From 5G to 6G Networks: A Survey on AI-Based Jamming and Interference Detection and Mitigation

被引:3
作者
Lohan, Poonam [1 ]
Kantarci, Burak [1 ]
Amine Ferrag, Mohamed [2 ]
Tihanyi, Norbert [2 ]
Shi, Yi [3 ]
机构
[1] Univ Ottawa, Sch Elect Engn & Comp Sci, Ottawa, ON K1N 6N5, Canada
[2] Technol Innovat Inst, Artificial Intelligence & Digital Sci Res Ctr, Abu Dhabi, U Arab Emirates
[3] Virginia Tech, Bradley Dept Elect & Comp Engn, Blacksburg, VA 24061 USA
来源
IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY | 2024年 / 5卷
基金
加拿大自然科学与工程研究理事会;
关键词
5G; 6G; artificial intelligence; machine learning; jamming detection; jamming mitigation; interference mitigation; physical layer; NONORTHOGONAL MULTIPLE-ACCESS; RADIO RESOURCE-MANAGEMENT; NOMA-IOT NETWORKS; POWER-CONTROL; WIRELESS NETWORKS; TERAHERTZ COMMUNICATIONS; CHANNEL ASSIGNMENT; D2D COMMUNICATIONS; SPECTRUM ACCESS; RESEARCH ISSUES;
D O I
10.1109/OJCOMS.2024.3416808
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Fifth-generation and Beyond (5GB) networks are transformational technologies to revolutionize future wireless communications in terms of massive connectivity, higher capacity, lower latency, and ultra-high reliability. To this end, 5GB networks are designed as a coalescence of various schemes and enabling technologies such as unmanned aerial vehicles (UAV)-assisted networks, vehicular networks, heterogeneous cellular networks (HCNs), Internet of Things (IoT), device-to-device (D2D) communication, millimeter-wave (mm-wave), massive multiple-input multiple-output (mMIMO), non-orthogonal multiple access (NOMA), re-configurable intelligent surface (RIS) and Terahertz (THz) communications. Due to the scarcity of licensed bands and the co-existence of multiple technologies in unlicensed bands, interference management is a pivotal factor in enhancing the user experience and quality of service (QoS) in future-generation networks. However, due to the highly complex scenarios, conventional interference mitigation techniques may not be suitable in 5GB networks. To cope with this, researchers have investigated artificial intelligence (AI)-based interference management techniques to tackle complex environments. Existing surveys either focus on conventional interference management methods or AI-based interference management only for a specific scheme or technology. This survey article complements the existing survey literature by providing a detailed review of AI-based intentional-interference management such as jamming detection and mitigation, and AI-enabled unintentional-interference mitigation techniques from the standpoints of UAV-assisted networks, vehicular networks, HCNs, D2D, IoT, mmWave-MIMO, NOMA, and THz communications. While identifying and presenting the AI-based techniques for interference management in 5G and beyond networks, this article also points out the challenges, open issues, and future research directions to adopt AI-enabled techniques to curtail the effects of interference in 5GB and towards 6G networks.
引用
收藏
页码:3920 / 3974
页数:55
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共 256 条
  • [91] Deep Reinforcement Learning-Based Dynamic Spectrum Access for D2D Communication Underlay Cellular Networks
    Huang, Jingfei
    Yang, Yang
    He, Gang
    Xiao, Yang
    Liu, Jun
    [J]. IEEE COMMUNICATIONS LETTERS, 2021, 25 (08) : 2614 - 2618
  • [92] Learning-Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems
    Huang, Shaocheng
    Ye, Yu
    Xiao, Ming
    [J]. IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2021, 7 (01) : 120 - 132
  • [93] A New Block-Based Reinforcement Learning Approach for Distributed Resource Allocation in Clustered IoT Networks
    Hussain, Fatima
    Hussain, Rasheed
    Anpalagan, Alagan
    Benslimane, Abderrahim
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (03) : 2891 - 2904
  • [94] Optimal Learning Paradigm and Clustering for Effective Radio Resource Management in 5G HetNets
    Iqbal, Muhammad Usman
    Ansari, Ejaz Ahmad
    Akhtar, Saleem
    Farooq-I-Azam, Muhammad
    Hassan, Syed Raheel
    Asif, Rameez
    [J]. IEEE ACCESS, 2023, 11 : 41264 - 41280
  • [95] Improving the QoS in 5G HetNets Through Cooperative Q-Learning
    Iqbal, Muhammad Usman
    Ansari, Ejaz Ahmad
    Akhtar, Saleem
    Khan, Ali Nawaz
    [J]. IEEE ACCESS, 2022, 10 : 19654 - 19676
  • [96] Interference Mitigation in HetNets to Improve the QoS Using Q-Learning
    Iqbal, Muhammad Usman
    Ansari, Ejaz Ahmad
    Akhtar, Saleem
    [J]. IEEE ACCESS, 2021, 9 : 32405 - 32424
  • [97] Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges
    Islam, S. M. Riazul
    Avazov, Nurilla
    Dobre, Octavia A.
    Kwak, Kyung-Sup
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2017, 19 (02): : 721 - 742
  • [98] Cooperative Hybrid Transmit Beamforming in Cell-Free mmWave MIMO Networks
    Jafri, Meesam
    Srivastava, Suraj
    Venkategowda, Naveen K. D.
    Jagannatham, Aditya K.
    Hanzo, Lajos
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2023, 72 (05) : 6023 - 6038
  • [99] Towards Intelligent IoT Networks: Reinforcement Learning for Reliable Backscatter Communications
    Jameel, Furqan
    Khan, Wali Ullah
    Shah, Syed Tariq
    Ristaniemi, Tapani
    [J]. 2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2019,
  • [100] Secure Transmission in Cellular V2X Communications Using Deep Q-Learning
    Jameel, Furqan
    Javed, Muhammad Awais
    Zeadally, Sherali
    Jantti, Riku
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (10) : 17167 - 17176