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At present the O-RAN architecture provides a promising solution of an open-RAN ecosystem, where based on the defined functional splits (CU, DU, RU) a multi-vendor solution can theoretically be achieved. This so called “wave 1.0” 5G that is capable of utilizing only basic (rough) virtualization as well as introducing essential interfaces to enable open-ecosystem, like: E2 for the control of CU/DU/RU as well as A1, O1, O2 for policy based management, network configuration and monitoring. The existing state-of-the art based on IS-Wireless analysis and experiences (also as O-RAN member) should be upgraded to what we call open-RAN Wave 2.0 in order to allow greater flexibility of functional split as well as improve the capability of addressing the challenges of ultra-dense networks. Flexibility of functional splits is essential to adjust open-RAN based networks to the existing infrastructure capabilities including not only fronthaul but also midhaul interfaces. Fronthaul is understood mainly as splits beyond 6 and especially the 7.2 O-RAN split that requires a certain level of capacity, which may be even quadrupled with the split 7.1. In the midhaul e.g. where the CU-CP with RIC (RAN intelligent controller), CORE, MEC and application servers are located, the infrastructure can also vary in capacity. With highly granularized network functions packaged as VNF/CNF (virtual machines of containers) and also providing multitude of split options it is easier to tailor deployment of open-RAN network to fit into available fronthaul and to optimize cost of hardware and network. Moreover, it is then more convenient to orchestrate such “workloads” (i.e. 5G radio stack functions) across edge-cloud continuum, also including edge micro data centers. In this way, multiple split association types can also be achieved naturally e.g. split per slice, per UE, per bearer. The underlying compute resources can also be utilized more efficiently as particular workloads can be fitted to a variety of acceleration cards (GPU, FPGA, SmartNIC) or computer architectures (x86, ARM). Eventually such fine grained, highly composable (orchestrated) disaggregated open-RAN can be called open-RAN Wave 2.0, as it enables achieving higher capacities for network operators who are aiming to address the challenges of ultra-dense networks. Efficient data-driven resource management (both radio and compute) with the novel paradigms like cell-free (or distributed cell-free massive MIMO) are becoming more straightforward to be implemented with such improved open-RAN architectures.
Satellite communication (SatCom) offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. In addition, the integration of SatCom, aerial networks, and terrestrial communications into a single wireless network, called space-air-ground integrated network (SAGIN), is deemed from now on crucial. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. This panel will offer a general overview of emerging and future trends in SatCom. More specifically, the potential of SatCom and then the challenges facing diverse aspects of these systems will be discussed. The panel will also be a forum to debate the various proposed solutions.
As 5G takes to the airwaves, we now turn our imagination to the next generation of wireless technology. The promise of this technology has created an international race to innovate, with significant investment by government as well as industry. And much innovation is needed as 6G aspires to not only support significantly higher data rates than 5G, up to 100 Gbps, but also improved reliability along with excellent coverage indoors and out, including for underserved areas. New architectures including edge computing must be designed to drastically enhance efficient resource allocation while also reducing latency for real-time control. Breakthrough energy-efficiency architectures, algorithms and hardware will be needed so that wireless devices can be powered by tiny batteries, energy-harvesting, or over-the-air power transfer. There are many technical challenges that must be overcome in order to make this vision a reality. This talk will describe what the wireless future might look like along with some of the innovations and breakthroughs required to realize this vision.
How to efficiently and accurately predict the channel impulse response (CIR) is crucial to wireless communications. In this talk, we first briefly discuss the prevailing learning-based approaches and the key problems involved. Then we introduce our work on the CIR representation and prediction with only the user's position information and a set of channel instances obtained within a certain wireless communication environment. Specifically, we resort to a novel physics-inspired generative approach to design the learning network, which makes use of the physical model of EM-wave reflections along each path and the excellent properties of the Gaussian Radial Basis Function network (GRBF) and sinusoidal representation network (SIREN) in predicting the path amplitude and phase respectively. We also discuss how to extend to the resultant learning architecture to the MIMO case and how to apply it to mobile channel prediction.
Federated Learning (FL) and Multi-agent Reinforcement Learning (MARL) are two emerging machine learning paradigms for future intelligent wireless IoT and networked systems. FL is a data-driven supervised machine learning setting where the centralized location trains a learning model by using remote devices (e.g., sensors, user devices). On the other hand, the decentralized MARL schemes, which are based on interactions of the learning agents with the environment, present suitable frameworks to solve decision and control problems considering the heterogeneity of IoT systems. In this talk, I shall discuss example applications and also the challenges of employing FL and MARL methods in resource-constrained and unreliable wireless IoT systems and networks. I shall present an FL algorithm that is suitable for a resource-constrained wireless access network and also a MARL method for a practical wireless edge computing environment. To this end, I shall discuss several of the key open research issues.
Connected Health: Challenges and Solutions for Successful Adoption What are the challenges we face in the introduction and especially the adoption of connected health innovation? Give concrete examples of challenges and solutions tested (both successfully and unsuccessfully) for successful integration and adoption (postponement of challenges and solutions experienced during COVID)
"Unmanned aerial vehicles (UAVs) have found fast growing applications during the past few years. As such, it is imperative to develop innovative communication technologies for supporting reliable UAV command and control (C&C), as well as mission-related payload communication. However, traditional UAV systems mainly rely on the simple direct communication between the UAV and the ground pilot over unlicensed spectrum (e.g., ISM 2.4GHz), which is typically of low data rate, unreliable, insecure, vulnerable to interference, difficult to legitimately monitor and manage, and can only operate within the visual line of sight (LoS) range. To overcome the above limitations, there has been significant interest in integrating UAVs into cellular communication systems. On the one hand, UAVs with their own missions could be connected into cellular networks as new aerial users. Thanks to the advanced cellular technologies and almost ubiquitous accessibility of cellular networks, cellular-connected UAVs are expected to achieve orders-of-magnitude performance improvement over the existing point-to-point UAV communications. It also offers an effective option to strengthen the legitimate UAV monitoring and management, and achieve more robust UAV navigation by utilizing cellular signals as a complement to GPS (Global Position System). On the other hand, dedicated UAVs could be deployed as aerial base stations (BSs), access points (APs), or relays, to assist terrestrial wireless communications from the sky, leading to another paradigm known as UAV-assisted communications. UAV-assisted communications have several promising advantages, such as the ability to facilitate on-demand deployment, high flexibility in network reconfiguration, high chance of having LoS communication links, and enable numerous applications such as BS traffic offloading, information dissemination and collection for Internet of Things (IoTs). UAV communications are significantly different from conventional communication systems, due to the high altitude and high mobility of UAVs, the unique channel of UAV-ground links, the asymmetric quality of service (QoS) requirements for downlink C&C and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the additional design degrees of freedom enabled by joint UAV mobility control and communication resource allocation."
This workshop aims at bringing together academic and industrial researchers in an effort to identify and discuss the major technical challenges, recent breakthroughs, and new applications related to OTFS.
Orbital Angular Momentum (OAM) is regarded as one of the potential key technologies for B5G and 6G mobile communications. No matter in the optical transmission or the radio wave transmission, OAM has been concerned as a new dimension (or a degree of freedom) which can provide additional multiplexing and higher spectrum efficiency, e.g. Tbps data rate is aimed with OAM channels multiplexed in the free space backhaul transmission and Pbps data rate is aimed in the optical fiber with OAM mode division multiplexing. In addition, the theoretical study of OAM has already been engaged in the quantum mechanics for a long time. Many researches in the vortex electron show the promising technology in OAM photon radiation and reception, e.g., relativistic electron cyclotron radiation and electron cyclotron masers. Therefore, the 3rd workshop on OAM transmission in ICC 2021 will focus on both the detailed physical theories of OAM and applications in wireless communications. The workshop is expected to be held with the discussion of the state-of-the-art research on OAM transmission and the promising future application
This workshop provides a venue to bring together standards developers, leading researchers and engineers from government, industry, and academia to present and discuss recent results on shared spectrum technology, and to promote its expedited development.
The goal of the workshop is to solicit the recent developments in ultra-high speed, low latency, and massive connectivity communication with a vision of their potential advancement into beyond 5G and towards 6G. We aim to organize the 4th Workshop on “Ultra-high speed, Low latency and Massive Communication for futuristic 6G Networks (ULMC6GN)” in ICC 2021 to bring together academic researchers, industrial practitioners, and individuals working on this emerging exciting research areas to share their new ideas, latest findings, identify and discuss potential use cases, open research problems, technical challenges, and solution methods in this context.
The aim of this workshop is to streamline research on affective sensing applications in communication networks. It further comes in response to a steadfastly growing trend in communication context both to facilitate cost-effective sensing, and to utilize the user’s affect to improve the network operation. These include the use of ISM-band equipment to contactlessly capture human movement, pose, breathing rate, etc., and infer affect whether in standalone or a multimodal manner, i.e., with or with video/audio feeds. Another example is the automating QoE capture to improve the networked service delivery.
In this workshop, the covered topics include but are not limited to THz transceivers, antennas and antenna arrays; information theoretic analysis of THz communication systems, THz channel modeling, estimation and equalization techniques; ultra-broadband modulation and waveform design; beamforming, precoding and space-time coding schemes; MAC design and interference management; relaying and routing in ultra- broadband networks; system-level modeling and experimental platforms and demonstrations.
Future wireless systems will require a paradigm shift in how they are networked, organized, configured, optimized, and recovered automatically, based on their operating situations. Emerging Internet of Things (IoT) and Cyber-Physical Systems (CPS) applications aim to bring people, data, processes, and things together, to fulfill the needs of our everyday lives. With the emergence of software defined networks, adaptive services and applications are gaining much attention since they allow automatic configuration of devices and their parameters, systems, and services to the user's context change. It is expected that upcoming Fifth Generation and Beyond (5G&B) wireless networks, known as more than an extension to 4G, will be the backbone of IoT and CPS, and will support IoT systems by expanding their coverage, reducing latency and enhancing data rate. However, there are several challenges to be addressed to provide resilient connections supporting the massive number of often resource-constrained IoT and other wireless devices. Hence, due to several unique features of emerging applications, such as low latency, low cost, low energy consumption, resilient and reliable connections, traditional communication protocols and techniques are not suitable.
Traditional machine learning tends to be centralized in nature (e.g., in the cloud). However, security and privacy concerns as well as the availability of abundant data and computational resources in wireless networks motivate moving learning algorithms deployed on mobile networks towards the network edge. This has led to the emergence of the rapidly growing area of (mobile) edge learning, which integrates two originally decoupled areas: wireless communication and machine learning. It is widely expected that the advancements in edge learning will provide a platform for implementing edge artificial intelligence (AI) in 5G-and-Beyond systems and supporting large-scale problems ranging from autonomous driving to personalized healthcare. Thus, this proposed full-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.