Computing Research Challenges in Next Generation Wireless Networking

• URL: http://arxiv.org/abs/2101.01279v1
• Date: Mon, 4 Jan 2021 23:27:19 GMT
• Title: Computing Research Challenges in Next Generation Wireless Networking
• Authors: Elisa Bertino, Daniel Bliss, Daniel Lopresti, Larry Peterson, and Henning Schulzrinne
• Abstract summary: Technology continues to advance rapidly, and the next generation, 6G, is already being envisioned. 6G will make possible a wide range of powerful, new applications including holographic telepresence, telehealth, remote education, ubiquitous robotics and autonomous vehicles. The advances we will see begin at the hardware level and extend all the way to the top of the software "stack"
• Score: 16.806194058232528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: By all measures, wireless networking has seen explosive growth over the past decade. Fourth Generation Long Term Evolution (4G LTE) cellular technology has increased the bandwidth available for smartphones, in essence, delivering broadband speeds to mobile devices. The most recent 5G technology is further enhancing the transmission speeds and cell capacity, as well as, reducing latency through the use of different radio technologies and is expected to provide Internet connections that are an order of magnitude faster than 4G LTE. Technology continues to advance rapidly, however, and the next generation, 6G, is already being envisioned. 6G will make possible a wide range of powerful, new applications including holographic telepresence, telehealth, remote education, ubiquitous robotics and autonomous vehicles, smart cities and communities (IoT), and advanced manufacturing (Industry 4.0, sometimes referred to as the Fourth Industrial Revolution), to name but a few. The advances we will see begin at the hardware level and extend all the way to the top of the software "stack." Artificial Intelligence (AI) will also start playing a greater role in the development and management of wireless networking infrastructure by becoming embedded in applications throughout all levels of the network. The resulting benefits to society will be enormous. At the same time these exciting new wireless capabilities are appearing rapidly on the horizon, a broad range of research challenges loom ahead. These stem from the ever-increasing complexity of the hardware and software systems, along with the need to provide infrastructure that is robust and secure while simultaneously protecting the privacy of users. Here we outline some of those challenges and provide recommendations for the research that needs to be done to address them.

Related papers

• Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control [76.13180570097299]
  We first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. We review current state-of-the-art research efforts related to the i4C, focusing on recent trends of both conventional and artificial intelligence (AI)-based integration approaches. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.
  arXiv  Detail & Related papers  (2022-12-26T12:58:56Z)
• Digital Twins for Industry 4.0 in the 6G Era [8.87362871411654]
  6G is envisaged to become the infrastructural backbone of future intelligent industry. This article provides a survey in the research area of 6G-empowered industrial DT system.
  arXiv  Detail & Related papers  (2022-10-05T14:01:33Z)
• Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication [71.21459460829409]
  We introduce an emerging deep learning architecture, known as the transformer, and discuss its potential impact on 6G network design. Specifically, we propose transformer-based solutions for massive multiple-input multiple-output (MIMO) systems and various semantic communication problems in 6G networks.
  arXiv  Detail & Related papers  (2022-05-08T03:22:20Z)
• Explainable AI for B5G/6G: Technical Aspects, Use Cases, and Research Challenges [4.7501565899812]
  Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency. Such a 6G network will lead to an excessive number of automated decisions made every second. Risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making.
  arXiv  Detail & Related papers  (2021-12-09T04:46:31Z)
• To Talk or to Work: Energy Efficient Federated Learning over Mobile Devices via the Weight Quantization and 5G Transmission Co-Design [49.95746344960136]
  Federated learning (FL) is a new paradigm for large-scale learning tasks across mobile devices. It is not clear how to establish an effective wireless network architecture to support FL over mobile devices. We develop a wireless transmission and weight quantization co-design for energy efficient FL over heterogeneous 5G mobile devices.
  arXiv  Detail & Related papers  (2020-12-21T01:13:44Z)
• Artificial Intelligence at the Edge [25.451110446336276]
  5G mobile communication networks increase communication capacity, reduce transmission latency and error, and save energy. The envisioned future 6G technology will integrate many more technologies, including for example visible light communication. Many applications require computations and analytics close to application end-points: that is, at the edge of the network, rather than in a centralized cloud.
  arXiv  Detail & Related papers  (2020-12-10T02:08:47Z)
• A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence [152.89360859658296]
  5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC) On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in 3D space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference.
  arXiv  Detail & Related papers  (2020-10-19T08:56:04Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)
• Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications [39.38804488121544]
  Swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. We provide an overview of SI techniques from fundamental concepts to well-knowns. We review the applications of SI to settle emerging issues in next-generation wireless networks.
  arXiv  Detail & Related papers  (2020-07-30T04:32:49Z)
• Federated Learning for 6G Communications: Challenges, Methods, and Future Directions [71.31783903289273]
  We introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G. We describe key technical challenges, the corresponding federated learning methods, and open problems for future research on federated learning in the context of 6G communications.
  arXiv  Detail & Related papers  (2020-06-04T15:17:19Z)
• Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning with Deep Unfolding [17.186326961526994]
  We present the service requirements and the key challenges posed by the envisioned 6G communication architecture. We outline the deficiencies of the traditional algorithmic principles and data-hungry deep learning approaches. This article motivates open research challenges to truly realize hardware-efficient edge intelligence for future 6G networks.
  arXiv  Detail & Related papers  (2020-04-22T17:20:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.