Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

• URL: http://arxiv.org/abs/2212.13141v1
• Date: Mon, 26 Dec 2022 12:58:56 GMT
• Title: Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control
• Authors: Musbahu Mohammed Adam, Liqiang Zhao, Kezhi Wang, and Zhu Han
• Abstract summary: 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.
• Score: 76.13180570097299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration approach. 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. We also highlight the need for intelligence in resources integration. Then, we discuss integration of sensing and communication (ISAC) and classify the integration approaches into various classes. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.

Related papers

• A Survey on Integrated Sensing, Communication, and Computation [57.6762830152638]
  The forthcoming generation of wireless technology, 6G, promises a revolutionary leap beyond traditional data-centric services. It aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. Existing techniques like integrated communication and computation (ICC), integrated sensing and computation (ISC), and integrated sensing and communication (ISAC) have made partial strides in addressing this challenge. This paper begins with a comprehensive survey of historic and related techniques such as ICC, ISC, and ISAC, highlighting their strengths and limitations. It then explores the state-of-the-art signal designs for
  arXiv  Detail & Related papers  (2024-08-15T11:01:35Z)
• Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration [56.330705072736166]
  We propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system.
  arXiv  Detail & Related papers  (2023-10-26T15:19:40Z)
• A Comprehensive Study on Artificial Intelligence Algorithms to Implement Safety Using Communication Technologies [1.2710179245406195]
  The study aims at providing a comprehensive picture of the state of the art AI based safety solutions that uses different communication technologies. The results demonstrate that automotive domain is the one applying AI and communication the most to implement safety. The use of non-cellular communication technologies is dominant however a clear trend of a rapid increase in the use of cellular communication is observed specially from 2020 with the roll-out of 5G technology.
  arXiv  Detail & Related papers  (2022-05-17T14:38:38Z)
• Artificial Intelligence for Satellite Communication: A Review [91.3755431537592]
  This work provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. The application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing.
  arXiv  Detail & Related papers  (2021-01-25T13:01:16Z)
• 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)
• Communication-Efficient Edge AI: Algorithms and Systems [39.28788394839187]
  Wide scale deployment of edge devices (e.g., IoT devices) generates an unprecedented scale of data. Such enormous data cannot all be sent from end devices to the cloud for processing. By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative.
  arXiv  Detail & Related papers  (2020-02-22T09:27:55Z)

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.