mlsanityData to 7 Oct 2026

At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

arXiv 2402.18587 ↗First posted 2 Feb 2024cs.NI cs.AI cs.LGcited by 25

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Abstract (arXiv)

The majority of data-driven wireless research leans heavily on discriminative AI (DAI) that requires vast real-world datasets. Unlike the DAI, Generative AI (GenAI) pertains to generative models (GMs) capable of discerning the underlying data distribution, patterns, and features of the input data. This makes GenAI a crucial asset in wireless domain wherein real-world data is often scarce, incomplete, costly to acquire, and hard to model or comprehend. With these appealing attributes, GenAI can replace or supplement DAI methods in various capacities. Accordingly, this combined tutorial-survey paper commences with preliminaries of 6G and wireless intelligence by outlining candidate 6G applications and services, presenting a taxonomy of state-of-the-art DAI models, exemplifying prominent DAI use cases, and elucidating the multifaceted ways through which GenAI enhances DAI. Subsequently, we present a tutorial on GMs by spotlighting seminal examples such as generative adversarial networks, variational autoencoders, flow-based GMs, diffusion-based GMs, generative transformers, large language models, to name a few. Contrary to the prevailing belief that GenAI is a nascent trend, our exhaustive review of approximately 120 technical papers demonstrates the scope of research across core wireless research areas, including physical layer design; network optimization, organization, and management; network traffic analytics; cross-layer network security; and localization & positioning. Furthermore, we outline the central role of GMs in pioneering areas of 6G network research, including semantic/THz/near-field communications, ISAC, extremely large antenna arrays, digital twins, AI-generated content services, mobile edge computing and edge AI, adversarial ML, and trustworthy AI. Lastly, we shed light on the multifarious challenges ahead, suggesting potential strategies and promising remedies.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 52 of its 304 references that are in the map. First posted on arXiv 2 Feb 2024.

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52 of 304 references.

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Citing papers per year: 2024: 5; 2025: 11; 2026: 9.

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  1. 2024Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challengescited by 14
  2. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Artcited by 13
  3. 2024Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Surveycited by 9
  4. 2024ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perceptioncited by 7
  5. 2025Diffusion Models for Future Networks and Communications: A Comprehensive Surveycited by 6
  6. 2024Communicate Less, Synthesize the Rest: Latency-aware Intent-based Generative Semantic Multicasting with Diffusion Modelscited by 5
  7. 2025Retrieval Augmented Generation with Multi-Modal LLM Framework for Wireless Environmentscited by 5
  8. 2024Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Studycited by 4
  9. 2025Mapping the Landscape of Generative AI in Network Monitoring and Managementcited by 4
  10. 2025Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocationcited by 3
  11. 2025Generative Artificial Intelligence for Internet of Things Computing: A Systematic Surveycited by 3
  12. 2026Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Surveycited by 3
13 more
  1. 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approachescited by 2
  2. 2026A Survey on AI for 6G: Challenges and Opportunitiescited by 2
  3. 2025Denoising and Augmentation: A Dual Use of Diffusion Model for Enhanced CSI Recoverycited by 1
  4. 2025Robust MIMO Channel Estimation Using Energy-Based Generative Diffusion Modelscited by 1
  5. 2026Wireless Dataset Similarity: Measuring Distances in Supervised and Unsupervised Machine Learningcited by 1
  6. 2025SNR and Resource Adaptive Deep JSCC for Distributed IoT Image Classificationcited by 0
  7. 2025Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networkscited by 0
  8. 2026Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Managementcited by 0
  9. 2026Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networkscited by 0
  10. 2026Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networkscited by 0
  11. 2026Exploiting Overlapping Fields of View for Redundancy-Aware Uplink Transmission in Vehicular 6Gcited by 0
  12. 2026Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directionscited by 0
  13. 2026FM4WiFi: Flow Matching for Multi-AP Coordination in Dense Deployments of Beyond Wi-Fi 8 Networkscited by 0