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

Abstract

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.

Keywords

• 5G
• 6G
• artificial intelligence
• machine learning
• jamming detection
• jamming mitigation