All of the tutorials will be available for access on-demand through the conference virtual platform for those attendees with a Tutorial registration. Some of the tutorials will take place in-person in Madrid and some will be presented remotely but live. The list is below. Please note: This list is subject to change depending on how many people sign up for these tutorials in advance.
7 December, 9:00 - 12:30 CET (Madrid time)
TUT-25: NB-IoT over Aerial and Space Networks: Technology Overview, Challenges, and Potential Solutions (hybrid in-person + remote)
7 December, 14:00 - 17:30 CET (Madrid time)
TUT-16: Universal Decoding by Guessing Random Additive Noise Decoding (remote LIVE)
TUT-28: Tools and Techniques for Future Spectrum Sharing and Coexistence (in-person)
11 December, 14:00 - 17:30 CET (Madrid time)
TUT-31: QoS-Driven 6G Promising Techniques Over Multimedia Mobile Wireless Networks (remote LIVE)
List of tutorials
The complete list of tutorials is as follows. The pre-recording presentations of these tutorials will be available for on-demand access through the conference virtual platform.
TUT-01: Reconfigurable Intelligent Surfaces for Future Wireless Communications
TUT-02: Sensors-as-a-Service for Internet of Things
TUT-03: AI-Enabled Optimization of Virtualized Open RAN
TUT-04: On the Way to 6G UAV and Satellite Communications
TUT-05: Wi-Fi unleashed: Wi-Fi 7, 6 GHz, and beyond
TUT-06: Wireless for Machine Learning
TUT-07: Semantic Communications: Communication beyond Shannon Paradigm
TUT-08: Application of NOMA in 6G Networks: Future Vision for Next Generation Multiple Access
TUT-09: Integrated Access and Backhaul for 5G and Beyond
TUT-10: Deep Reinforcement Learning and Transfer Learning for Future Wireless Networks
TUT-11: Integrated Sensing and Communication (ISAC) for 6G: From Theory to Applications
TUT-12: Deep Learning and Reinforcement Learning for Future Wireless Network Optimization
TUT-13: Intelligent Reflecting Surface for Wireless Communications: Fundamentals, Designs, and Open Issues
TUT-14: Signal Processing for Converged Terahertz Communications and Sensing
TUT-15: Authentication Protocols for Next Generation Wireless Networks
TUT-16: Universal Decoding by Guessing Random Additive Noise Decoding
TUT-17: 6G Core Networks: New Components and Enabling Technologies
TUT-18: Wireless Information and Energy Transfer in the Era of 6G Communications
TUT-19: Reconfigurable Intelligent Surfaces for 6G: Communications, Localization, and Sensing
TUT-20: Blockchain and Cryptoeconomics for Cyber-physical System and Wireless Networks
TUT-21: Beyond 5G Joint Sensing and Communications
TUT-22: Cellular V2X for Connected Automated Driving
TUT-23: Deep Learning in Wireless Security and Privacy for Next-Generation Communication Systems
TUT-24: Nano-scale Terahertz based Communication: Opportunities, Challenges, and Recent Advances
TUT-25: NB-IoT over Aerial and Space Networks: Technology Overview, Challenges, and Potential Solutions
TUT-26: Towards 6G V2X for Connected and Automated Vehicles
TUT-27: Age of Information: A Paradigm Shift Towards Semantic Properties in Data Science
TUT-28: Tools and Techniques for Future Spectrum Sharing and Coexistence
TUT-29: Machine Learning for MIMO Systems with Large Arrays
TUT-30: Networking and Communications for Intelligent and Connected Vehicles in 5G and Beyond
TUT-31: QoS-Driven 6G Promising Techniques Over Multimedia Mobile Wireless Networks
TUT-01: Reconfigurable Intelligent Surfaces for Future Wireless Communications
Presenters:
Alessio Zappone (University of Cassino and Southern Lazio, Italy); Shi Jin (Southeast University, China); Mérouane Debbah (Huawei, France); Marco Di Renzo (Paris-Saclay University / CNRS, France)
Abstract:
As 5G networks take their final form, connectivity demands continue to increase exponentially and new services pose more constraints on the performance that end-users expect. A recent technological breakthrough that holds the potential to meet these demands is that of reconfigurable intelligent surfaces. We believe that a tutorial on the principles and latest approaches of reconfigurable intelligent surfaces for beyond 5G wireless communications will be of great value for both academics and industry practitioners.
TUT-02: Sensors-as-a-Service for Internet of Things
Presenters:
Mohammad S. Obaidat (University of Jordan, USA); Sudip Misra (Indian Institute of Technology-Kharagpur, India); Arijit Roy (University of Luxembourg & SensorDrops Networks Pvt. Ltd., India)
Abstract:
A user procures a Wireless Sensor Network (WSN) solution targeting a specific application such as surveilling a particular area, gathering raw data from an environment, and tracking a moving object. These networks become an intrinsic part of the Internet of Things (IoT) infrastructure. Typically, IoT infrastructure provides a unified platform to serve multiple distinct applications, simultaneously. The efficient management and utilization of sensors become crucial research issue in IoT. The sensor-cloud (SC) technological paradigm has gained popularity due to its unique features of provisioning Sensors-as-a-Service (Se-aaS) and efficiently managing an IoT infrastructure. Se-aaS facilitates the IoT platform to serve multiple applications concurrently and eliminates the single-user-centric usage of traditional sensor networks. In this tutorial, we deliver a comprehensive introduction of Sensors-as-a-Service (Se-aaS). Specifically, we first survey the basics of IoT and the usage of sensors in it. We then enumerate different critical issues -- virtual sensor formation, data caching, and pricing -- associated in designing an SC platform for provisioning Se-aaS. We target to discuss its economic aspects and highlight various pricing models. Finally, the tutorial concludes with a discussion of new research challenges, which can be investigated in the future.
This tutorial is intended for the attendees of IEEE ICC 2021 from academia, research organizations, and industries. We are confident that this tutorial will help researchers from academia and research organizations to explore the open research issues in Se-aaS. On the other hand, an industry participant will be trained how to set up an SC platform for provisioning Se-aaS.
TUT-03: AI-Enabled Optimization of Virtualized Open RAN
Presenters:
Melike Erol-Kantarci (University of Ottawa, Canada); Meryem Simsek (VMWare & International Computer Science Institute, USA)
Abstract:
Future wireless networks are expected to support a multitude of services. According to the International Telecommunication Union (ITU), 5G network services can be classified into three service scenarios: Enhanced Mobile Broadband (eMBB), Ultra-Reliable and Low-latency Communications (uRLLC), and massive Machine Type Communications (mMTC). Heterogeneous devices of different quality of service demands will require intelligent and flexible allocation of network resources in response to network dynamics. For instance, a highly reliable and low-latency network is needed to enable rapid transfer of messages between connected autonomous vehicles. At the same time, the same physical infrastructure is expected to serve users with high-quality video demand or even mobile Augmented/Virtual Reality entertainment applications. Next-generation wireless networks, i.e. 5G and the upcoming 6G, are expected to simultaneously accommodate diverse use cases. In particular, the heterogeneous traffic coming from mobile, vehicular, smart grid, and tactile domains calls for efficient utilization of network resources to maintain quality of service demands of each application. In addition, resource efficiency, reliability, and robustness are becoming more stringent for 5G and beyond networks. To meet this, 6G must incorporate a paradigm shift in network and radio resource optimization, in which efficient and intelligent resource management techniques have to be employed. In addition to all those new types of services and demands, wireless networks are at the cusp of a new paradigm with open virtualized architectures where softwarization of networks is helping to disaggregate network functions in the wireless domain and allowing for ultimate autonomy capabilities. Artificial intelligence (AI), or more specifically machine learning (ML) algorithms stand as promising tools to intelligently manage the networks such that network efficiency, reliability and robustness goals are achieved, quality of service demands are satisfied, network and computational resources are used most efficiently, and performance targets are achieved in a self-optimized manner. The opportunities that arise from learning the environment parameters under varying conditions, positions AI-enabled 5G and 6G superior to preceding generations of wireless networks. In addition to using AI for networks, the distributed nature of networks provides a natural environment for enhanced machine learning opportunities. This tutorial will begin with an introduction to 5G and beyond networks together with open radio access network (RAN) features and some fundamentals on ML. After summarizing the state-of-art in ML algorithms and their applications to (open) RAN, it will continue with a full-fledged treatment of clustering algorithms, reinforcement learning, deep learning and federated learning techniques. Finally, challenges and open issues will be discussed both in terms of AI algorithms and their applicability to various functions of future wireless networks. These discussions will be put into perspective considering the recent 5G NR Release 16 and the plans for 6G.
TUT-04: On the Way to 6G UAV and Satellite Communications
Presenters:
Adrian Garcia-Rodriguez (Huawei Technologies, France); Giovanni Geraci (Universitat Pompeu Fabra, Spain); Muhammad Zeeshan Shakir (University of the West of Scotland, United Kingdom (Great Britain))
Abstract:
Drones--a.k.a. UAVs--are taking over many processes requiring efficient, automated, and flexible machines. For their whole ecosystem to take off, the wireless community is trying to discover the full potential of this new class of mobile devices in both 5G and the future 6G networks. Simultaneously, the cellular communications industry is taking one step upward to the sky: integrating satellite communications in next-generation mobile networks with the ultimate goal of providing anything, anytime, anywhere connectivity. In light of the unprecedented interest in this field, this one-of-a-kind tutorial blends our academic and industrial views to take a holistic approach to UAV and satellite cellular communications:
- Outside the classroom: A fresh look at the economic, regulatory, and 3GPP standardization status of UAV cellular communications, including the essentials on the performance of UAVs and satellites in 5G NR cellular networks.
- Inside the classroom: Distilling, among others, the use of artificial intelligence and machine learning as enabling tools for future autonomous flying platforms acting as backhaul for small cells or pop-up flying base stations.
- A glimpse to the future: Projected 6G UAV and satellite use cases, requirements, and the potential disruptive technologies to make them a reality: AI for modeling and optimization, THz bands, aerial cell-free architectures, and the support of smart wireless environments.
TUT-05: Wi-Fi unleashed: Wi-Fi 7, 6 GHz, and beyond
Presenters:
Cheng Chen and Carlos Cordeiro (Intel Corporation, USA)
Abstract:
The arrival of Wi-Fi 6 in late 2019, which is based on IEEE 802.11ax, marked a giant leap forward in improving the capacity, efficiency, and coverage in most WLAN environments. While Wi-Fi 6 introduced various features to enhance the network performance and user experience of the high-dense deployment scenarios, emerging applications like 4K/8K video, AR/VR, industrial Internet of things (IoT), real-time collaborations demand more responsive connectivity. Meeting the most stringent requirements in throughput and latency in these scenarios is beyond the capabilities of Wi-Fi 6 and therefore motivates the development of a new Wi-Fi 7 generation. As a result, the IEEE 802.11 Task Group be has been formed to define Extreme High Throughput (EHT) PHY and MAC layers capable of supporting a maximum throughput of at least 30 Gbps, as well as reducing worst case latency and jitter to improve support for time sensitive applications. In this tutorial, our primary goal is to identify and describe the main PHY and MAC elements that will shape Wi-Fi 7, which will operate in the 2.4 GHz, 5 GHz, and 6 GHz bands. For each of the main features, including key PHY enhancements, multi-link operation (MLO), enhanced quality of service (QoS) management, and multiple access point (Multi-AP) coordination, we discuss how Wi-Fi 7 is designing the corresponding enabling mechanism and present performance results as appropriate. We will also talk about how Wi-Fi is extended to the 6 GHz band (e.g., Wi-Fi 6E), a spectrum that is being made available globally for unlicensed operation.
TUT-06: Wireless for Machine Learning
Presenters:
Carlo Fischione (KTH, Sweden); Viktoria Fodor (KTH NSE, Sweden); Henrik Hellström and José Mairton Barros da Silva, Jr. (KTH Royal Institute of Technology, Sweden)
Abstract:
In view of emerging applications from autonomous driving to health monitoring, it is very likely that a large part of machine learning (ML) services in the near future will take place over wireless networks, and conversely, a large part of wirelessly transmitted information will be related to ML. As data generation increasingly takes place on devices without a wired connection, ML over wireless networks becomes critical. Many studies have shown that traditional wireless protocols are highly inefficient or unsustainable to support distributed ML services. This is creating the need for new wireless communication methods, specifically on the medium access control and physical layers, that will be arguably included in 6G. In this tutorial, we plan to give a comprehensive review of the state-of-the-art wireless methods that are specifically designed to support ML services. Namely, over-the-air computation and physical and medium access control layer optimized for supporting ML. In the over-the-air approach, multiple devices communicate simultaneously over the same time slot and frequency band to exploit the superposition property of wireless channels for gradient averaging over-the-air. In physical and medium access control layer optimized for ML, active learning metrics guide the allocation of spectrum and energy resources for faster or more accurate ML. This tutorial introduces these methods, reviews the most important works, and highlights crucial open problems.
TUT-07: Semantic Communications: Communication beyond Shannon Paradigm
Presenters:
Geoffrey Ye Li (Imperial College London, United Kingdom (Great Britain)); Zhijin Qin (Queen Mary University of London, United Kingdom (Great Britain))
Abstract:
Shannon and Weaver categorized communications into three levels:
- Level A. How accurately can the symbols of communication be transmitted? (The technical problem)
- Level B. How precisely do the transmitted symbols convey the desired meaning? (The semantic problem)
- Level C. How effectively does the received meaning affect conduct in the desired way? (The effectiveness problem)
In the past decades, communications primarily focus on how to accurately and effectively transmit symbols (measured by bits) from the transmitter to the receiver, in which bit-error rate or symbol-error rate is usually taken as the performance metrics. With the development of cellular communication systems, the achieved transmission rate has been improved tens of thousands of times and the system capacity is gradually approaching to the Shannon limit. Inspired by powerful deep learning technologies, semantic communications have been regarded as a promising direction to improve the system efficiency and reduce the data traffic so that to realize the level B or even level C communications. Semantic communications aim to realize the successful semantic information transmission that is relevant to the transmission goal at the receiver. In this tutorial, we will first introduce the concept of the semantic communications and a general model of it. We then detail the principles and performance metrics of semantic communications. Afterwards, we will present the initial work on deep learning enabled semantic communications for different sources, multi-user semantic communication systems, and green semantic communications. Finally, we will identify the research challenges in semantic communications.
TUT-08: Application of NOMA in 6G Networks: Future Vision for Next Generation Multiple Access
Presenters:
Yuanwei Liu (Queen Mary University of London, United Kingdom (Great Britain)); Zhiguo Ding (University of Manchester, United Kingdom (Great Britain)); Daniel Benevides da Costa (National Yunlin University of Science and Technology (YunTech), Taiwan)
Abstract:
User data traffic, especially large amount of video traffic and small-size internet-of-things (IoT) packets, has dramatically increased in recent years with the emergence of smart devices, smart sensors and various new applications such as virtual reality and autonomous driving. It is hence crucial to increase network capacity and user access to accommodate these bandwidth consuming applications and enhance the massive connectivity. As a prominent member of the next generation multiple access (NGMA) family, non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access candidate for the sixth-generation (6G) networks. The main contents of this tutorial is to discuss the so-called "One Basic Principle plus Four New" concept. Starting with the basic NOMA principle to explore the possible multiple access techniques in non-orthogonal manner, the advantages and drawbacks of both the channel state information based successive interference cancelations (SIC) and quality-of-service based SIC are discussed. Then, the application of NOMA to meet the new 6G performance requirements, especially for massive connectivity, is explored. Furthermore, the integration of NOMA with new physical layer techniques is considered, followed by introducing new application scenarios for NOMA towards 6G. Finally, the application of machine learning in NOMA networks is investigated, ushering in the machine learning empowered NGMA era, for making multiple access in an intelligent manner for the next generation networks.
TUT-09: Integrated Access and Backhaul for 5G and Beyond
Presenters:
Mohamed-Slim Alouini (King Abdullah University of Science and Technology (KAUST), Saudi Arabia); Behrooz Makki and Erik Dahlman (Ericsson Research, Sweden); Filip Barac (Ericsson AB, Sweden)
Abstract:
To cope with the exponential growth of wireless communications, 5G and beyond will densify the network with many base stations (BSs) of different types. The BSs, however, require backhauling. On a global scale, fiber and wireless microwave technology are dominating backhauling media. Compared to fiber, wireless backhaul comes with significantly lower cost and time-to-market as well as higher flexibility, at the cost of reliability/peak rate.
Traditionally, wireless backhaul has been mainly based on proprietary technology operating in millimeter wave (mmw) spectrum and constrained to line-of-sight propagation conditions. However, with 5G, cellular technology extends into mmw spectrum; the spectrum historically used for backhauling. Also, with small-cells deployed on street level, the backhaul links need to operate also under nonline-of-sight conditions. These, along with reducing the time-to-market/operation cost, are the main motivations for the integrated access and backhaul (IAB) concept. The aim of IAB is to provide flexible wireless backhaul using 3GPP NR technology, providing not only backhaul but also the existing cellular services in the same node.
The objective of the tutorial is to bring new insights to the analysis, design and standardization of IAB networks. Such networks guarantee the communication requirements in future smart cities and solve the ubiquitous connectivity problems in many challenging network environments, e.g., coverage/capacity enhancements in rural areas. The tutorial will 1) go through the recently finished Release 16 and ongoing Release 17 IAB work-items of 3GPP, 2) present proof-of-concept results for the usefulness of IAB, and 3) provide a vision for IAB-related research towards 6G.
TUT-10: Deep Reinforcement Learning and Transfer Learning for Future Wireless Networks
Presenters:
Ekram Hossain (University of Manitoba, Canada); Dusit Niyato (Nanyang Technological University, Singapore); Hoang Thai Dinh (University of Technology Sydney (UTS), Australia); Shimin Gong (Sun Yat-sen University, China)
Abstract:
In wireless networks, Deep Reinforcement Learning (DRL) and its Transfer Learning (TL) models have been recently used as an emerging tool to effectively address various problems and challenges. In particular, modern networks such as IoT and UAV networks become more decentralized, ad-hoc, and autonomous in nature. Network entities such as IoT devices, mobile users, and UAVs need to make local and autonomous decisions, e.g., spectrum access, data rate selection, transmit power control, and base station association, to achieve the goals of different networks including throughput maximization and energy consumption minimization. Under uncertain and stochastic environments, most of the decision-making problems can be modelled by a so-called Markov Decision Process (MDP). Dynamic programming and other algorithms such as value iteration, as well as reinforcement learning techniques can be adopted to solve the MDP. However, the modern networks are large-scale and complicated, and thus the computational complexity of the techniques rapidly becomes unmanageable. As a result, DRL has been developing to be an alternative solution to overcome the challenge. In general, the DRL and TL approaches can provide many outstanding advantages. For example, with the DRL approaches, network entities can make observation and obtain the best policy locally with minimum or without information exchange among each other. This not only reduces communication overheads but also improves security and robustness of the networks. Obviously, DRL will be the key-enabler for next generation of wireless communications networks. Therefore, DRL is of increasing interest to researchers, communication engineers, computer scientists, and application developers.
TUT-11: Integrated Sensing and Communication (ISAC) for 6G: From Theory to Applications
Presenters:
Fan Liu (Southern University of Science and Technology, China); Christos Masouros (University College London, United Kingdom (Great Britain))
Abstract:
Jointly suggested by recent advances from wireless communications and signal processing, radio sensing functionality can be integrated into 6G RAN by slightly modifying the standards and signal processing strategies. Therefore, the future cellular network could measure and image the surrounding environment to enable advanced spatial/location-aware services, ranging from the physical layer (e.g., fast and training-free beam alignment) to application layers (e.g., spatial computing or city-wide weather monitoring system, mmWave-specified). This type of research is typically referred to as Integrated Sensing And Communication (ISAC), which refers to the design paradigm and corresponding enabling technologies that combine sensing and communication systems to utilize wireless resources efficiently and even to pursue mutual benefits.
In this tutorial, we will firstly overview the background and application scenarios of ISAC. As a step further, we will introduce the state-of-the-art research progress on this topic, which consists of 3 technical parts: 1) Spectral Coexistence for Sensing and Communication Systems, 2) Co-design for ISAC Systems, and 3) ISAC for V2X Networks. Finally, we will conclude the tutorial by summarizing the future directions and open problems in the area of ISAC.
TUT-12: Deep Learning and Reinforcement Learning for Future Wireless Network Optimization
Presenters:
Haijun Zhang (University of Science and Technology Beijing, China); Yansha Deng (King's College London, United Kingdom (Great Britain)); A Nallanathan (QMUL, United Kingdom (Great Britain))
Abstract:
This tutorial will identify and discuss technical challenges and recent results related to deep learning and reinforcement learning-based future wireless networks. The tutorial is mainly divided into four parts. In the first part, we will introduce future wireless networks and AI, discuss the future wireless networks architecture, and provide some main technical challenges in AI-based future wireless networks. In the second part, we will focus on the issue of AI-based resource management in future wireless networks and provide different recent research findings that help us to develop engineering insights. In the third part, we will address the signal processing and PHY layer design of AI-based future wireless networks and address some key research problems. In the fourth part, we will focus on AI-assisted MAC control for IoT networks. In the fifth part, we will focus on the reinforcement learning solutions for wireless virtual reality networks. In the last part, we will summarize by providing a future outlook of AI-based future wireless networks.
TUT-13: Intelligent Reflecting Surface for Wireless Communications: Fundamentals, Designs, and Open Issues
Presenters:
Rui Zhang (National University of Singapore, Singapore); Emil Björnson (Linköping University, Sweden)
Abstract:
In this tutorial, we introduce a new promising paradigm for 6G by leveraging a massive number of low-cost passive elements with independently controllable reflection amplitude and/or phase, named Intelligent Reflecting Surface (IRS), which is able to smartly reconfigure wireless channels for enhancing the communication performance. The tutorial consists of two parts. In the first part, we present the signal and channel models of IRS by taking into account its hardware constraints in practice. We then illustrate the main functions and applications of IRS in achieving spectral and energy efficient wireless networks, and highlight its cost and performance advantages as compared to existing wireless technologies such as ultra-dense network, massive MIMO, etc. We also present the results on recently developed prototypes and conducted experiments on IRS as well as its related industry activities. Next, in the second part, we focus on the main design challenges in efficiently integrating IRSs into future wireless networks such as 6G, including passive reflection optimization, IRS channel acquisition and IRS deployment, as well as overview their start-of-the-art solutions in both narrowband and broadband systems. In particular, we will emphasize on the latest results on multi-IRS aided wireless networks, beyond the conventional single IRS for point-to-point passive relaying. We will also discuss other extensions such as active IRS, refractive IRS, etc. and point out directions worthy of further investigation in the future.
TUT 14: Signal Processing for Converged Terahertz Communications and Sensing
Presenters:
Hadi Sarieddeen and Mohamed-Slim Alouini (King Abdullah University of Science and Technology (KAUST), Saudi Arabia); Tareq Y. Al-Naffouri (King Abdullah University of Science and Technology, USA)
Abstract:
Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications at the intersection of communications, localization, and sensing. Recent advancements in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and the corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress paves the way for well-grounded research into THz-specific signal processing techniques for wireless communications and sensing, as the authors detail in [1]. This tutorial overviews these techniques, emphasizing ultra-massive multiple-input multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, which are vital to overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.
TUT-15: Authentication Protocols for Next Generation Wireless Networks
Presenters:
Yi Qian (University of Nebraska - Lincoln, USA)
Abstract:
Authentication is a process for a system to verify the identity of a user who wishes to access the system. In a mobile wireless communication network, authentication is the first security mechanism that always be deployed to control the access of the network, so that only legitimate users or devices can be allowed to access the network and to use the services. This tutorial presents the evolutions of the authentication protocols for mobile wireless communication networks. It starts with the 2nd generation GSM authentication protocol, to 3rd generation UMTS and 4th generation LTE authentications. The tutorial will conclude with an overview for the next generation 5G and beyond system authentication approaches. The history of development of authentication protocols and lessons learnt for wireless network security designs will be presented.
TUT-16: Universal Decoding by Guessing Random Additive Noise Decoding
Presenters:
Muriel Médard (MIT, USA); Rabia T Yazicigil (Boston University, USA); Ken R. Duffy (Hamilton Institute, Maynooth University, Ireland)
Abstract:
Forward error correction decoding has traditionally been a code-specific endeavor. An innovative recent alternative is noise-centric guessing random additive noise decoding (GRAND). Our approach uses modern developments in the analysis of guesswork to create a universal algorithm where the effect of noise is guessed from most likely to least likely. The noise effect is removed from the received signal and the codebook is used simply as a hash check to verify whether the result is in the codebook. The guessing continues until the hash check is correct or the algorithm declares an erasure. This approach provably provides a Maximum Likelihood (ML) decoding for any block code as long as the guesswork order matches the channel statistics. This tutorial will introduce the algorithmic basis of GRAND for universal hard and soft decoding. It will introduce the audience to the evaluation of GRAND's performance in terms of common metrics (such as block error rate, bit error rate, and complexity) for a wide variety of codes, and it will present hardware architectures for GRAND, highlighting themes such as parallelism and pipelining.
TUT-17: 6G Core Networks: New Components and Enabling Technologies
Presenters:
Anwer Al-Dulaimi (EXFO Inc., Canada); Farah Slim (EXFO, France)
Abstract:
Provided with network virtualization, 5G allows the operators infrastructure to become fully automated and scalable chain of hybrid network functions that reside in public and private clouds. This also provides the network automation with northbound API to control the MEC and reconfigure operations considering E2E vision for KPIs. This new form of network modeling optimizes the computational resources and improve network resiliency towards unexpected traffic or sudden failures. This texture of network provides the necessary foundation for a new network generation that monitors end users behavior, analyze requested service and resource availability, instantiates the necessary topology, and migrate network functions for localized service processing. The upcoming 6G will employ the artificial intelligence (AI) for operational and functional operations to achieve a composable network of smaller elements that act proactively to user demands. Both presenters are R&D leaders at EXFO, which is one of the most prominent world companies that develop products and solutions for monitoring, testing, troubleshooting, and automating network infrastructure. Through direct daily engagement with Tier #1 mobile operators, we understand the main requirements for the 6G communications specifically the core network functions and the automation requirements for the network E2E. We would like to share our visions and technical industrial expertise with the wide research community through this tutorial and demonstrate examples of applied technology.
TUT-18: Wireless Information and Energy Transfer in the Era of 6G Communications
Presenters:
Ioannis Krikidis and Constantinos Psomas (University of Cyprus, Cyprus)
Abstract:
Conventional energy-constrained wireless systems such as sensor networks are powered by batteries and have limited lifetime. Wireless power transfer (WPT) is a promising technology for energy sustainable networks, where terminals can harvest energy from dedicated electromagnetic radiation through appropriate electronic circuits.
The integration of WPT technology into communication networks introduces a fundamental co-existence of information and energy flows; radio-frequency signals are used in order to convey information and/or energy. The efficient management of these two flows through sophisticated networking protocols, signal processing/communication techniques and network architectures, gives rise to a new communication paradigm called wireless powered communications (WPC).
In this tutorial, we discuss the principles of WPC and we highlight its main network architectures as well as the fundamental trade-off between information and energy transfer. Several examples, which deal with the integration of WPC in modern communication systems, are presented. Specifically, we study some fundamental network structures such as the MIMO broadcast channel, the interference channel, the relay channel, the multiple-access channel, and ad-hoc networks. The integration of WPC in 6G and beyond is analyzed and discussed through the use of tools from stochastic geometry. Future research directions and challenges are also pointed out.
TUT-19: Reconfigurable Intelligent Surfaces for 6G: Communications, Localization, and Sensing
Presenters:
George C. Alexandropoulos (University of Athens, Greece); Lingyang Song (Peking University, China); Henk Wymeersch (Chalmers University of Technology, Sweden); Zhu Han (University of Houston, USA); Boya Di (Peking University & Imperial College London, China); Hongliang Zhang (Princeton University, USA)
Abstract:
Reconfigurable Intelligent Surfaces (RISs) 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 RISs in wireless networks has been recently advocated as a revolutionary means to transform any naturally passive wireless communication environment to an active one. The goal of this tutorial is to provide attendees with an overview of the state-of-the-art in RIS-empowered communication, localization, and sensing, as well as their promising interactions. In particular, the tutorial has the following four core objectives: i) to detail the various available hardware designs for RISs, their available modeling approaches, and their implications in the design of communication, localization, and sensing algorithms; ii) to present the latest approaches for efficient RIS-enabled communication; iii) to discuss how RIS-enabled networks can enable or boost radio localization; and iv) to detail how RIS can support sensing and radar applications beyond the ability provided by 5G and earlier generations.
TUT-20: Blockchain and Cryptoeconomics for Cyber-physical System and Wireless Networks
Presenters:
Zhu Han and Jing Li (University of Houston, USA); Dusit Niyato (Nanyang Technological University, Singapore); Lei Zhang (University of Glasgow, United Kingdom (Great Britain)); Salil S Kanhere (UNSW Sydney, Australia)
Abstract:
Due to its salient features including decentralization, anonymity, security, trust, and auditability, blockchain has attracted tremendous attention from both academia and industry. The advent of blockchain networks and their applications in various domains, including computer networks, data sciences, and Fin-Tech, create a new field of study for academia and industry: cryptoeconomics. It can extend the analytical framework based on the economic networking perspective to modeling, designing, and analyzing the participant interactions in any ecosystem raised from or built upon blockchain networks.
Therefore, this tutorial first analyzes the participants' behaviors from the economic perspective and presents how the rational/irrational behaviors affect the performance and security of a distributed system. Then, from the engineering perspective, this tutorial shows a series of economic mechanisms and case studies, illustrating that a well-functioning, scalable cryptoeconomics network is able to serve as an efficient platform for decision arbitration and allocation of the resources ranging from physical utilities (e.g., hardware) to financial assets. As a result, cryptoeconomics can shed light on the better characterization of the blockchain-assisted systems.
The cryptoeconomics has the huge potential within the cyber- physical system (CPS), where the more and more devices within are wireless connected. Considering that highly dynamic wireless channel and scarce frequency spectrum, communication can impact the performance of conventional wired blockchain systems in a wireless CPS, we start from presenting wireless blockchain networks (WBN) under various commonly used consensus mechanisms (CMs) and analyzing and demonstrating how much communication resource is needed to run such a network for CPS. In particular, we will answer the following questions:
- What is the role of communication and the procedures in WBN under various commonly used CMs (e.g., PoW, PBFT, Raft), with different network typologies (e.g. mesh, tree, etc.) and communication protocols (e.g. grant-based or contention-based)?
- What is the analytical relationship between blockchain performance and communication resource provision in different communication protocols for different CPS applications?
- How can we use blockchain to solve the most imperative challenges we are facing in typical CPS such as smart vehicle, supply chain, healthcare etc.
TUT-21: Beyond 5G Joint Sensing and Communications
Presenters:
Kumar Vijay Mishra (United States Army Research Laboratory, USA); Bhavani Shankar Mysore R (Interdisciplinary Centre for Security, Reliability and Trust & University of Luxembourg, Luxembourg); Gerhard P. Fettweis (Technische Universität Dresden, Germany)
Abstract:
Today's cellular networks are at a crossroads while moving from the current 4G cellular networks used for content delivery to the upcoming 5G networks that will provide a ubiquitous tactile Internet infrastructure for controlling and steering real and virtual objects. At the same time, a crunch in spectrum usage implies that such high data networks must coexist with the radar sensing systems of the future. The tutorial aims to shed light on sensing-communications coexistence scenarios beyond 5G (B5G) considered thus far. Building on the existing approaches, the tutorial focuses on highlighting emerging scenarios in collaborative and joint sensing and communications systems, particularly at mm-Wave and THz frequencies, highly dynamic vehicular environments, distributed radar-communications networks, and aerial channels, that would benefit from information exchange between the two systems. It presents the architectures, possible methodologies for mutually beneficial co-existence as separate entities or as a joint module and presents some recent results. The avenues discussed in the tutorial offer rich research potential while also enabling innovative plug-and-play methodologies for co-existence and co-design.
TUT-22: Cellular V2X for Connected Automated Driving
Presenters:
Toktam Mahmoodi (King's College London, United Kingdom (Great Britain)); Tommy Svensson (Chalmers University of Technology, Sweden); Mikael Fallgren (Ericsson Research, Sweden); Markus Dillinger (Huawei Technologies Duesseldorf GmbH, Germany)
Abstract:
5G technologies and beyond enable road users and vehicles to be connected to the networks as well as to communicate directly with each other ensuring ultra-high reliability and ultra-low latency. Enabling such kind of connectivity will leverage disruptive new applications, such as cooperative manoeuvre among vehicles, and awareness of and interaction with vulnerable road users, to improve driving experience and boost road safety. This tutorial elaborates on the role of 5G and beyond technologies for the connected vehicles, and various technological advances that foster advances in connected automated driving. The tutorial is shaped primarily based on the outcome of the EC-funded 5GPPP 5GCAR project results, which are presented in numerous academic venues and industry forums and published in peer-reviewed journals and conferences, as well as recent advances. This work is one of the first studies that brought together academia, telecom and automotive industry, from science, technology, business and regulatory perspectives.
TUT-23: Deep Learning in Wireless Security and Privacy for Next-Generation Communication Systems
Presenters:
Koduvayur P Subbalakshmi (Stevens Institute of Technology, USA); Yalin E Sagduyu (Intelligent Automation, Inc., USA); Dola Saha (University at Albany, SUNY, USA)
Abstract:
This tutorial will cover three important aspects of machine/deep learning (M/DL) in wireless security and privacy: (i) federated learning (FL) (ii) adversarial machine learning (AML) and (iii) secure waveform generation for next generation communication networks. FL emerged as a viable alternative to deal with the disparate computation and resource issues encountered in wireless communications and is considered to be a good vehicle for preserving privacy. However, recent works have demonstrated privacy leakages and security concerns in FL. This tutorial will provide a fundamental understanding of FL in wireless communications and discuss challenges and solutions to privacy and security. While ML finds diverse applications in emerging wireless technologies, a new security threat arises due to AML that aims to exploit the vulnerabilities of ML to adversarial manipulations. AML presents a new attack surface on the training and inference processes of ML by manipulating the (training/test) input data and leaking information about the underlying model and training data. As a stealthy attack mechanism with small spectrum footprint, the application of AML to the wireless domain will be discussed in this tutorial with motivating examples from waveform design, signal classification and spectrum sharing that extend to 5G and beyond communication systems. We will then discuss how we can jointly optimize encryption/decryption and modulation/demodulation by minimizing mutual information between legitimate user and adversary through adversarial learning. We will discuss secure waveform generation, fake signal generation (spoofing) and efficient hiding of signals using adversarial learning.
TUT-24: Nano-scale Terahertz based Communication: Opportunities, Challenges, and Recent Advances
Presenters:
Qammer H Abbasi (University of Glasgow, United Kingdom (Great Britain)); Akram Alomainy (Queen Mary University of London, United Kingdom (Great Britain)); Najah A. Abu Ali (UAEU, United Arab Emirates); Yansha Deng (King's College London, United Kingdom (Great Britain)); Muhammad Ali Imran (University of Glasgow, United Kingdom (Great Britain))
Abstract:
Advancement in nanotechnology has made it possible to manufacture sensors, circuits and devices measuring only nano-meters in size. This development is creating an extraordinary opportunity to observe, interact, and optimize physical systems from the very bottom. Wireless communication and networking at nanoscale, however, faces new challenges not encountered in conventional sensor networks. For example, nanoscale antenna calls for wireless communication in the Terahertz band, which encounters new path loss d noise phenomena posing significant challenges for many target applications of such networking. Nanoscale computing and communication is a new and rapidly growing field of research promoting collaboration between wireless networking, nanotechnology, and other fundamental disciplines. However, the research is in its early stages to realize communication and networking at the nanoscale. Currently, there is no definitive standard that provides guidelines and regulation for nanoscale communication and networking. This motivates this proposal to shed light on and promote this area of research and foster.
TUT-25: NB-IoT over Aerial and Space Networks: Technology Overview, Challenges, and Potential Solutions
Presenters:
Carla Amatetti (Alma Mater Studiorum University of Bologna, Italy); Matteo Conti, Alessandro Guidotti and Alessandro Vanelli-Coralli (University of Bologna, Italy); Mustafa A Kishk (King Abdullah University of Science and Technology, Saudi Arabia); Mohamed-Slim Alouini (King Abdullah University of Science and Technology (KAUST), Saudi Arabia)
Abstract:
The last few years have seen two fundamental events take hold in the world of telecommunications: on the one hand the urgency to serve the continuously growing Machine Type Communications (MTC) and Internet of Things (IoT) services, with millions of connected device, on the other hand the necessity to extend and complement the terrestrial network (TN) in un- or under-served area, through the integration of Non Terrestrial Networks (NTN) (including aerial and space networks). In Rel. 17, The Third Generation Partnership Project (3GPP) initiated a new Study Item about the integration of Narrowband IoT (NB-IoT) over NTN to assess the problem related to the NB- IoT air interface adaptation at NTNs characteristics. In this context, the tutorial aims at describing the NB-IoT NTN systems characteristics and their pivotal role in the evolution of the MTC/IoT markets. Focusing on NB-IoT, the 3GPP standardization process for its integration in NTN architecture will be thoroughly explained and the challenges posed by the drones and satellites based solutions. Some solutions will be proposed to overcome these problems and challenges. The objective of the tutorial is to help the readers to easily understand the potential of NB-IoT integration over NTN shedding light on the 3GPP standardization process and on the current State of the Art of techniques related to this topic.
TUT-26: Towards 6G V2X for Connected and Automated Vehicles
Presenters:
Alessandro Bazzi (University of Bologna, Italy); Antonella Molinaro (University Mediterranea of Reggio Calabria, Italy); Antoine O. Berthet (CentraleSupélec, Université Paris-Saclay, France)
Abstract:
In the last decade, there has been a surge of interest in connected and automated vehicles (CAVs) and related enabling technologies in the field of communication, automation, sensing, and positioning, which are expected to revolutionize future transportation and quality of life. By leveraging novel efficient air interfaces, sophisticated transceivers design, revised resource allocation procedures, and higher frequencies as well as softwarization technologies, fifth generation (5G) systems will surely be the game-changer to guarantee ultra-low latency, ultra-high reliability, high-data rate vehicle-to-everything (V2X) connectivity. In this tutorial, the status quo of V2X-related research, development and standardization activities will be first reviewed. Special attention will be given to the sidelink cellular-based V2X (CV2X) communication technology promoted by 3GPP, its 5G New Radio based evolution (e.g., through flexible numerology or new resource allocation mechanisms), and the perspectives towards 6G. The experience of the instructors concerning the ongoing standardization processes about V2X and the performance evaluations of the C-V2X technologies will be shared, as well as proposed extensions for their performance improvements. Speculation about research for V2X enhancements in the direction of 6G (beyond-OFDM modulations/waveforms, advanced multiple access schemes and in-band full-duplex transceiver technology) along with presentation of related results will also be part of the tutorial.
TUT-27: Age of Information: A Paradigm Shift Towards Semantic Properties in Data Science
Presenters:
Ali Maatouk (CNRS-L2S, France); Mohamad Assaad (CentraleSupelec, France); Anthony Ephremides (University of Maryland, USA)
Abstract:
The 5G networks have radically transformed cellular networks from a purely data-oriented architecture to a service-based architecture. The new architecture promises the support of a diverse set of verticals enabled by carefully tailored network resources and capacities. The next-generation networks are expected to incorporate an even more extensive set of applications and services such as the tactile internet, interactive hologram, and intelligent humanoid robot. These shifts allow us to call into question the fundamental semantics-blind approach to communication so far prevalent in today's wireless systems. While traditional communications overlook the semantics of the data, i.e., the purpose of using the data, in the design of the communications schemes, future communication schemes should be conceived and optimized by taking into account the semantics of the data.
Among the various possible facets of data semantics, the information freshness captured through the Age of Information (AoI) has been recently proposed and has gained significant research attention. The AoI measures the information time-lag at the receiver side, and accordingly, its minimization is regarded as a means to achieve the freshness of information. This tutorial introduces the main fundamental characteristics of this metric and highlights how it compares to the standard optimization frameworks: delay minimization and throughput maximization. The tutorial also discusses how the introduction of AoI can impact the design/optimization of various techniques such as multiple access, scheduling, and random access. Additionally, it reports recent results investigating other facets of semantics beyond information freshness and discusses the potential research directions in this area.
TUT-28: Tools and Techniques for Future Spectrum Sharing and Coexistence
Presenters:
Constantinos B. Papadias (The American College of Greece, Greece); Tharmalingam Ratnarajah (The University of Edinburgh, United Kingdom (Great Britain)); Dirk Slock (EURECOM, France)
Abstract:
Wireless spectrum is a scarce commodity in today's connected and data-hungry world, where demand for higher data rates is increasing exponentially on a daily basis. Beyond 5G cellular systems are venturing more and more into unlicensed spectrum, leading to increased coexistence of a multitude of wireless systems. To tackle this coexistence, efficient spectrum utilization techniques, such as spectrum sharing (SS) and full-duplex (FD) transmission have been considered. While SS can substantially increase the spectrum utilization efficiency by allowing licensed and unlicensed users to share the spectrum, FD radios have the potential to double the spectrum efficiency of current half-duplex links by transmitting and receiving at the same time and frequency resources. Furthermore, they allow simultaneous transmission and sensing, opening up avenues for new random-access schemes. The objective of this tutorial is to provide an overview of the following ingredients: 1) Key SS approaches (from cognitive radio to eLSA, CBRS, unlicensed access in 3GPP, etc.); 2) Enabling SS techniques (spectrum sensing, cooperative communications, antenna arrays, resource allocation, etc.); 3) New trends (FD, radar-communications, SS in mmWave, machine learning-based spectrum monitoring, etc.). This tutorial is based on (but goes beyond) our recent edited book: Spectrum Sharing: The Next Frontier in Wireless Networks.
TUT-29: Machine Learning for MIMO Systems with Large Arrays
Presenters:
Nuria González-Prelcic (North Carolina State University, USA); Aldebaro Klautau (Universidade Federal do Para, Brazil); Robert Heath (North Carolina State University & The University of Texas at Austin, USA)
Abstract:
MIMO communication remains a key technology in current and future cellular systems. With each evolution, e.g. 11n to 11ay or 4G LTE to 5G NR, the number of antennas, ports, and users who may be served simultaneously increases. At the same time, the complexity of configuring those antennas increases, especially for higher bandwidths and mmWave / THz carriers. Machine learning (ML) provides a data-driven solution to configuring parameters in a MIMO system. In this tutorial, we review the fundamentals of ML both shallow and deep, and introduce important problems in massive MIMO, mmWave MIMO and THz MIMO communications. We explain how and why a particular ML tool is suitable for a certain communications problem with specific examples, inspired by problems in 5G and beyond (B5G).
TUT-30: Networking and Communications for Intelligent and Connected Vehicles in 5G and Beyond
Presenters:
Jiajia Liu (Xidian University, China); Nei Kato (Tohoku University, Japan)
Abstract:
The development of LIDAR, Radar, camera, and other advanced sensor technologies inaugurated a new era in autonomous driving. However, due to the intrinsic limitations of these sensors, autonomous vehicles are prone to making erroneous decisions and causing serious disasters. At this point, the 5G and beyond networking and communication technologies can greatly make up for sensor deficiencies, and are more reliable, feasible and efficient to promote the information interaction, thereby improving autonomous vehicle's perception and planning capabilities as well as realizing better vehicle control. We provide in this tutorial a comprehensive review of recent research works concerning the networking and communication technologies in autonomous driving from two aspects: intra- and inter-vehicle. The intra-vehicle network as the basis of realizing autonomous driving connects the on-board electronic parts. The inter-vehicle network is the medium for interaction between vehicles and outside information. In addition, we present the new trends of communication technologies in autonomous driving, as well as investigate the current mainstream verification methods and emphasize the challenges and open issues of networking and communications in autonomous driving.
TUT-31: QoS-Driven 6G Promising Techniques Over Multimedia Mobile Wireless Networks
Presenters:
Xi Zhang (Texas A&M University, USA)
Abstract:
While 5G is being deployed around the world, the efforts and initiatives from academia, industry, standard bodies have started to look beyond 5G, conceptualize 6G mobile wireless networks, and propose various 6G promising candidate techniques. While it is widely recognized that various multimedia services such as video/audio streaming and even 3D immersive-media (e.g., XR - AR/MR/VR) will continue dominating the wireless traffics in 6G networks, how to efficiently support statistical delay and error-rate bounded QoS provisioning for wireless multimedia transmissions over 6G remains one of the most difficult challengers because real-time big-data multimedia services are both highly spectrum-/computation-intensive and time-sensitive, for which the deterministic delay-bounded guarantee is practically infeasible due to randomly time-varying wireless channels and interferences. To overcome these difficulties, the academia and industry have made a great deal of efforts in developing various 6G promising candidate techniques from the perspectives of theory, architectures, protocols, techniques, etc. Towards this end, in this speech we will address the 6G's fundamental pillar techniques, including information-centric network (ICN), network functions virtualization (NFV), and software defined networks (SDN), Edge Artificial Intelligence (Edge-AI), Cell-Free massive MIMO (CF m-MIMO), Finite Blocklength Coding (FBC), Terahertz (THz) Wireless Nano-Networks, Unmanned Aerial Vehicle (UAV), Intelligent Reflecting Surface (IRS), etc., and how these techniques can be integrated to efficiently support the statistical delay and error-rate bounded QoS provisioning for wireless multimedia transmissions over 6G mobile wireless networks. Furthermore, we will also discuss several future research directions and challenges in the general areas for 6G mobile wireless networks.
