Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
175 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Hybrid-Task Meta-Learning: A Graph Neural Network Approach for Scalable and Transferable Bandwidth Allocation (2401.10253v2)

Published 23 Dec 2023 in cs.NI and cs.LG

Abstract: In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios, such as non-stationary wireless channels, different quality-of-service (QoS) requirements, and dynamically available resources. To support scalability, the bandwidth allocation policy is represented by a graph neural network (GNN), with which the number of training parameters does not change with the number of users. To enable the generalization of the GNN, we develop a hybrid-task meta-learning (HML) algorithm that trains the initial parameters of the GNN with different communication scenarios during meta-training. Next, during meta-testing, a few samples are used to fine-tune the GNN with unseen communication scenarios. Simulation results demonstrate that our HML approach can improve the initial performance by $8.79\%$, and sampling efficiency by $73\%$, compared with existing benchmarks. After fine-tuning, our near-optimal GNN-based policy can achieve close to the same reward with much lower inference complexity compared to the optimal policy obtained using iterative optimization.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (42)
  1. X. Hao, P. L. Yeoh, Y. Liu, C. She, B. Vucetic, and Y. Li, “Graph neural network-based bandwidth allocation for secure wireless communications,” in Proc. 2023 IEEE Int. Conf. Commun. Workshops (ICC workshops), Rome, Italy, 2023, pp. 332–337.
  2. M. Z. Chowdhury, M. Shahjalal, S. Ahmed, and Y. M. Jang, “6G wireless communication systems: Applications, requirements, technologies, challenges, and research directions,” IEEE Open J. Commun. Soc., vol. 1, pp. 957–975, 2020.
  3. Y. Gu, C. She, Z. Quan, C. Qiu, and X. Xu, “Graph neural networks for distributed power allocation in wireless networks: Aggregation over-the-air,” IEEE Trans. Wireless Commun., Early access.
  4. J. Guo and C. Yang, “Learning power allocation for multi-cell-multi-user systems with heterogeneous graph neural networks,” IEEE Trans. Wireless Commun., vol. 21, no. 2, pp. 884–897, Feb. 2022.
  5. R. D-Mohammady, M. Y. Naderi, and K. R. Chowdhury, “Spectrum allocation and QoS provisioning framework for cognitive radio with heterogeneous service classes,” IEEE Trans. Wireless Commun., vol. 13, no. 7, pp. 3938–3950, Jul. 2014.
  6. B. Han, V. Sciancalepore, X. Costa-Pérez, D. Feng, and H. D. Schotten, “Multiservice-based network slicing orchestration with impatient tenants,” IEEE Trans. Wireless Commun., vol. 19, no. 7, pp. 5010–5024, Jul. 2020.
  7. L. Zanzi, V. Sciancalepore, A. Garcia-Saavedra, H. D. Schotten, and X. Costa-Pérez, “LACO: A latency-driven network slicing orchestration in beyond-5G networks,” IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 667–682, Jan. 2021.
  8. Y. Yuan, G. Zheng, K. -K. Wong, B. Ottersten, and Z. -Q. Luo, “Transfer learning and meta learning-based fast downlink beamforming adaptation,” IEEE Trans. Wireless Commun., vol. 20, no. 3, pp. 1742–1755, Mar. 2021.
  9. J. Zhang, Y. Yuan, G. Zheng, I. Krikidis, and K. -K. Wong, “Embedding model-based fast meta learning for downlink beamforming adaptation,” IEEE Trans. Wireless Commun., vol. 21, no. 1, pp. 149–162, Jan. 2022.
  10. R. Dong, C. She, W. Hardjawana, Y. Li, and B. Vucetic, “Deep learning for radio resource allocation with diverse quality-of-service requirements in 5G,” IEEE Trans. Wireless Commun., vol. 20, no. 4, pp. 2309–2324, Apr. 2021.
  11. H. Lee, J. Park, S. H. Lee, and I. Lee, “Message-passing based user association and bandwidth allocation in HetNets with wireless backhaul,” IEEE Trans. Wireless Commun., vol. 22, no. 1, pp. 704–717, Jan. 2023.
  12. Q. Xu, Z. Su, D. Fang, and Y. Wu, “Hierarchical bandwidth allocation for social community-oriented multicast in space-air-ground integrated networks,” IEEE Trans. Wireless Commun., vol. 22, no. 3, pp. 1915–1930, Mar. 2023.
  13. K. B. Letaief, Y. Shi, J. Lu, and J. Lu, “Edge artificial intelligence for 6G: Vision, enabling technologies, and applications,” IEEE J. Sel. Areas Commun., vol. 40, no. 1, pp. 5–36, Jan. 2022.
  14. C. She, C. Sun, Z. Gu, Y. Li, C. Yang, H. V. Poor, B. Vucetic, “A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning,” Proc. IEEE, vol. 109, no. 3, pp. 204–246, Mar. 2021.
  15. D. He, C. Liu, H. Wang, and T. Q. S. Quek, “Learning-based wireless powered secure transmission,” IEEE Wireless Commun. Lett., vol. 8, no. 2, pp. 600–603, Apr. 2019.
  16. C. Sun, C, She, and C. Yang, “Unsupervised deep learning for optimizing wireless systems with instantaneous and statistic constraints” in Ultra-reliable and low-latency communications (URLLC) theory and practice: Advances in 5G and beyond, 1st ed. Hoboken, NJ, USA: John Wiley&Sons, Ltd. 2023, ch. 4, pp. 85–118.
  17. J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Proc. Int. Conf. Mach. Learn. (ICML), Sydney, Australia, pp. 1263–1272, Apr. 2017.
  18. Y. Liu, C. She, Y. Zhong, W. Hardjawana, F.-C. Zheng, and B. Vucetic, “Interference-limited ultra-reliable and low-latency communications: Graph neural networks or stochastic geometry?,” 2022, arXiv:2207.06918.
  19. Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis,” IEEE J. Sel. Areas Commun., vol. 39, no. 1, pp. 101–115, Jan. 2021.
  20. J. Guo and C. Yang, “Deep neural networks with data rate model: Learning power allocation efficiently,” IEEE Trans. Commun., vol. 71, no. 3, pp. 1447–1461, Mar. 2023.
  21. C. Guo, L. Liang, and G. Y. Li, “Resource allocation for vehicular communications with low latency and high reliability,” IEEE Trans. Wireless Commun., vol. 18, no. 8, pp. 3887–3902, Aug. 2019.
  22. D. Wu and R. Negi, “Effective capacity: a wireless link model for support of quality of service,” IEEE Trans. Wireless Commun., vol. 2, no. 4, pp. 630-–643, Jul. 2003.
  23. J. Tang and X. Zhang, “Quality-of-service driven power and rate adaptation over wireless links,” IEEE Trans. Wireless Commun., vol. 6, no. 8, pp. 3058–3068, Aug. 2007.
  24. W. Yu, A. Chorti, L. Musavian, H. Vincent Poor, and Q. Ni, “Effective secrecy rate for a downlink NOMA network,” IEEE Trans. Wireless Commun., vol. 18, no. 12, pp. 5673–5690, Dec. 2019.
  25. H. Yang, Z. Xiong, J. Zhao, D. Niyato, L. Xiao, and Q. Wu, “Deep reinforcement learning based intelligent reflecting surface for secure wireless communications,” IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 375–388, Jan. 2021.
  26. C. Liu, J. Lee, and T. Q.S. Quek, “Safeguarding UAV communications against full-duplex active eavesdropper,” IEEE Trans. Wireless Commun., vol. 18, no. 6, pp. 2919–2931, Jun. 2019.
  27. H. -M. Wang, Q. Yang, Z. Ding, and H. V. Poor, “Secure short-packet communications for mission-critical IoT applications,” IEEE Trans. Wireless Commun., vol. 18, no. 5, pp. 2565–2578, May 2019.
  28. C. Li, C. She, N. Yang, and T. Q. S. Quek, “Secure transmission rate of short packets with queueing delay requirement,” IEEE Trans. Wireless Commun., vol. 21, no. 1, pp. 203–218, Jan. 2022.
  29. Y. Polyanskiy, H. V. Poor, and S. Verdu, “Channel coding rate in the finite blocklength regime,” IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2307–2359, May 2010.
  30. M. Alsenwi, N. H. Tran, M. Bennis, S. R. Pandey, A. K. Bairagi, and C. S. Hong, “Intelligent resource slicing for eMBB and URLLC coexistence in 5G and beyond: A deep reinforcement learning based approach,” IEEE Trans. Wireless Commun., vol. 20, no. 7, pp. 4585–4600, Jul. 2021.
  31. G. Sun, Z. T. Gebrekidan, G. O. Boateng, D. A.-Mensah, and W. Jiang, “Dynamic reservation and deep reinforcement learning based autonomous resource slicing for virtualized radio access networks,” IEEE Access, vol. 7, pp. 45758–45772, 2019.
  32. T. T. Do, T. J. Oechtering, S. M. Kim, M. Skoglund, and G. Peters, “Uplink waveform channel with imperfect channel state information and finite constellation Input,” IEEE Trans. Wireless Commun., vol. 16, no. 2, pp. 1107–1119, Feb. 2017.
  33. Y. Lu, P. Cheng, Z. Chen, W. H. Mow, Y. Li and B. Vucetic, “Deep multi-task learning for cooperative NOMA: System design and principles,” IEEE J. Sel. Areas Commun., vol. 39, no. 1, pp. 61–78, Jan. 2021.
  34. M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, B. Shillingford, N. Freitas, “Learning to learn by gradient descent by gradient descent,” in Proc. 30th Conf. Neural Inf. Process. Syst. (NIPS 2016), Barcelona, Spain.
  35. A. Nichol, J. Achiam, and J. Schulman, “On first-order meta-learning algorithms,” 2018, arXiv:1803.02999.
  36. A. Raghu, M. Raghu, S. Bengio, and O. Vinyals, “Rapid learning or feature reuse? Towards understanding the effectiveness of MAML,” in Proc. Int. Conf. Learn. Representations (ICLR), Apr. 2020.
  37. C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proc. Int. Conf. Mach. Learn. (ICML), Sydney, Australia, pp. 1126–1135, 2017.
  38. L. Huang, L. Zhang, S. Yang, L. P. Qian, and Y. Wu, “Meta-learning based dynamic computation task offloading for mobile edge computing networks,” IEEE Commun. Lett., vol. 25, no. 5, pp. 1568–1572, May 2021.
  39. Y. Wang, M. Chen, Z. Yang, W. Saad, T. Luo, S. Cui, H. V. Poor, “Meta-reinforcement learning for reliable communication in THz/VLC wireless VR networks,” IEEE Trans. Wireless Commun., vol. 21, no. 9, pp. 7778–7793, Sept. 2022.
  40. C. Xiong, G. Y. Li, Y. Liu, Y. Chen, and S. Xu, “Energy-efficient design for downlink OFDMA with delay-sensitive traffic,” IEEE Trans. Wireless Commun., vol. 12, no. 6, pp. 3085–3095, Jun. 2013.
  41. C. Sun, C. She, C. Yang, T. Q. S. Quek, Y. Li, and B. Vucetic, “Optimizing resource allocation in the short blocklength regime for ultra-reliable and low-latency communications,” IEEE Trans. Wireless Commun., vol. 18, no. 1, pp. 402–415, Jan. 2019.
  42. N. Ye, X. Li, H. Yu, L. Zhao, W. Liu, and X. Hou, “DeepNOMA: A unified framework for NOMA using deep multi-task learning,” IEEE Trans. Wireless Commun., vol. 19, no. 4, pp. 2208–2225, Apr. 2020.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets