2,012 research outputs found
A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks
The fifth generation (5G) mobile networks are envisaged to enable a plethora
of breakthrough advancements in wireless technologies, providing support of a
diverse set of services over a single platform. While the deployment of 5G
systems is scaling up globally, it is time to look ahead for beyond 5G systems.
This is driven by the emerging societal trends, calling for fully automated
systems and intelligent services supported by extended reality and haptics
communications. To accommodate the stringent requirements of their prospective
applications, which are data-driven and defined by extremely low-latency,
ultra-reliable, fast and seamless wireless connectivity, research initiatives
are currently focusing on a progressive roadmap towards the sixth generation
(6G) networks. In this article, we shed light on some of the major enabling
technologies for 6G, which are expected to revolutionize the fundamental
architectures of cellular networks and provide multiple homogeneous artificial
intelligence-empowered services, including distributed communications, control,
computing, sensing, and energy, from its core to its end nodes. Particularly,
this paper aims to answer several 6G framework related questions: What are the
driving forces for the development of 6G? How will the enabling technologies of
6G differ from those in 5G? What kind of applications and interactions will
they support which would not be supported by 5G? We address these questions by
presenting a profound study of the 6G vision and outlining five of its
disruptive technologies, i.e., terahertz communications, programmable
metasurfaces, drone-based communications, backscatter communications and
tactile internet, as well as their potential applications. Then, by leveraging
the state-of-the-art literature surveyed for each technology, we discuss their
requirements, key challenges, and open research problems
A prospective look: key enabling technologies, applications and open research topics in 6G networks
The fifth generation (5G) mobile networks are envisaged to enable a plethora of breakthrough advancements in wireless technologies, providing support of a diverse set of services over a single platform. While the deployment of 5G systems is scaling up globally, it is time to look ahead for beyond 5G systems. This is mainly driven by the emerging societal trends, calling for fully automated systems and intelligent services supported by extended reality and haptics communications. To accommodate the stringent requirements of their prospective applications, which are data-driven and defined by extremely low-latency, ultra-reliable, fast and seamless wireless connectivity, research initiatives are currently focusing on a progressive roadmap towards the sixth generation (6G) networks, which are expected to bring transformative changes to this premise. In this article, we shed light on some of the major enabling technologies for 6G, which are expected to revolutionize the fundamental architectures of cellular networks and provide multiple homogeneous artificial intelligence-empowered services, including distributed communications, control, computing, sensing, and energy, from its core to its end nodes. In particular, the present paper aims to answer several 6G framework related questions: What are the driving forces for the development of 6G? How will the enabling technologies of 6G differ from those in 5G? What kind of applications and interactions will they support which would not be supported by 5G? We address these questions by presenting a comprehensive study of the 6G vision and outlining seven of its disruptive technologies, i.e., mmWave communications, terahertz communications, optical wireless communications, programmable metasurfaces, drone-based communications, backscatter communications and tactile internet, as well as their potential applications. Then, by leveraging the state-of-the-art literature surveyed for each technology, we discuss the associated requirements, key challenges, and open research problems. These discussions are thereafter used to open up the horizon for future research directions
XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges
We present a new approach to Extended Reality (XR), denoted as iCOPYWAVES,
which seeks to offer naturally low-latency operation and cost-effectiveness,
overcoming the critical scalability issues faced by existing solutions.
iCOPYWAVES is enabled by emerging PWEs, a recently proposed technology in
wireless communications. Empowered by intelligent (meta)surfaces, PWEs
transform the wave propagation phenomenon into a software-defined process. We
leverage PWEs to i) create, and then ii) selectively copy the scattered RF
wavefront of an object from one location in space to another, where a machine
learning module, accelerated by FPGAs, translates it to visual input for an XR
headset using PWEdriven, RF imaging principles (XR-RF). This makes for an XR
system whose operation is bounded in the physical layer and, hence, has the
prospects for minimal end-to-end latency. Over large distances,
RF-to-fiber/fiber-to-RF is employed to provide intermediate connectivity. The
paper provides a tutorial on the iCOPYWAVES system architecture and workflow. A
proof-of-concept implementation via simulations is provided, demonstrating the
reconstruction of challenging objects in iCOPYWAVES produced computer graphics
Machine-Learning Methods for Computational Science and Engineering
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications
Machine-Learning Methods for Computational Science and Engineering
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications
Telecommunication Systems
This book is based on both industrial and academic research efforts in which a number of recent advancements and rare insights into telecommunication systems are well presented. The volume is organized into four parts: "Telecommunication Protocol, Optimization, and Security Frameworks", "Next-Generation Optical Access Technologies", "Convergence of Wireless-Optical Networks" and "Advanced Relay and Antenna Systems for Smart Networks." Chapters within these parts are self-contained and cross-referenced to facilitate further study
Wearable Wireless Devices
No abstract available
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