In addition to exciting technical symposia, tutorials, industry panels and exhibitions, WCNC 2021 will feature a series of half and full-day workshops. The Goal of the WCNC 2021 workshops is to exchange ideas in emerging topics not covered in the main symposia. The workshops will feature topics related to technical and business issues in wireless communications and networking, and other topics ranging from technology issues to emerging applications.
Please address all questions to the IEEE WCNC 2021 Workshops Chairs:
- Yi Qian, University of Nebraska (firstname.lastname@example.org)
- Honggang Zhang, Zhejiang University (email@example.com)
- Deadline for Workshop Paper Submission: 22 December 2020 (Extended)
- Acceptance/Rejection Announcement: 20 January 2021
- Final Workshop Papers Due: 8 February 2021
- WS1: IEEE WCNC 2021 Workshop on Reconfigurable Intelligent Surfaces for 5G and Beyond
- WS2: IEEE WCNC 2021 Workshop on Distributed Machine Learning for Future Communications and Networking
- WS3: IEEE WCNC 2021 Workshop on ICDT Converged Beyond 5G Networks
- WS4: IEEE WCNC 2021 Workshop on Computing First Networking
- WS5: IEEE WCNC 2021 Workshop on Intelligent Computing and Caching at the Network Edge
- WS6: IEEE WCNC 2021 Workshop on Energy-Efficient Schemes for Beyond 5G
- WS7: IEEE WCNC 2021 Workshop on Internet of Integrated Space-Terrestrial Intelligent Systems and Applications
- WS8: IEEE WCNC 2021 Workshop on Rate-Splitting (Multiple Access) for Beyond 5G
- WS9: IEEE WCNC 2021 Workshop on Integrated Sensing (Radar) and Communications: From Spectrum Sharing to Joint Transmission
- WS10: IEEE WCNC 2021 Workshop on Short-Packet Communication for Beyond 5G Wireless Networks
- Marco Di Renzo, CentraleSupelec, Paris-Saclay University, France
- Yonghui Li, School of Electrical and Information Engineering, The University of Sydney, NW, Australia
- Hongliang Zhang, EE Department, Princeton University, NJ, USA
- Boya Di, Department of Computing, Imperial College London, UK
- Miaowen Wen, South China University of Technology, China
- Beixiong Zheng, Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Zhu Han, ECE Department, University of Houston, TX, USA
Abstract: Launching from fifth-generation communications which provide a single platform enabling a variety of data services, evolution towards sixth-generation (6G) has been kicked off, envisioning future wireless networks to become distributed intelligent communications, sensing, and localization system. Though such demand has gained support from existing techniques such as massive MIMO and small cells, they heavily depend on the quality of the uncontrollable wireless environments. Differently, reconfigurable intelligent surfaces (RIS), also referred to as Intelligent Reflecting Surfaces (IRS), are a new type of ultra-thin meta material inlaid with multiple sub-wavelength scatters, which can create favorable propagation conditions by controlling the phase shifts of the reflected waves at the surface such that the received signals are directly reflected towards the receivers without any extra cost of power sources or hardware. The RISs have given rise to an emerging concept of “smart radio environments”. In contrast to conventional wireless networks where the environment is out of the control of the operators, the “smart radio environments” refer to the wireless networks where the environment is turned into a smart reconfigurable space by holographic beamforming that play an active role in transferring and processing information. Against this background, the RIS has been considered as a promising and transformative technology that has the potential of fundamentally changing how wireless networks are implemented and optimized in current days.
Thus, this proposed half-day workshop will seek to bring together researchers and experts from academia, industry, and governmental agencies to discuss and promote the research and development needed to overcome the major challenges that pertain to this cutting-edge research topic. Particularly, the scope of the workshop focuses on (but is not limited to) six major lines of fundamental research and development on RIS for future communication networks:
- Physical modeling for RIS: the electromagnetic-compliant modeling of the RIS and the channel modeling of the RIS involved communications.
- Fundamental performance limits of RIS-assisted wireless networks: exploiting channel estimation in multi-antenna networks, multi-user detection in multi-cell networks, and joint codebook and decoding strategy with the assistance of RIS.
- Integrations of RIS and state-of-art wireless technologies: integrating RIS into state-of-art technologies, such as millimeter-wave communications, device-to-device communications, and Internet of Things, etc.
- Algorithms and protocols design for RIS-based wireless networks: new communication and signal processing techniques to handle the coupling between the radio propagation and the configurations of the RIS.
- AI-inspired control of RIS-empowered wireless networks: utilizing the power of machine learning to control the configurations of the RISs where the relations between the configurations and the electromagnetic response of the RIS is complicated and challenging to model.
- Emerging applications of RIS: extentive RIS based applications in wireless networking, e.g., RF sensing and localization.
WS2: IEEE WCNC 2021 Workshop on Distributed Machine Learning for Future Communications and Networking
- Mérouane Debbah, CentraleSupélec, Gif-sur-Yvette, Huawei France Research Center and Mathematical and Algorithmic Sciences Lab, France
- Dusit Niyato, Nanyang Technological University, Singapore
- Petar Popovski, Aalborg University, Denmark
Workshop Organizing Chairs:
- Zehui Xiong, Nanyang Technological University, Singapore
- Mingzhe Chen, Princeton University, USA
- Junshan Zhang, Arizona State University, USA
- Jiawen Kang, Nanyang Technological University, Singapore
- Walid Saad, Virginia Tech, USA
- Zhaohui Yang, King’s College London, UK
- Mehdi Bennis, University of Oulu, Finland
- Shuguang Cui, The Chinese University of Hong Kong, Shenzhen, China
- Tony Q.S. Quek, Singapore University of Technology and Design, Singapore
- Yang Zhang, Wuhan University of Technology, China
- Helin Yang, Nanyang Technological University, Singapore
- Kun Zhu, Nanjing University of Aeronautics and Astronautics, China
Abstract: With the widespread deployment of Internet of Things, a wide range of modern data-intensive applications are emerging to enrich our daily life. Empowered by the rise of advanced machine learning (ML) techniques, intelligent decisions based on sensing data significantly improve the application performance and give rise to a large number of innovative applications and intelligent services, which will become a crucial component for future communications and networking, e.g., 5G, 6G, and beyond. It is expected that the ML technologies will be adopted in many fields, and the ubiquitous artificial intelligence (AI) will empower promising future communications and networking. However, user data are usually stored in the end devices, which leads to higher-level requirements in aspects of resources, security, and privacy protection when employing ML techniques. In particular, most of the traditional learning techniques for wireless networks perform centralized data aggregation and inference operations on a single data center, which are suffering from critical security challenges, e.g., single point of failure. To this end, distributed ML solutions as an important direction for learning techniques enable the wireless devices to collaboratively build a shared learning model with training their collected data locally. These solutions have high potential to enable ubiquitous, efficient, and secure AI for future communications and networking.
The goal of this workshop is to bring together researchers and practitioners interested in distributed machine learning to understand the topic, identify technical challenges, and discuss potential solutions. Topics of interest include but are not limited to the following:
- Novel concept, theory, principles, and algorithms on the convergence of distributed ML and future communication or network techniques
- Intelligent radio resource management for future communications and networking
- Distributed ML for intelligent signal processing, e.g., signal detection
- Over-the-air access for future communications and networking
- Energy efficiency of implementing distributed ML over future wireless communications and networking
- Ultra-low latency distributed ML for latency-sensitive future communications and networking
- Data analytics driven wireless communications with distributed ML
- Multi-agent reinforcement learning for intelligent network control and optimization
- Network architectures, and communication protocols for distributed ML
- Privacy and security issues of distributed ML for future communications and networking, e.g., physical layer security.
- Distributed ML for mobile user behaviour analysis and inference
- Distributed ML for emerging applications, e.g., vehicle to everything (V2X), UAV-enabled communication, Internet of Things, intelligent reflecting surface (IRS), Massive MIMO, virtual reality (VR), and augmented reality (AR)
- Wireless network optimization for improvingperformance of distributed ML
- Data compression for distributed ML in future communications and networking
- Adaptive control and transmission for distributed ML in future communications and networking
- Emerging theories and techniques such as age of information, blockchain, and edge computing for distributed ML
The workshop will feature three keynote speeches given by world leading researchers in the field. The workshop accepts only original and previously unpublished papers. All submissions must be formatted in standard IEEE camera-ready format (double-column, 10pt font). The maximum number of printed pages is six including figures without incurring additional page charges (6 pages plus 1 additional page allowed with a charge for the one additional page of USD 100 if accepted)
- Prof. Ping Zhang, Beijing University of Posts and Telecommunications
- Prof. Jiangzhou Wang, University of Kent
- Guangyi Liu, China Mobile (The lead organizer)
Abstract: 5G design fully embodies ICT integration idea and cloud native concepts, introduces SDN / NFV and service based architecture, as well as technologies such as CP/UP separation, network slicing and edge computing, so as to realize the differentiated technical scenarios, and provide a powerful capability for a rapid and elastic network deployment and function upgrade. However, with the accelerated development of ICDT, especially the penetration of AI, there’s been an explosion of intelligent end-to-end information processing requirements. A large number of autonomous things (UAV, autonomous vehicles, robots, etc.) and virtual spaces (AR/VR, digital twin, intelligent space, hologram, etc.) are emerging. The interconnection requirements of autonomous things and virtual spaces with/through mobile network are challenging which highlights the shortcomings and bottlenecks of 5G from the following aspects: 1) the 5G SBA architecture is not fully realized, which leads to a complex network operation; 2) part of network functions are redundant, which increases the network cost; 3) the AI capability is patched and not fully utilized networkwide; 4) and the multi-dimensional resources such as communication, computing and storage are relatively isolated and unable to be globally scheduled.
To meet the ultimate experience requirements of intelligent applications, mobile network needs further innovation/evolution in an ICDT converged framework. The core capability of mobile network will extend from information delivery to end-to-end information processing. All the heterogeneous terminals and network nodes will participate in the information processing, involving information acquisition, delivery, computing, storage, utilization and security. This requires the mobile network to have extreme capabilities of communication, computing, intelligence and security, as well as the enhanced capabilities such as sensing and 3D positioning. This in turn requires an extremely simple network architecture with the autonomous operation and maintenance capabilities.
Outlining the solutions for a simple ICDT converged beyond 5G network architecture with much more functionalities is a rather challenging task but also an interesting work. Recently, preliminary viewpoints and thoughts on beyond 5G network concept, possible architecture and potential technologies have been reported from both academia and industry. However, there are still more work needed to identify what beyond 5G networks will be. The proposed workshop aims to look for some deep insight into the beyond 5G concept, theories, features, network architecture, functions, and potential technologies.
Topics of interest include (but not limited to) the following:
- Scientific problems on the ICDT convergence and fundamental theories
- Systemic design for the integration of computing, communication, and sensing
- ICDT converged Beyond 5G network architecture
- Native AI for ICDT converged Beyond 5G networks
- Native security for ICDT converged Beyond 5G networks
- Deterministic network technologies
- Enhanced radio connection technology
- Lite network technologies
- Open network architecture and ecosystem
- Digital twin network and technologies
- Space-air-ground integrated network
- Network technologies for digital twin body
- Energy-efficient end-to-end information processing
- ICDT converged wireless transmission
- Yunjie Liu, Purple Mountain Laboratories, Nanjing, P.R. China
- Victor C. M. Leung, Shenzhen University/The University of British Columbia, BC, Canada
- F. Richard Yu, Carleton University, ON, Canada
- Tao Huang, Beijing University of Posts and Telecommunications, P.R. China
- Renchao Xie, Beijing University of Posts and Telecommunications, P.R. China
Abstract: Edge computing is emerging as a promising computing paradigm by deploying computing and storage resources at the edge of the network and providing cloud computing service environment and capability for end users. It can greatly reduce the delay of access to network content and computing services, which is of great significance to improve network performance and the quality of user experience. However, as the massive deployment of edge computing, it also brings many problems. On the one hand, the computing resources of a single edge computing node are limited, and it is difficult to process computing tasks efficiently and quickly. On the other hand, there is no effective cooperation mechanism between edge computing nodes as well as between edge computing nodes and cloud computing nodes, which results in low utilization of computing resources. To address these issues, Computing First Networking (CFN) has been proposed to enhance the collaboration between edge computing nodes and improve the utilization of computing resources. Compute First Networking can leverage both computing and networking status to help determine the optimal edge computing node among multiple edge computing nodes with different geographic locations to serve a specific edge computing request. And these requests for the same service can be determined and dispatched to different edge computing nodes based on service requirements, network and computing resource conditions and other factors to achieve better load balancing and system efficiency. Compute First Networking has been recognized as one of the promising techniques for future networks.
Based on the above observations, the Workshop on “Computing First Networking” provides a forum for researchers to discuss the developments of Computing First Networking, and brings together industry and academia, engineers and researchers. The workshop invites submissions of the unpublished works on the following topics (but not limited to):
- Architecture design for Computing First Networking
- Protocol design for Computing First Networking
- Service discovery and task scheduling for Computing First Networking
- Serverless Computing for Computing First Networking
- Edge Computing, Fog Computing, Cloudlet, Cloud Computing
- Ubiquitous convergence of computing, network and storage
- Deterministic Networking for Computing First Networking
- Information-Centric Networking, Named Data Networking, Content-Centric Networking for Computing First Networking
- Blockchain for Computing First Networking
- Software defined networks for Computing First Networking
- Machine learning and deep learning enabled Computing First Networking
- Automation and optimization for integrated networking, caching and computing
- QoS provisioning and resource management for Computing First Networking
- Energy-efficiency for Computing First Networking
- Computing First Networking in Satellite Internet
- Computing First Networking in Internet of vehicles
- Computing First Networking in Industrial Internet
- Performance evaluation for Testbed and prototype implementation for Computing First Networking
- Luiz DaSilva, Trinity College Dublin, Ireland,
- Zhisheng Niu, Tsinghua University, China
- Sheng Zhou, Tsinghua University, China
- Zheng Chang, University of Jyväskylä, Finland
- Jie Gong, Sun Yet-Sen University, China
- Jie Xu, University of Miami, U.S.
- Zhiyuan Jiang, Shanghai University, China
Abstract: With the explosive growth of smart devices and the advent of many new applications, mobile traffic volume has been growing exponentially. The myriad technological advances proposed for the 5G networks still mostly focus on capacity increase, which is constrained by the limited spectrum resources as well as the diminishing profits for operators and, therefore, will always lag behind the growth rate of mobile traffic. Therefore, novel distributed architectures, which bring network functions (such as computing and caching) and contents to the edge, emerges, i.e., mobile edge computing and caching, to confront the aforementioned challenges in the network development and many emerging applications, such as AR/VR, IoT, eHealth, autonomous driving , gaming etc. However, on the way towards efficient and intelligent network edge computing and caching, there are many open problems ahead. From the computing part, how to flexibly utilize the distributed computing resources at the network edge, such as mobile computing or fog computing is of significance. Moreover, what to be offloaded to the edge node and when to offload also call for research attentions. From the caching part, what, when, where and how to cache the contents to reduce the demand for radio resources are vital. Last but not the most, how to efficiently integrate computing and caching at the edge node and utilize the synergy of computing and caching also requires a breakthrough. This workshop aims to consolidate the timely and solid works of the current state-of-the art in terms of fundamental research ideas and network engineering geared towards exploiting intelligent caching and computing at the network edge. The topics of interests related to edge caching and computing include (but are not limited to):
- System modelling: Computation modelling, caching modelling, energy consumption modelling etc.;
- Enabling technologies: e.g., SDN, NFV, CRAN, D2D, cloud/fog computing and networking, etc.;
- Application areas: vehicular networking, IoT, smart grid etc;
- Novel network architecture: convergence of computing, communications and caching, content/information-centric network, cognitive computing and networking, big data analytic;
- Context-aware schemes: incentive mechanism for computing and caching, pricing, game theoretic approach, network economic etc, caching placement and delivery;
- Mobility management for mobile edge computing and proactive caching;
- Energy efficiency aspects: energy harvesting, energy storage, energy transfer, etc;
- Security and privacy issue;
- Prototyping, test-beds and field trials.
- Alagan Anpalagan, Department of Electrical, Computer and Biomedical EngineeringRyerson University, Toronto, Canada
- Zhiguo Ding, School of Electrical and Electronic Engineering, University of Manchester, United Kingdom
- George K. Karagiannidis, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki
- Jules M. Moualeu, School Electrical and Information Engineering, University of the Witwatersrand, South Africa
- Daniel Benevides da Costa, Department of Computer Engineering, Federal University of Ceará, Brazil
Abstract: The ongoing deployment of the fifth generation (5G) networks is continuously revealing its inherent limitations as a true carrier of the Internet of Everything (IoE) applications. To overcome these challenges, beyond 5G (B5G) networks have recently attracted a lot interest in the research community with the aim of truly integrating applications ranging from autonomous systems and extended reality. However, common to 5G, B5G will connect massive number of wireless terminals/devices (sensors, drones, cars, etc…) leading to a massive volume of traffic and energy consumption. Energy efficiency has become an important design objective for the successful deployment of B5G wireless networks. Hence, there is a great need to develop energy efficient architectures and transmission techniques/protocols that extend the lifetime of networks and provide significant energy savings under the aegis of green radio communications. In this regard, further efforts are essential in the quest to explore this critical issue in the context of key emerging technologies for B5G such as intelligent reflecting surfaces (IRS), visible light spectra, Terahertz communications, massive machine-type communications (mMTC) just to name a few.
WS7: IEEE WCNC 2021 Workshop on Internet of Integrated Space-Terrestrial Intelligent Systems and Applications
- Min Jia, Harbin Institute of Technology, China
- Tomaso De Cola, German Aerospace Center (DLR), Germany
- Chunxiao Jiang, Tsinghua University, China
- Ruidong Li, National Institute of Information and Communications Technology (NICT), Japan
Abstract: There will be more requirements for human exploiting space and terrestrial ecosystems as enormous connections of devices or sensors with different applications, such as intelligent transportation systems, environmental monitoring, security surveillances, smart homes, internet of satellite and space information networks, unmanned border awareness systems and other applications, eg. Vehicle communications and unmanned aerial vehicle. Internet of integrated space-terrestrial intelligent (STI) systems and applications will take the burden of massive data generated by different kinds of terminals and sensors. And it shows that intelligent and integrated STI are the key metrics which are changing the society and bringing evolution of techniques in contrast to the conventional people’s lives. However, it is still difficult to predict the exact future internet of STI systems. It is still a great challenge to conduct the integrated intelligent sensing, navigation, computing, and assessment to satisfy the requirements of the green communications and lower the cost of the future internet of STI systems.
The topics of this workshop that may apply intelligence to STI to solve the integrated and hybrid problems of networks and systems toward the mentioned applications include, but not limited to:
- Integrated or hybrid architectures for internet of STI systems
- Innovative protocols and network for future integrated or hybrid architectures
- Machine learning/deep learning aided integrated or hybrid network architectures
- Spectrum sensing and spectrum sharing for cognitive self-organization networks
- Smart and fast data processing techniques
- Massive access scheme for mobile users with large scale mobility
- Intelligent self-resource management and self-control
- Sensing and cognition techniques
- Green IoT techniques and applications in STI systems
- Co-existence and inferences suppression of various wireless systems
- Metrics for dynamic spectrum monitoring, measurements and analysis
- Reconfigurable RF components
- Edge computing
All accepted papers will be published in the proceeding of the WCNC workshop and made available through IEEE Xplore.
- Prof. Bruno Clerckx, Imperial College London, UK
- Prof. Daniel Benevides da Costa, Federal University of Ceará (UFC), Sobral, Brazil
- Dr. Yijie Mao, Imperial College London, UK
Abstract: Numerous techniques have been developed in the past decade for wireless networks, including among others MU-MIMO, CoMP, Massive MIMO, NOMA, millimetre wave MIMO, relay. All those techniques rely on two extreme interference management strategies, namely fully decode interference and treat interference as noise. Indeed, while NOMA based on superposition coding with successive interference cancellation relies on strong users to fully decode and cancel interference created by weaker users, MU-MIMO/Massive MIMO/CoMP/millimetre wave MIMO based on linear precoding rely on fully treating any multi-user interference as noise.
In this workshop, we argue that to efficiently cope with the high throughput, reliability, heterogeneity of Quality-of-Service (QoS), and massive connectivity requirements of future multi-antenna wireless networks, multiple access and multiuser communication system design need to depart from those two extreme interference management strategies, namely fully treat interference as noise (as commonly used in 4G/5G, MU-MIMO, CoMP, Massive MIMO, millimetre wave MIMO) and fully decode interference (as in NOMA).
This workshop focuses on a more general and powerful transmission framework based on Rate-Splitting (RS) that splits messages into common and private parts and enables to partially decode interference and treat remaining part of the interference as noise. This enables RS to softly bridge and therefore reconcile the two extreme strategies of fully decode interference and treat interference as noise and provide room for spectral efficiency, energy efficiency and QoS enhancements, robustness to imperfect channel state information at the transmitter (CSIT), and feedback overhead and complexity reduction. RS provides a powerful framework for the design and optimization of non-orthogonal transmission, multiple access, and interference management strategies. Thanks to its versatility, RS has the potential to tackle challenges of modern communication systems and is a gold mine of research problems for academia and industry, spanning fundamental limits, optimization, PHY and MAC layers, and standardization.
This workshop is dedicated to the theory, design, optimization and applications of rate splitting in many different scenarios relevant to wireless communication and signal processing for beyond 5G. The workshop will give the audience a comprehensive introduction of the state-of-the-art development in rate splitting theory and applications in the wireless communication and signal processing society.
WS9: IEEE WCNC 2021 Workshop on Integrated Sensing (Radar) and Communications: From Spectrum Sharing to Joint Transmission
- Taneli Riihonen, Tampere University, Finland
- Yongzhe Li, Beijing Institute of Technology, China
- Yuanhao Cui, Beijing University of Posts and Telecommunications, China
Abstract: The communications and signal processing societies are welcoming the research trend of integrating radar/sensing with communications as a new era of information technology, which triggers a vast number of applications such as autonomous vehicles, intelligent street lights, wearable Internet of Things (IoT) devices, indoor positioning, next-generation mobile phones, etc. The functionality of radar/sensing and wireless communications, namely, the Integrated Sensing (Radar) and Communications (ISAC), which is also termed as the Joint Radar/Sensing and Communications (JRC), has been evidenced by huge commercial demands in the forthcoming 5G and beyond network. In essence, the ISAC/JRC can be implemented through a synergistic design of communications and radar/sensing systems with shared spectral and hardware resources, wherein the strategies of cohabitation designs and/or dynamic spectrum allocation/access with interference suppression or management are mainly devised. The ISAC/JRC can also be implemented from a co-design perspective, wherein the communications and radar/sensing systems are able to operate in parallel with jointly optimized performance.
The possible strategies, which enable advantages such as lower cost, reduced size, less power consumption, more flexible spectrum usage, etc., are urgently demanded in real applications, especially in the presence of 5G and 6G. However, their studies are still inadequate and relevant solutions and techniques are to be developed. The workshop aims at bringing together researchers from academia and industry in an effort to discuss the major technical challenges, recent breakthroughs, and new applications about ISAC/JRC, from both communication and signal processing perspectives.
- Chih-Lin I, China Mobile Research Institute, USA
- Geoffrey Ye Li, Georgia Institute of Technology, USA
- Guangyi Liu, China Mobile Research Institute, China
- Linglong Dai, Tsinghua University, China
- Jincheng Dai, Beijing University of Posts and Telecommunications, China (firstname.lastname@example.org)
- Chuan Zhang, Southeast University, China (email@example.com)
- Yongpeng Wu, Shanghai Jiao Tong University, China (firstname.lastname@example.org)
- Zhifeng Yuan, ZTE Corporation, China (email@example.com)
Abstract: The fifth generation (5G) wireless communication networks are being standardized and deployed worldwide from 2020. The major communication scenarios of 5G are the enhanced mobile broadband (eMBB) and the ultra-reliable and low-latency communications (uRLLC). Towards the future requirements in 2030+, researchers now start to focus on the beyond 5G (B5G) wireless networks. The mobile Internet and Internet of Everything (IoE) are two drivers for B5G that will support holographic and high-precision communications for tactile and haptic applications (e.g., the tactile Internet). One notable observation in these applications is that the transmit information is control (command) type information (e.g., move, speed up/down) or sensing information (e.g., temperature, moisture, pressure and gas density) so that the amount of information to be delivered is tiny. Such short-packet communication of sporadic nature will dominate the future wireless networks and the conventional centrally-managed wireless network infrastructure will not be flexible enough to keep pace with these demands. Although intensively discussed in the research community, the most fundamental question here on how we will efficiently transmit the short-sized packet remains largely unresolved. A key problem is how to acquire, communicate, and process channel information. In addition, the current wireless transmission strategy designed to maximize the coding gain by transmitting capacity achieving long code-block will be not relevant to these short-packet communication scenarios. Hence, entirely new transmission strategy to support the short-packet communication is required. The field is still at its infancy as there are many open theoretical and practical problems yet to be addressed.
This workshop aims to bring together academic and industrial researchers in an effort to identify and discuss the major challenges and recent breakthroughs related to short-packet communication for B5G wireless networks. Topics of interest include but are not limited to the following:
- Fundamental performance limits for short-packet communication
- New signal transmission paradigms for short-packet communication
- Frame structure design for short-packet communication
- Advanced channel coding for short-packet communication
- Signal modulation design for short-packet communication
- Multi-carrier modulation and waveform design for short-packet communication
- Noncoherent and blind strategies for short-packet communication
- Channel estimation for short-packet communication
- Signal detection for short-packet communication
- Compressed sensing techniques for short-packet communication
- MIMO techniques for short-packet communication
- Coverage enhancement techniques for short-packet communication
- Multiple/massive access protocol design for short-packet communication
- Orthogonal and non-orthogonal multiple access for short-packet communication
- Grant-free data-only access schemes for short-packet communication
- Synchronized and non-synchronized data access for short-packet communication
- Interference avoidance, management, and cancellation techniques for short-packet communication
- Privacy and security guarantee for short-packet communication
- Packet-level coding (network coding) for short-packet communication
- Routing and queuing strategies for short-packet communication
- Machine learning and data analytics for short-packet communication