Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

被引:7
作者
Abd Elaziz, Mohamed [1 ,2 ,3 ,4 ]
Al-qaness, Mohammed A. A. [5 ]
Dahou, Abdelghani [6 ,7 ]
Alsamhi, Saeed Hamood [8 ,9 ]
Abualigah, Laith [10 ,11 ,12 ]
Ibrahim, Rehab Ali [1 ]
Ewees, Ahmed A. [13 ]
机构
[1] Zagazig Univ, Fac Sci, Dept Math, Zagazig, Egypt
[2] Galala Univ, Fac Comp Sci & Engn, Suze 435611, Egypt
[3] Ajman Univ, Artificial Intelligence Res Ctr AIRC, Ajman, U Arab Emirates
[4] Lebanese Amer Univ, Dept Elect & Comp Engn, Byblos, Lebanon
[5] Zhejiang Normal Univ, Coll Phys & Elect Informat Engn, Jinhua 321004, Peoples R China
[6] Zhejiang Normal Univ, Sch Comp Sci & Technol, Jinhua, Peoples R China
[7] Univ Ahmed Draia Adrar, Fac Sci & Technol, LDDI Lab, Adrar, Algeria
[8] Univ Galway, Insight Ctr Data Analyt, Galway, Ireland
[9] IBB Univ, Fac Engn, Ibb, Yemen
[10] Al Ahliyya Amman Univ, Hourani Ctr Appl Sci Res, Amman, Jordan
[11] Middle East Univ, MEU Res Unit, Amman, Jordan
[12] Appl Sci Private Univ, Appl Sci Res Ctr, Amman, Jordan
[13] Damietta Univ, Dept Comp, Dumyat, Egypt
关键词
5G; 6G network; cybersecurity; deep learning; green communication; sustainability; CONVOLUTIONAL NEURAL-NETWORK; COMMUNICATIONS CHALLENGES; PERSON REIDENTIFICATION; WIRELESS COMMUNICATIONS; RESOURCE-ALLOCATION; 6G-ENABLED INTERNET; CELLULAR NETWORKS; EDGE INTELLIGENCE; IOT; SECURITY;
D O I
10.1002/widm.1521
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The sixth generation (6G) represents the next evolution in wireless communication technology and is currently under research and development. It is expected to deliver faster speeds, reduced latency, and greater capacity compared to the current 5G wireless technology. 6G is envisioned as a technology capable of establishing a fully data-driven network, proficient in analyzing and optimizing end-to-end behavior and handling massive volumes of real-time data at rates of up to terabits per second (Tb/s). Moreover, 6G is designed to accommodate an average of 1000+ substantial connections per person over the course of a decade. The concept of a data-driven network introduces a new service paradigm, which offers fresh opportunities for applications within 6G wireless communication and network design in the future. This paper aims to provide a survey of existing applications of 6G that are based on deep learning techniques. It also explores the potential, essential technologies, scenarios, challenges, and related topics associated with 6G. These aspects are crucial for meeting the requirements for the development of future intelligent networks. Furthermore, this work delves into various research gaps between deep learning and 6G that remain unexplored. Different potential deep learning applications for 6G networks, including privacy, security, environmentally friendly communication, sustainability, and various wireless applications, are discussed. Additionally, we shed light on the challenges and future trends in this field.This article is categorized under: Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning
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页数:37
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共 271 条
  • [71] Deep Learning for Signal Authentication and Security in Massive Internet-of-Things Systems
    Ferdowsi, Aidin
    Saad, Walid
    [J]. IEEE TRANSACTIONS ON COMMUNICATIONS, 2019, 67 (02) : 1371 - 1387
  • [72] Foret Pierre, 2020, INT C LEARN REPR
  • [73] Security of Deep Learning Models in 5G Networks: Proposition of Security Assessment Process
    Ftaimi, Asmaa
    Mazri, Tomader
    [J]. NETWORKING, INTELLIGENT SYSTEMS AND SECURITY, 2022, 237 : 393 - 407
  • [74] Fu Z., 2021, J CYBERSECURITY, V3, P11, DOI DOI 10.32604/JCS.2021.016632
  • [75] Ganapathy K., 2020, ARXIV
  • [76] Garg S, 2020, AAAI CONF ARTIF INTE, V34, P7780
  • [77] Ge Z., 2021, ARXIV, DOI 10.48550/arXiv.2107.08430
  • [78] Generative Adversarial Networks
    Goodfellow, Ian
    Pouget-Abadie, Jean
    Mirza, Mehdi
    Xu, Bing
    Warde-Farley, David
    Ozair, Sherjil
    Courville, Aaron
    Bengio, Yoshua
    [J]. COMMUNICATIONS OF THE ACM, 2020, 63 (11) : 139 - 144
  • [79] Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
    Gopi, Sudheesh Puthenveettil
    Magarini, Maurizio
    Alsamhi, Saeed Hamood
    Shvetsov, Alexey V.
    [J]. DRONES, 2021, 5 (04)
  • [80] Graves A, 2013, INT CONF ACOUST SPEE, P6645, DOI 10.1109/ICASSP.2013.6638947