From 5G to 6G Networks: A Survey on AI-Based Jamming and Interference Detection and Mitigation
Why this work is in the frame
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Bibliographic record
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it