are used in a wide variety of commercial applications, and the number of drones in the air is steadily growing. Naturally, as any other new global phenomenon, this growth results in public concerns about safety and security issues aroused by the UAV-use. To ensure the safe operation of drones, traffic (and conflict) management rules must be designed and implemented by a team effort of avionics and telecommunication experts. In this tutorial, we establish a common terminology for these two communities. We first describe the UAV traffic management (UTM) architecture and services as well as main conflict management procedures. Moreover, we analyze which existing wireless technologies (from ADS-B to 5G) can be useful for UTM. Next, we will discuss how to ensure the safe use of UAVs via various RF-based techniques for detecting the presence of non-collaborative UAVs in the airspace (including Machine Learning and Passive Coherent Location techniques). Finally, we will show how UAV-enabled wireless networks can be deployed in the cases when the ground infrastructure is damaged (for instance, during a natural disaster).

 

TUT 09: Federated Learning at the Network Edge: Fundamentals, Key Technologies, and Future Trends

Presenters: Howard Yang (SUTD, Singapore); Zhongyuan Zhao (Beijing University of Posts and Telecommunications, China); Tony Q. S. Quek (Singapore University of Technology and Design, Singapore)

Abstract: The burgeoning advances from machine learning and wireless technologies are forging a new paradigm for future networks, which are expected to possess higher degrees of intelligence via the inference from vast data set and being able to respond to local events in a prompt manner. Due to the sheer volume of data generated by the end devices, as well as the increasing concerns about sharing private information, a new learning model, namely the federated learning, has emerged from the intersection of artificial intelligence and edge computing. In contrast to the conventional machine learning methods, federated learning brings the models directly to the device for training, where only the resultant parameters shall be sent to the edge servers. The local copies of the model bring along advantages of eliminating the network latency and preserving data privacy. Nevertheless, to make federated learning possible, one needs to tackle new challenges that require a fundamental departure from the standard methods designed for distributed optimizations. In this tutorial, we first survey the basis of federated learning, including its distinct features from conventional machine learning models, the fundamental theories that ensure the successful operation of federated learning, and algorithms to avail an effective adoption. We then enumerate several critical issues associated with the deployment of federated learning in a wireless network, and show how technologies from different perspectives, ranging from algorithmic design, on-device training, to communication resource management, shall be jointly integrated to facilitate the full implementation. Finally, we conclude by shedding light on future works.

 

TUT 10: Introducing IEEE 802.11be - The Wi-Fi of the Future

Presenters: Giovanni Geraci (Universitat Pompeu Fabra, Spain); Lorenzo Galati Giordano (Nokia Bell Labs, Ireland); Boris Bellalta (Universitat Pompeu Fabra, Spain)

Abstract: Wi-Fi is among the greatest success stories of this technology era, it has become an essential part of the home and a key complementary technology for both enterprise and carrier networks. Since the requirements of wireless data services keep increasing in many scenarios such as homes, enterprises, and hotspots, the Wi-Fi community is aiming high and has recently initiated discussions on new IEEE 802.11 technical features for bands between 1 and 7.125 GHz. The creation and standardization of the next-generation of Wi-Fi technology beyond Wi-Fi 6--referred to as 802.11be Extremely High Throughput (EHT)--targets to increase peak throughput and ensure that Wi-Fi meets the requirements set by incoming applications, thereby maintaining--or even augmenting--its appeal to consumers.

The scope of this tutorial is to provide the research community with fresh updates on the most recent outcomes and directions related to the next-generation Wi-Fi technology, directly from the IEEE 802.11be standardization meetings. IEEE Globecom 2020, taking place in December 2020, is the perfect venue to provide a digested summary of Wi-Fi's prospective. Indeed, the key features of 802.11be are currently being shaped, and the first draft of the standard is expected in September 2020.

 

TUT 11: Machine Learning-Enabled and Ultra-Low Latency Connected Transportation

Presenters: Shih-Chun Lin (North Carolina State University, USA)

Abstract: Connected autonomous vehicles (CAVs) emerge as one major technological paradigm shift in the industry and human society while introducing more technological challenges in wireless networks. As the technology for a single autonomous vehicle/robot becoming mature, the real challenge comes from the reliable, safe, real-time operation of connected transportation with massive CAVs/robots. To achieve such multi-scale management and control, effective on-board computing, edge computing, and cloud computing, as well as innovative networking and computing technologies in real-time to interact with environments and other agents such as vehicles and individuals, are the must. Aligning with this trend, 3GPP has released the service requirements to sustain the next-generation vehicle-to-everything (V2X) applications in June 2018, which include 28 use cases to involve advanced driving, remote control, vehicle platooning and extended sensors. Particularly, ultra-low latency mobile/vehicular networking with high reliability and safety is inevitably wanted to ensure successful control and services in this most challenging Internet-of-Things (IoT) and robotics. This tutorial will present emerging and key technological aspects of ultra-low latency and machine learning (ML)-based network architecture dedicated to connected transportation. It will include uplink and downlink air-interface, ultra-reliable and low-latency communications (uRLLC) for beyond 5G, channel estimation and radio resource allocation based on ML, software-defined networking (SDN) architecture and realization, network function virtualization (NFV) of system resources, ML-enabled anticipatory mobility management, and network security, and ML-based network architecture under new development by the ITU-T.

 

TUT 12: Massive Machine-Type Communications for IoT: Recent Progress and Future Directions

Presenters: Liang Liu (The Hong Kong Polytechnic University, China); Wei Yu (University of Toronto, Canada)

Abstract: The future wireless cellular networks are envisioned to not only enhance broadband access for human-centric applications, but also offer seamless connectivity to a variety of devices for machine-centric applications empowered by the Internet of Things (IoT) technologies, e.g., smart manufacturing and smart wearables. Notably, thanks to the rapid advancement in IoT technologies, it is envisioned that the number of IoT devices will exceed 75 billion by the year of 2025, which is much larger than the number of the mobile phone users. This gives rise to the critical challenge about how to provide massive connectivity across tens of billions of devices in the coming era of IoT. To resolve the above issue, the fifth-generation (5G) cellular communication standard has already identified massive machine-type communications (mMTC) as a key use case in future cellular networks. Inspired by the urgent demand, recently, researchers all over the world have devoted tremendous efforts to the investigation of mMTC techniques. This tutorial aims to provide a state-of-the-art overview of the recent progress in this exiting realm.

 

TUT 13: Millimeterwave and Terahertz Propagation Channels - Measurement, Modeling, and Simulation

Presenters: Andreas Molisch and Naveed Ahmed Abbasi (University of Southern California, USA)

Abstract: For the design, performance assessment, and deployment planning of wireless systems, understanding of the propagation mechanisms and creation of suitable channel models is a conditio sine qua non. For mmWave and THz systems, measurement and modeling of the corresponding propagation channels is thus of the utmost interest. This is particularly relevant since many of the dominant propagation effects are significantly different from those at the traditional cm-wave frequencies (i.e., microwave range). Due to the complexity of mmWave and THz systems, the channel models also have to correctly account for a variety of channel parameters.

This tutorial will first provide a review of physical propagation processes, stressing the points that are particularly relevant at mmWave and THz channels. We then proceed to the measurement techniques, which are considerably more challenging than at lower frequencies, as well as deterministic (ray tracing/launching) methods for high frequency ranges. A review of key measurement results in the literature as well as gaps in our current experimental knowledge follows next. A review of channel models, ranging from purely stochastic to GSCM to map-based, will round off the tutorial. Throughout the tutorial, impact of the propagation on the system design will be emphasized.

 

TUT 14: New IP: Rethinking a Future Internet with New Service Capabilities

Presenters: Lijun Dong and Alexander Clemm (Futurewei Technologies, USA)

Abstract: The Internet has been a massive success, but the technology it is based on is increasingly reaching its limits concerning the ability to support novel emerging applications such as Industry 4.0, holographic media, and telehaptics. This tutorial presents a number of emerging use cases that illustrate those limitations and highlight challenges and opportunities for research. In addition, a new data communication framework called New IP is presented as an approach to address many of those challenges. New IP evolves the current IP in a non-disruptive way in order to provide the new service capabilities that are essential in the future.

A main goal of the tutorial is to stimulate research innovations within the network by pointing out future networking challenges and presenting new technical approaches by which networking technology can be rethought.

 

TUT 15: Non-Terrestrial Networks (NTN): The Next 20 Years

Presenters: Halim Yanikomeroglu (Carleton University, Canada)

Abstract: The very fundamental principles of digital and wireless communications reveal that the provision of ubiquitous super-connectivity in the global scale - i.e., beyond indoors, dense downtown or campus-type areas - is infeasible with the legacy terrestrial network architecture as this would require prohibitively expensive gross over-provisioning. The problem will only exacerbate with even more demanding use-cases of 2030s such as UAVs requiring connectivity (ex: delivery drones), thus the 3D super-connectivity.

The roots of today's (4G & 5G) wireless access architecture (the terrestrial cellular network) go back to 1940s. The access architecture has evolved substantially over the decades. However, rapid developments in a number of domains outside telecommunications, including those in aerospace and satellite industries as well as in artificial intelligence, will likely result in a disruptive transformation in the wireless access architecture in the next 20+ years.

In this tutorial, an ultra-agile, dynamic, distributed, and partly-autonomous vertical heterogeneous network (VHetNet) architecture with very low earth orbit satellites (VLEOs), high-altitude platform stations (HAPS), and UAV-BSs (UxNB in 3GPP terminology) for almost-ubiquitous super-connectivity will be presented. In this disruptive setting, free-space optical (FSO) communications will play an important role in addition to the legacy radio communications.

 

TUT 16: Quantum Communications - A Glimpse Beyond Moore's Law

Presenters: Angela Sara Cacciapuoti (University of Naples Federico II, Italy); Marcello Caleffi (University of Naples "Federico II", Italy); Anthony Soong (Futurewei Technologies Inc, U.S.A.); Lajos Hanzo (University of Southampton, United Kingdom / Great Britain)

Abstract: Moore's law has indeed prevailed since he outlined his empirical rule-of-thumb in 1965, but based on this trend the scale of integration is set to depart from classical physics, entering nano-scale integration, where the postulates of quantum physics have to be obeyed. The quest for quantum-domain communication solutions was inspired by Feynman's revolutionary idea in 1985: particles such as photons or electrons might be relied upon for encoding, processing and delivering information. Hence in the light of these trends it is extremely timely to build an interdisciplinary momentum in the area of quantum communications, where there is an abundance of open problems for a broad community to solve collaboratively. In this workshop-style interactive presentation we will address the following issues:

  • We commence by highlighting the nature of the quantum channel, followed by techniques of mitigating the effects of quantum decoherence using quantum codes.
  • Then we bridge the subject areas of large-scale search problems in wireless communications and exploit the benefits of quantum search algorithms in multi-user detection, in joint-channel estimation and data detection, localization and in routing problems of networking, for example.

 

TUT 17: Reconfigurable Metasurfaces for Intelligent 6G Wireless Networking

Presenters: George C. Alexandropoulos (University of Athens, Greece); Christos Liaskos (Institute of Computer Science, Foundation of Research and Technology, Hellas, Greece); Ian F. Akyildiz (Georgia Institute of Technology, USA)

Abstract: The increasingly demanding objectives for sixth Generation (6G) wireless communication networks have spurred recent research activities on novel wireless hardware architectures. Among them belong the reconfigurable metasurfaces, which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate an incoming electromagnetic field in a wide variety of functionalities. Incorporating reconfigurable metasurfaces in wireless networks has been recently advocated as a revolutionary means to transform any naturally passive wireless communication environment (the set of objects between a transmitter and a receiver constitute the wireless environment) to an active one. This can be accomplished by deploying cost-effective and easy to coat reconfigurable metasurfaces to the environment's 3D components (e.g., building facades and room ceilings), thus, offering increased environmental intelligence for the scope of diverse wireless networking objectives. This tutorial has the following three core objectives: i) to detail the available hardware designs for reconfigurable metasurfaces intended for intelligent wireless networking; ii) to present and explain the latest signal processing and AI approaches for their efficient configuration; and iii) to discuss adequate candidate network architectures for metasurfaces-enabled 6G wireless communication systems.

 

TUT 18: Scalable, Optimal, Yet Explainable Gaussian Process Models for Data-Driven Wireless Systems

Presenters: Feng Yin (The Chinese University of Hong Kong (Shenzhen), China); Yue Xu (Beijing University of Posts and Telecommunications, China); Shuguang Cui (The Chinese University of Hong Kong, Shenzhen & Shenzhen Research Institute of Big Data, China)

Abstract: This fresh-baked tutorial will be held for the first time in GLOBECOM-2020 to cover both the theory and practice of Gaussian process models for futuristic data-driven wireless applications. We will focus on three important aspects, namely the scalability, optimality, and interpretability of the learning model. GP models suits wireless data better than other deep learning models. Besides, GP models can be easily integrated into the new-emerging graph learning and meta learning paradigms to trigger more sample efficient inference algorithms for data-driven wireless applications.

 

TUT 19: Softwarization and Virtualization in 5G and Beyond Mobile Networks

Presenters: Fabrizio Granelli (University of Trento, Italy); Frank H.P. Fitzek (Technische Universität Dresden & ComNets - Communication Networks Group, Germany)

Abstract: The aim of the tutorial is to illustrate how the emerging paradigms of Software Defined Networking, Network Function Virtualization, and Information Centric Networking will impact on the development of future systems and networks, both from the theoretical/formal as well as from the practical perspective. Main focus will be on mobile networks, i.e. 5G and beyond. The tutorial will provide a comprehensive overview of the individual building blocks (software defined networking; network function virtualization; information centric networks) enabling the concept of computing in future networks, starting from use cases and concepts over technological enablers (Mininet; Docker) and future innovations (machine learning; network coding; compressed sensing) to implementing all of them on personal computers. Practical hands-on activities will be proposed, with realistic use cases to bridge theory and implementation by several examples, through the usage of a pre-built ad-hoc Virtual Machine (ComNetsEmu) that can be easily be extended for new experiments. The instructions to download the Virtual Machine will be provided in advance of the event.

The main objective of the tutorial will be to expose attendees to the most recent technologies in the field of networking and teach them how to use them in a real setup in the "hands-on" session.

A related book written by the two presenters "Computing in Communication Networks" will be published in 2020 by Elsevier, and provide in-depth description of the concepts and hands-on activities presented in the tutorial, to enable interested attendees to learn additional details on the reviewed technologies.

In order to perform the hands-on activities and run the examples of this tutorial, participants are recommended to install the corresponding virtual machine following the instructions at this link: https://git.comnets.net/public-repo/comnetsemu

TUT 20: Spectrum Sharing for Inter-Technology Coexistence

Presenters: Marina Petrova (KTH Royal Institute of Technolgy, Sweden); Ljiljana Simić and Andra M. Voicu (RWTH Aachen University, Germany)

Abstract: Increasing capacity demands in emerging wireless technologies are triggering spectrum bands opening to multiple technologies. This will, in turn, increase the interference level and result in more complex inter-technology interactions, which will need to be managed through spectrum sharing mechanisms. It is not trivial to design such efficient coexistence mechanisms, not only due to technical aspects, but also due to regulatory and business constraints. A topical example is the use of the new mm-wave spectrum bands, where the envisioned high-density deployment of heterogeneous, directional devices poses new regulatory questions and has spectrum management implications. In this tutorial we will address spectrum sharing mechanisms for inter-technology coexistence from a system-level perspective, including engineering and regulatory aspects. We will first discuss coexistence in sub-6 GHz bands within different regulatory frameworks, i.e. hierarchical and spectrum commons. We will then highlight current technical and regulatory approaches to access and share mm-wave spectrum to provide new services and features in the context of 5G and Wi-Fi evolution. Furthermore, we will discuss spectrum sharing in the 6 GHz band, which will likely open to unlicensed operation soon. Throughout, we will use relevant case studies to highlight coexistence challenges and to illustrate the state-of-the-art sharing mechanism design. Finally, we will emphasize open research questions for inter-technology coexistence in emerging deployments.

https://spectrumcoexistence2020tutorial.inets.rwth-aachen.de

TUT 21: Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network

Presenters: Rui Zhang (National University of Singapore, Singapore); Qingqing Wu (University of Macau, China)

Abstract: This tutorial aims to address the various new issues arising from analyzing, designing and implementing IRS-aided wireless networks, for achieving smart and reconfigurable wireless environment in a cost-effective manner. Specifically, we will first present how to model the IRS signal reflection as well as the IRS-reflected channel based on the basic principles of physics and EM theory. Based on the derived channel model, we will then address the main challenges in IRS-aided wireless networks, including 1) capacity characterization for the single-user/multiuser/multicell setups; 2) joint active and passive beamforming with continuous/discrete amplitudes/phase shifts in both narrowband and wideband systems; 3) efficient channel acquisition methods with an emphasis on the case without receive RF chains at the IRS; 4) how to jointly deploy the passive IRSs with active BSs/APs/relays; 5) how to model and deal with the practical hardware imperfections. We will present the latest results from both academia and industry on addressing the above issues, which are timely and have not been systematically presented previously based on our best knowledge.

 

TUT 22: Ultra-Reliable and Low-Latency Communications for Industry 4.0: Industrial and Academic Perspectives

Presenters: Zhibo Pang (ABB AB Corporate Research, Sweden); Guodong Zhao (University of Glasgow, United Kingdom (Great Britain)); Yonghui Li (University of Sydney, Australia)

Abstract: Future wireless networks need to provide Ultra-Reliable and Low-Latency Communications (URLLC) for control-oriented applications, which is also one of the major goals in the fifth generation (5G) communication systems. In this tutorial, we will show our testbeds on wireless controlled robotics in Glasgow and Sydney. We discuss some fundamental design aspects and challenges to enable real-time industrial applications in future wireless networks. First, we start from industrial perspective to introduce the basic requirements of wireless communications for Industry 4.0, where specific use cases will be provided. Then, we review the recent advances in communication-control co-design to understand the strong dynamics and inter-dependencies between wireless communication and industrial control systems. Finally, we discuss the URLLC design, open problems, and potential research directions from academic perspective, which covers physical (PHY) and media access control (MAC) layers.

 

TUT 23: What Physical Layer Security Can Do for 6G

Presenters: Arsenia Chorti (ETIS / ENSEA UCP CNRS & University Paris Seine, France); H. Vincent Poor (Princeton University, USA)

Abstract: While security protocols predominantly focus on the core network, the enhancement of the security of the B5G access network becomes of critical importance. Despite the strengthening of 5G security protocols with respect to LTE, there are still open issues that have not been fully addressed. The current tutorial is articulated around the premise that rethinking the security design bottom up, starting at the physical layer, is not only viable in 6G but importantly, arises as an efficient way to overcome security hurdles in novel use cases, notably mMTC and URLLC. In this tutorial, we begin with a review of fundamental concepts in security overall and physical layer security in particular. We then move to provide a comprehensive review of the state-of the-art in i) secret key generation from shared randomness, ii) the wiretap channel in the mMIMO era, iii) authentication of devices using physical unclonable functions (PUFs) and localization based authentication, protocols using multi-factor authentication, iv) jamming attacks and intrusion detection at PHY. We finally conclude with the proposers' aspirations for the 6G security landscape, in the hyper-connectivity and semantic communications era.

 

TUT 24: Wireless Channel Charting for Massive MIMO Presenters:

Presenters: Maxime Guillaud (Huawei Technologies, France); Christoph Studer (ETH Zurich, Switzerland)

Abstract: Channel charting is an emerging framework that enables pseudo-positioning of user equipments (UEs) from channel state information (CSI) only. More concretely, channel charting associates CSI to UE spatial location by means of dimensionality reduction and manifold learning, thus enabling the infrastructure base-stations or wireless access points to perform a number of predictive tasks relevant to emerging wireless networks that depend on UE location. Prominent application examples are localization relative to points-of-interest, UE grouping, cell handover, UE path prediction, predictive rate control, assisted beam-finding, etc. The distinctive characteristic of channel charting with respect to classical positioning techniques resides in its self-supervised nature, i.e., the fact that it relies only on measured CSI and no other information (e.g., from global navigation satellite systems or classical localization anchors) is required.

This tutorial will cover the theoretical and algorithmic foundations of channel charting, discuss its implementation in next-generation (beyond 5G) cellular systems, and showcase applications ranging from predictive radio resource management to positioning. The goal of this tutorial is to provide the audience with an exhaustive overview of the nascent research field of channel charting, which is at the intersection of machine learning, numerical optimization, channel modeling, and communication theory. To this end, this tutorial will (i) introduce a wide range of theoretical and algorithm-level concepts, and (ii) demonstrate its efficacy with real-world results from indoor and outdoor channel measurements.

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