Integration of Generative AI and Mobile Networking: A Comprehensive Survey
Bibliographic record
Abstract
Sixth-generation (6G) mobile networks are foreseen to be intelligent, pervasive, and automated systems that support a broad spectrum of emerging applications, such as immersive extended reality, autonomous mobility, and personalized AI agents. These applications demand transformative network management, requiring real-time intelligence, semantic awareness, and user-centric optimization. However, existing network management frameworks, including those based on conventional deep learning, struggle with the non-stationary, heterogeneous nature of 6G environments. Generative Artificial Intelligence (GAI) emerges as a pivotal paradigm to address these limitations with its capabilities in context-aware generation, adaptive reasoning, and dynamic decision-making. This survey pioneers a comprehensive examination of the bidirectional integration between GAI and mobile networking. First, it explores how GAI techniques fundamentally transform network management across critical domains, including channel modeling, transmission optimization, beamforming, routing, network slicing, and resource orchestration. Second, it systematically investigates how mobile networking must evolve in architecture, data management, mobility support, and resource orchestration to effectively deploy, infer, and adapt increasingly complex GAI models under dynamic and constrained conditions. Distinct from existing works, we provide a structured review of GAI developments and their interplay with mobile networking, thoroughly analyze applications and enabling techniques, identify critical technical, architectural, and practical challenges, and propose concrete research directions. Our work bridges the gap between GAI and mobile networking, outlining pathways towards intelligent, adaptive, and collaborative 6G systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".