Е-Публикации
Технически университет - София

Abstract: Low-altitude economic networks (LAENets) underpin the emerging low-altitude economy, where unmanned aerial vehicles (UAVs) support reliable communications under highly dynamic 3D propagation conditions. However, severe Doppler shifts, channel aging, beam misalignment, and interference fundamentally challenge conventional processing, especially under stringent onboard constraints. In this work, we investigate multi-task intelligence in LAENets from an agentic and embodied intelligence perspective, where a UAV operates as an autonomous agent that dynamically executes heterogeneous tasks on demand. To address this challenge, we propose a multi-task low-rank adaptation (LoRA)-assisted cloud-edge collaborative framework that decomposes agent decision-making into task-specific skill components over a shared foundation-model backbone. Specifically, a frozen and quantized large language model (LLM) is deployed at the ground base station as a universal cognitive reasoning module, while lightweight task-specific LoRA adapters, together with dedicated encoders and decoders, are hosted on UAV platforms and dynamically activated according to mission objectives, for avoiding negative transfer among heterogeneous tasks as well as eliminating unnecessary computation during single-task execution. Extensive simulations demonstrate that the proposed framework consistently outperforms conventional model-based methods, independently trained deep neural networks, and fully fine-tuned foundation models in channel prediction, beamforming, and signal detection, while achieving orders-of-magnitude reductions in UAV-side memory footprint and inference latency.

References

1. Y. Xiao, Z. Ye, M. Wu, H. Li, M. Xiao, M.-S. Alouini, A. Al-Hourani, and S. Cioni, “Space-air-ground integrated wireless networks for 6g: Basics, key technologies and future trends,” IEEE Journal on Selected Areas in Communications, 2024.
2. Z. Lyu, Y. Gao, J. Chen, H. Du, J. Xu, K. Huang, and D. I. Kim, “Empowering intelligent low-altitude economy with large AI model deployment,” IEEE Wireless Communications, vol. 33, no. 1, pp. 64–72, 2026.
3. L. Cai, J. Wang, R. Zhang, Y. Zhang, T. Jiang, D. Niyato, X. Wang, A. Jamalipour, and X. Shen, “Secure physical layer communications for low-altitude economy networking: A survey,” IEEE Communications Surveys & Tutorials, 2025.
4. Y. Gao, Z. Ye, and H. Yu, “Cost-efficient computation offloading in sagin: A deep reinforcement learning and perception-aided approach,” IEEE Journal on Selected Areas in Communications, 2024.
5. H. Du, R. Zhang, Y. Liu, J. Wang, Y. Lin, Z. Li, D. Niyato, J. Kang, Z. Xiong, S. Cui, et al., “Enhancing deep reinforcement learning: A tutorial on generative diffusion models in network optimization,” IEEE Communications Surveys & Tutorials, vol. 26, no. 4, pp. 2611–2646, 2024.
6. N. Islam and S. Shin, “Deep learning in physical layer: Review on data driven end-to-end communication systems and their enabling semantic applications,” IEEE Open Journal of the Communications Society, vol. 5, pp. 4207–4240, 2024.
7. N. Yang, M. Fan, W. Wang, and H. Zhang, “Decision-making large language model for wireless communication: A comprehensive survey on key techniques,” IEEE Communications Surveys & Tutorials, 2025.
8. G. O. Boateng, H. Sami, A. Alagha, H. Elmekki, A. Hammoud, R. Mizouni, A. Mourad, H. Otrok, J. Bentahar, S. Muhaidat, et al., “A survey on large language models for communication, network, and service management: Application insights, challenges, and future directions,” IEEE Communications Surveys & Tutorials, 2025.
9. X. Cheng, R. Meng, X. Xu, H. Gao, P. Zhang, and D. Niyato, “Apeg: Adaptive physical layer authentication with channel extrapolation and generative ai,” IEEE Transactions on Information Forensics and Security, vol. 21, pp. 1257–1272, 2026.
10. T. Zheng and L. Dai, “Large language model enabled multi-task physical layer network,” IEEE Transactions on Communications, 2025.
11. X. Cai, J. Rodríguez-Piñeiro, X. Yin, N. Wang, B. Ai, G. F. Pedersen, and A. P. Yuste, “An empirical air-to-ground channel model based on passive measurements in lte,” IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1140–1154, 2018.
12. B. Y. Shikur and T. Weber, “Channel prediction using an adaptive kalman filter,” in WSA 2015; 19th International ITG Workshop on Smart Antennas, pp. 1–7, VDE, 2015.
13. J. Zhan and X. Dong, “Interference cancellation aided hybrid beamforming for mmwave multi-user massive mimo systems,” IEEE Transactions on Vehicular Technology, vol. 70, no. 3, pp. 2322–2336, 2021.
14. H. Yan, W. Bao, X. Zhu, J. Wang, and L. Liu, “Data offloading enabled by heterogeneous uavs for iot applications under uncertain environments,” IEEE Internet of Things Journal, vol. 10, no. 5, pp. 3928–3943, 2022.
15. H. Jiang, M. Cui, D. W. K. Ng, and L. Dai, “Accurate channel prediction based on transformer: Making mobility negligible,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 9, pp. 2717–2732, 2022.
16. I. Ahmed, M. K. Shahid, H. Khammari, and M. Masud, “Machine learning based beam selection with low complexity hybrid beamforming design for 5g massive mimo systems,” IEEE Transactions on Green Communications and Networking, vol. 5, no. 4, pp. 2160–2173, 2021.
17. C. Liu, Y. Chen, and S.-H. Yang, “Signal detection with co-channel interference using deep learning,” Physical Communication, vol. 47, 2021.
18. T. Wang, C.-K. Wen, H. Wang, F. Gao, T. Jiang, and S. Jin, “Deep learning for wireless physical layer: Opportunities and challenges,” China Communications, vol. 14, no. 11, pp. 92–111, 2017.
19. A. Jagannath and J. Jagannath, “Multi-task learning approach for modulation and wireless signal classification for 5g and beyond: Edge deployment via model compression,” Physical Communication, vol. 54, p. 101793, 2022.
20. M. Crawshaw, “Multi-task learning with deep neural networks: A survey,” arXiv preprint arXiv:2009.09796, 2020.
21. J. Fontaine, A. Shahid, and E. De Poorter, “Towards a wireless physical-layer foundation model: Challenges and strategies,” in 2024 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–7, IEEE, 2024.
22. J. Du, T. Lin, C. Jiang, Q. Yang, C. F. Bader, and Z. Han, “Distributed foundation models for multi-modal learning in 6g wireless networks,” IEEE Wireless Communications, vol. 31, no. 3, pp. 20–30, 2024.
23. S. Xu, C. K. Thomas, O. Hashash, N. Muralidhar, W. Saad, and N. Ramakrishnan, “Large multi-modal models (lmms) as universal foundation models for ai-native wireless systems,” IEEE Network, 2024.
24. R. Zhang, G. Liu, Y. Liu, C. Zhao, J. Wang, Y. Xu, D. Niyato, J. Kang, Y. Li, S. Mao, et al., “Toward edge general intelligence with agentic ai and agentification: Concepts, technologies, and future directions,” IEEE Communications Surveys & Tutorials, vol. 28, pp. 4285–4318, 2026.
25. T. Yang, P. Zhang, M. Zheng, Y. Shi, L. Jing, J. Huang, and N. Li, “Wirelessgpt: A generative pre-trained multi-task learning framework for wireless communication,” IEEE Network, 2025.
26. J. Zhang, J. Li, Z. Wang, L. Shi, S. Jin, W. Chen, and H. V. Poor, “Decision transformers for wireless communications: A new paradigm of resource management,” IEEE Wireless Communications, 2025.
27. Y. Wang, Z. Gao, D. Zheng, S. Chen, D. Gündüz, and H. V. Poor, “Transformer-empowered 6g intelligent networks: From massive mimo processing to semantic communication,” IEEE Wireless Communications, vol. 30, no. 6, pp. 127–135, 2022.
28. X. Han, W. Zhiqin, L. Dexin, T. Wenqiang, L. Xiaofeng, L. Wendong, J. Shi, S. Jia, Z. Zhi, and Y. Ning, “Ai enlightens wireless communication: A transformer backbone for csi feedback,” China Communications, vol. 21, no. 12, pp. 243–256, 2024.
29. Y. Tian, Q. Zhao, F. Boukhalfa, K. Wu, F. Bader, et al., “Multimodal transformers for wireless communications: A case study in beam prediction,” arXiv preprint arXiv:2309.11811, 2023.
30. Z. Chen, Z. Zhang, and Z. Yang, “Big ai models for 6g wireless networks: Opportunities, challenges, and research directions,” IEEE wireless communications, vol. 31, no. 5, pp. 164–172, 2024.
31. Y. Lin, R. Zhang, W. Huang, K. Wang, Z. Ding, D. K. So, and D. Niyato, “Empowering large language models in wireless communication: A novel dataset and fine-tuning framework,” arXiv preprint arXiv:2501.09631, 2025.
32. C. Li, C. Ding, K. Luan, and X. Di, “Low-rank adaptation with task-relevant feature enhancement for fine-tuning language models,” arXiv preprint arXiv:2412.09827, 2024.

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