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
相关论文
共 256 条
  • [1] Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach
    Abdel-Aziz, Mohamed K.
    Samarakoon, Sumudu
    Bennis, Mehdi
    Saad, Walid
    [J]. IEEE COMMUNICATIONS LETTERS, 2020, 24 (02) : 367 - 370
  • [2] Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction
    Abuzainab, Nof
    Alrabeiah, Muhammad
    Alkhateeb, Ahmed
    Sagduyu, Yalin E.
    [J]. 2021 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS), 2021,
  • [3] Next Generation 5G Wireless Networks: A Comprehensive Survey
    Agiwal, Mamta
    Roy, Abhishek
    Saxena, Navrati
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2016, 18 (03): : 1617 - 1655
  • [4] Ahmad Ijaz, 2018, IEEE Communications Standards Magazine, V2, P36, DOI 10.1109/MCOMSTD.2018.1700063
  • [5] Reinforcement Learning for Optimized Beam Training in Multi-Hop Terahertz Communications
    Ahmadi, Arian
    Semiari, Omid
    [J]. IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021), 2021,
  • [6] Hybrid Machine-Learning-Based Spectrum Sensing and Allocation With Adaptive Congestion-Aware Modeling in CR-Assisted IoV Networks
    Ahmed, Ramsha
    Chen, Yueyun
    Hassan, Bilal
    Du, Liping
    Hassan, Taimur
    Dias, Jorge
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (24) : 25100 - 25116
  • [7] Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach
    Ahsan, Waleed
    Yi, Wenqiang
    Qin, Zhijin
    Liu, Yuanwei
    Nallanathan, Arumugam
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2021, 20 (08) : 5083 - 5098
  • [8] Terahertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade
    Akyildiz, Ian F.
    Han, Chong
    Hu, Zhifeng
    Nie, Shuai
    Jornet, Josep Miquel
    [J]. IEEE TRANSACTIONS ON COMMUNICATIONS, 2022, 70 (06) : 4250 - 4285
  • [9] Machine Learning Framework for Sensing and Modeling Interference in IoT Frequency Bands
    Al Homssi, Bassel
    Al-Hourani, Akram
    Krusevac, Zarko
    Rowe, Wayne S. T.
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (06) : 4461 - 4471
  • [10] Self-Organizing mmWave MIMO Cell-Free Networks With Hybrid Beamforming: A Hierarchical DRL-Based Design
    Al-Eryani, Yasser
    Hossain, Ekram
    [J]. IEEE TRANSACTIONS ON COMMUNICATIONS, 2022, 70 (05) : 3169 - 3185