Abstract
In the future, 6th-generation networks, ultra-reliable and low-latency communications (URLLC) will lay the foundation for emerging mission-critical applications that have stringent requirements on end-to-end delay and reliability. Nevertheless, applications in different vertical industries have unique requirements on top of latency and reliability, such as global connectivity, high mobility, and low jitter. Since these key performance indicators have not been addressed in the 5th-generation (5G) communication systems, 5G systems are not ready for URLLC. In this chapter, we first identify promising technologies to fulfill these requirements and summarize corresponding new research challenges. To address these challenges, we investigate design methodologies in wireless artificial intelligence and put forward a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC. The basic idea is to merge theoretical models and real-world data in analyzing the latency and reliability and training deep neural networks (DNNs). Considering that the computing capacity at each user and each mobile edge computing server is limited, federated learning is applied to improve the learning efficiency. Furthermore, meta-learning is adopted in the architecture to increase the generalization ability of DNNs in nonstationary networks. Finally, we provide some experimental and simulation results and discuss some future directions.
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References
C. She, R. Dong, Z. Gu, Z. Hou, Y. Li, W. Hardjawana, C. Yang, L. Song, and B. Vucetic, “Deep learning for ultra-reliable and low-latency communications in 6G networks,” IEEE Network, vol. 34, no. 5, pp. 219–225, 2020.
Z. Hou, C. She, Y. Li, D. Niyato, M. Dohler, and B. Vucetic, “Intelligent communications for tactile internet in 6G: Requirements, technologies, and challenges,” IEEE Commun. Mag., vol. 59, no. 12, pp. 82–88, 2021.
P. Schulz, M. Matthe, H. Klessig, et al., “Latency critical IoT applications in 5G: Perspective on the design of radio interface and network architecture,” IEEE Commun. Mag., vol. 55, no. 2, pp. 70–78, Feb. 2017.
A. Aijaz and M. Sooriyabandara, “The tactile internet for industries: A review,” Proc. IEEE, vol. 107, no. 2, pp. 414–435, Feb. 2019.
M. Maier, M. Chowdhury, B. P. Rimal et al., “The tactile internet: vision, recent progress, and open challenges,” IEEE Commun. Mag., vol. 54, no. 5, pp. 138–145, May 2016.
C. She, C. Sun, Z. Gu et al., “A tutorial of ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning,” Proc. IEEE, vol. 109, no. 3, pp. 204–246, March.
J. Park, S. Samarakoon, H. Shiri et al., “Extreme URLLC: Vision, challenges, and key enablers,” arXiv preprint arXiv:2001.09683, 2020.
N. Bui, M. Cesana, S. A. Hosseini et al., “A survey of anticipatory mobile networking: Context-based classification, prediction methodologies, and optimization techniques,” IEEE Commun. Surveys Tuts, vol. 19, no. 3, pp. 1790–1821, Apr. 2017.
C. Sun, C. She, C. Yang, T. Q. Quek, Y. Li, and B. Vucetic, “Optimizing resource allocation in the short blocklength regime for ultra-reliable and low-latency communications,” IEEE Trans. on Wireless Commun., vol. 18, no. 1, pp. 402–415, Jan. 2019.
Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, “Application of machine learning in wireless networks: Key techniques and open issues,” IEEE Commun. Surveys Tuts., vol. 21, no. 4, pp. 3072–3108, 2019.
R. Dong, C. She, W. Hardjawana et al., “Deep learning for hybrid 5G services in mobile edge computing systems: Learn from a digital twin,” IEEE Trans. Wireless Commun., vol. 18, no. 10, Oct. 2019.
O. Holland, E. Steinbach, R. V. Prasad et al., “The IEEE 1918.1 “Tactile Internet” standards working group and its standards,” Proc. IEEE, vol. 107, no. 2, pp. 256–279, Jan. 2019.
Z. Hou, C. She, Y. Li, Z. Li, and B. Vucetic, “Prediction and communication co-design for ultra-reliable and low-latency communications,” IEEE Trans. Wireless Commun., vol. 19, no. 2, pp. 1196–1209, Feb. 2020.
M. Giordani and M. Zorzi, “Non-terrestrial networks in the 6G era: Challenges and opportunities,” IEEE Network, vol. 35, no. 2, pp. 244–251, Dec. 2020.
C. Chaccour, M. N. Soorki, W. Saad et al., “Can terahertz provide high-rate reliable low latency communications for wireless VR?” arXiv preprint arXiv:2005.00536, 2020.
P. Kumari, A. Mezghani, and R. W. Heath Jr, “JCR70: A low-complexity millimeter-wave proof-of-concept platform for a fully-digital MIMO joint communication-radar,” arXiv preprint arXiv:2006.13344, Jan. 2020.
W. Yuan, Z. Wei, J. Yuan, and D. W. K. Ng, “A simple variational Bayes detector for orthogonal time frequency space (OTFS) modulation,” IEEE Trans. Veh. Technol., vol. 69, no. 7, pp. 7976–7980, Apr. 2020.
J. Park, S. Samarakoon, M. Bennis, and M. Debbah, “Wireless network intelligence at the edge,” Proc. IEEE, vol. 107, no. 11, pp. 2204–2239, Oct. 2019.
M. Wise, “APM: Driving value with the digital twin.” GE Digit, 2017.
R. Dong, C. She, W. Hardjawana, Y. Li, and B. Vucetic, “Deep learning for hybrid 5G services in mobile edge computing systems: Learn from a digital twin,” IEEE Trans. Wireless Commun., vol. 18, no. 10, pp. 4692–4707, 2019.
F. Tang, Y. Kawamoto, N. Kato, and J. Liu, “Future intelligent and secure vehicular network toward 6G: Machine-learning approaches,” Proc. IEEE, vol. 108, no. 2, pp. 292–307, Feb. 2020.
H. Sun, X. Chen, Q. Shi et al., “Learning to optimize: Training deep neural networks for interference management,” IEEE Trans. Signal Process., vol. 66, no. 20, pp. 5438–5453, Oct. 2018.
C. Qi, Y. Hua, R. Li et al., “Deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing,” IEEE Commun. Lett., vol. 23, no. 8, pp. 1337–1341, Aug. 2019.
C. She, C. Yang, and T. Q. S. Quek, “Cross-layer optimization for ultra-reliable and low-latency radio access networks,” IEEE Trans. Wireless Commun., vol. 17, no. 1, pp. 127–141, Jan. 2018.
L. Liu, J. Zhang, S. Song, and K. B. Letaief, “Edge-assisted hierarchical federated learning with non-IID data,” arXiv preprint arXiv:1905.06641, 2019.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proc. ICML, 2017.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in Proc. IEEE CVPR, 2018.
Z. Gu, C. She, W. Hardjawana, S. Lumb, D. McKechnie, T. Essery, and B. Vucetic, “Knowledge-assisted deep reinforcement learning in 5G scheduler design: From theoretical framework to implementation,” IEEE J. Sel. Areas Communications, vol. 39, no. 7, pp. 2014–2028, 2021.
J. Zhou, X. Zhang, and W. Wang, “Joint resource allocation and user association for heterogeneous services in multi-access edge computing networks,” IEEE Access, vol. 7, pp. 12 272–12 282, Jan. 2019.
Y. Shen, J. Zhang, S. Song, and K. B. Letaief, “Graph neural networks for wireless communications: From theory to practice,” arXiv preprint arXiv:2203.10800, 2022.
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She, C., Li, Y. (2024). Ultra-Reliable and Low-Latency Communications in 6G: Challenges, Solutions, and Future Directions. In: Lin, X., Zhang, J., Liu, Y., Kim, J. (eds) Fundamentals of 6G Communications and Networking. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-37920-8_24
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DOI: https://doi.org/10.1007/978-3-031-37920-8_24
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