Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G (2402.01665v1)
Abstract: In the sixth-generation (6G) networks, newly emerging diversified services of massive users in dynamic network environments are required to be satisfied by multi-dimensional heterogeneous resources. The resulting large-scale complicated network optimization problems are beyond the capability of model-based theoretical methods due to the overwhelming computational complexity and the long processing time. Although with fast online inference and universal approximation ability, data-driven deep learning (DL) heavily relies on abundant training data and lacks interpretability. To address these issues, a new paradigm called knowledge-driven DL has emerged, aiming to integrate proven domain knowledge into the construction of neural networks, thereby exploiting the strengths of both methods. This article provides a systematic review of knowledge-driven DL in wireless networks. Specifically, a holistic framework of knowledge-driven DL in wireless networks is proposed, where knowledge sources, knowledge representation, knowledge integration and knowledge application are forming as a closed loop. Then, a detailed taxonomy of knowledge integration approaches, including knowledge-assisted, knowledge-fused, and knowledge-embedded DL, is presented. Several open issues for future research are also discussed. The insights offered in this article provide a basic principle for the design of network optimization that incorporates communication-specific domain knowledge and DL, facilitating the realization of intelligent 6G networks.
- ITU-R, “Framework and overall objectives of the future development of IMT for 2030 and beyond,” Draft New Recommendation, June 2023, available online: https://www.itu.int/en/ITU-R/study-groups/rsg5/rwp5d/imt-2030/Pages/default.aspx.
- H. He, S. Jin, C.-K. Wen, F. Gao, G. Y. Li, and Z. Xu, “Model-driven deep learning for physical layer communications,” IEEE Wireless Commun., vol. 26, no. 5, pp. 77–83, Oct. 2019.
- N. Shlezinger, J. Whang, Y. C. Eldar, and A. G. Dimakis, “Model-based deep learning: Key approaches and design guidelines,” in Proc. IEEE Data Science and Learning Workshop (DSLW), June 2021, pp. 1–6.
- S. Leng and A. Yener, “Learning to transmit fresh information in energy harvesting networks,” IEEE Trans. Green Commun. and Netw., vol. 6, no. 4, pp. 2032–2042, Dec. 2022.
- Y. Hua, Z. Zhao, R. Li, X. Chen, Z. Liu, and H. Zhang, “Deep learning with long short-term memory for time series prediction,” IEEE Commun. Mag., vol. 57, no. 6, pp. 114–119, June 2019.
- S. He, S. Xiong, Y. Ou, J. Zhang, J. Wang, Y. Huang, and Y. Zhang, “An overview on the application of graph neural networks in wireless networks,” IEEE Open J. Commun. Soc., vol. 2, pp. 2547–2565, 2021.
- M. Eisen, C. Zhang, L. F. O. Chamon, D. D. Lee, and A. Ribeiro, “Dual domain learning of optimal resource allocations in wireless systems,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, pp. 4729–4733.
- D. Liu, C. Sun, C. Yang, and L. Hanzo, “Optimizing wireless systems using unsupervised and reinforced-unsupervised deep learning,” IEEE Network, vol. 34, no. 4, pp. 270–277, July 2020.
- M. Guenach, A. A. Gorji, and A. Bourdoux, “A deep neural architecture for real-time access point scheduling in uplink cell-free massive MIMO,” IEEE Trans. Wireless Commun., vol. 21, no. 3, pp. 1529–1541, Mar. 2022.
- Y. Zheng, “Leveraging domain knowledge for robust deep reinforcement learning in networking,” Proc. IEEE International Conference on Computer Communications (INFOCOM), 2021.
- Y. Li, S. Han, and C. Yang, “Multicell power control under rate constraints with deep learning,” IEEE Trans. Wireless Commun., vol. 20, no. 12, pp. 7813–7825, Dec. 2021.
- V. Monga, Y. Li, and Y. C. Eldar, “Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,” IEEE Signal Processing Mag., vol. 38, no. 2, pp. 18–44, Mar. 2021.
- A. Jagannath, J. Jagannath, and T. Melodia, “Redefining wireless communication for 6G: Signal processing meets deep learning with deep unfolding,” IEEE Trans. Artif. Intell., vol. 2, no. 6, pp. 528–536, Dec. 2021.
- Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He, “An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,” IEEE Trans. Signal Processing, vol. 59, no. 9, pp. 4331–4340, Apr. 2011.
- 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.