Large Language Model-Driven Intent-Aware Resource Allocation for 6G Semantic Communication Networks
Keywords:
6G, semantic communication, large language models, intent-based networking, resource allocation, network slicing, generative AI governance, sustainabilityAbstract
The emergence of sixth-generation wireless systems redefines communication through semantic-aware architectures that prioritize meaning and task effectiveness over raw bit transmission. Simultaneously, large language models have demonstrated remarkable capabilities in interpreting ambiguous human intent expressed in natural language. This paper presents a comprehensive system-level investigation into the convergence of these two paradigms, proposing an intent-aware resource allocation framework for 6G semantic communication networks that leverages large language models as the core intent interpretation engine. Departing from conventional optimization formulations, we examine the structural trade-offs involved in embedding powerful generative language understanding within the network control plane. The discussion spans architectural decomposition, cross-layer semantic mapping, dynamic resource orchestration, and governance challenges. We analyze how an intent-driven, language-model-mediated control loop can translate high-level user goals into semantically coherent resource assignments across radio, computation, and content dimensions. The study further investigates tensions between inference latency and resource granularity, between centralized intelligence and edge autonomy, and between model adaptability and operational determinism. Sustainability, fairness, trustworthiness, and regulatory alignment are examined as integral design requirements rather than afterthoughts. By situating large language models at the nexus of semantic intent interpretation and infrastructure orchestration, the paper articulates a holistic research agenda that informs future 6G standardization, network operations, and the responsible integration of generative artificial intelligence into critical communication infrastructures.
References
1. Saad, W., Bennis, M., & Chen, M. (2020). A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network, 34(3), 134–142.
2. Letaief, K. B., Chen, W., Shi, Y., Zhang, J., & Zhang, Y. J. (2019). The roadmap to 6G: AI as a main driver for future wireless networks. IEEE Communications Magazine, 57(8), 78–84.
3. Strinati, E. C., Barbarossa, S., Gonzalez-Jimenez, J. L., Kténas, D., Cassiau, N., Maret, L., & Dehos, C. (2019). 6G: The next frontier: From holographic messaging to artificial intelligence using subterahertz and visible light communication. IEEE Vehicular Technology Magazine, 14(3), 42–50.
4. Popovski, P., Simeone, O., Boccardi, F., Gündüz, D., & Sahin, O. (2020). Semantic-effectiveness filtering and control for post-5G wireless communication. IEEE Journal on Selected Areas in Communications, 38(5), 1015–1027.
5. Xie, H., Qin, Z., Li, G. Y., & Juang, B. H. (2021). Deep learning enabled semantic communication systems. IEEE Transactions on Signal Processing, 69, 2666–2679.
6. Gündüz, D., de Kerret, P., Sidiropoulos, N. D., Gesbert, D., Murthy, C. R., & van der Schaar, M. (2020). Machine learning in the air. IEEE Journal on Selected Areas in Communications, 37(10), 2184–2199.
7. IMT-2030 (6G) Promotion Group. (2021). White paper on 6G vision and requirements. China Academy of Information and Communications Technology.
8. Jiang, W., Han, B., Habibi, M. A., & Schotten, H. D. (2021). The road towards 6G: Opportunities, challenges, and the road ahead. IEEE Open Journal of the Communications Society, 2, 756–785.
9. Zhang, C., Zhang, H., Qiao, J., Li, Z., & Alouini, M. S. (2025). TIDES: Traffic Intelligence with DeepSeek Enhanced Spatial Temporal Prediction. IEEE Journal on Selected Areas in Communications.
10. Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694.
11. Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., … Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712.
12. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M. A., Lacroix, T., … Lample, G. (2023). LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
13. Mao, B., Tang, F., Kawamoto, Y., & Kato, N. (2022). AI models for green communications towards 6G. IEEE Communications Surveys & Tutorials, 24(1), 210–247.
14. Habibi, M. A., Nasimi, M., Han, B., & Schotten, H. D. (2019). A comprehensive survey of RAN architectures toward 5G mobile communication system. IEEE Access, 7, 70371–70421.
15. Chen, M., Gündüz, D., Huang, K., Saad, W., & Poor, H. V. (2022). Resource allocation for wireless networks: An optimization and machine learning perspective. Proceedings of the IEEE, 110(6), 922–951.
16. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
17. 3GPP. (2023). System architecture for the 5G System (5GS) (TS 23.501, Release 18). 3rd Generation Partnership Project.
18. ITU-T. (2020). Framework for evaluating intelligence levels of future networks including IMT-2020 (Recommendation Y.3173). International Telecommunication Union.
19. Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., & Sabella, D. (2017). On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Communications Surveys & Tutorials, 19(3), 1657–1681.
20. Zhou, Y., Liu, L., Wang, L., Hui, N., Cui, X., Wu, J., … Hanzo, L. (2020). Service-aware 6G: An intelligent and open network based on convergence of communication, computing, caching, and control. IEEE Journal on Selected Areas in Communications, 38(7), 1470–1487.
21. Dohler, M., Heath, R. W., Lozano, A., Papadias, C. B., & Valenzuela, R. A. (2011). Is the PHY layer dead? IEEE Communications Magazine, 49(4), 159–165.
22. Saad, W., Durrani, S., & Bennis, M. (2024). Generative AI for physical layer communications: A survey. IEEE Communications Surveys & Tutorials, early access.
23. Behringer, M., Pritikin, M., Bjarnarson, S., Clemm, A., Carpenter, B., Jiang, S., & Ciavaglia, L. (2015). Autonomic networking: Definitions and design goals (RFC 7575). Internet Engineering Task Force.
24. Shao, Y., Ozfatura, E., Perotti, A., Popovski, P., & Gündüz, D. (2022). Task-oriented communication for edge inference. IEEE Journal on Selected Areas in Communications, 40(12), 3503–3518.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
