AI-Driven Predictive Network Resource Management for Ultra-Low Latency Communications
Keywords:
Artificial intelligence; predictive networking; ultra-low latency communications; edge intelligence; network orchestration; software-defined networking; network slicing; distributed systems; reinforcement learning; communication infrastructure managementAbstract
Ultra-low latency communication infrastructures have emerged as foundational enablers for industrial automation, autonomous mobility, immersive computing, cyber-physical coordination, and intelligent edge ecosystems. The rapid proliferation of heterogeneous connected devices, combined with increasingly stringent quality-of-service expectations, has exposed the limitations of conventional static and reactive network resource management frameworks. Contemporary communication environments require predictive, adaptive, and context-aware orchestration strategies capable of responding to dynamic traffic conditions, mobility patterns, fluctuating workloads, and multi-domain operational constraints in real time. This paper investigates the role of artificial intelligence-driven predictive network resource management in enabling ultra-low latency communications across next-generation distributed infrastructures. The study examines the architectural evolution from deterministic rule-based control toward intelligent predictive orchestration systems integrating machine learning, reinforcement learning, edge intelligence, federated coordination, and autonomous optimization mechanisms. Particular attention is given to system-level trade-offs involving scalability, energy efficiency, fairness, governance, infrastructure resilience, and operational transparency. The paper further explores the interaction between predictive resource allocation and emerging paradigms including network slicing, edge-cloud convergence, software-defined networking, and intent-based orchestration. In addition, the analysis evaluates security vulnerabilities, sustainability implications, policy challenges, and socio-technical considerations associated with large-scale AI-enabled communication ecosystems. Through comparative examination of industrial deployments, smart infrastructure scenarios, and autonomous cyber-physical systems, the study demonstrates that predictive AI architectures fundamentally reshape communication management from reactive service provisioning into anticipatory infrastructure intelligence. The paper concludes by outlining future research trajectories concerning trustworthy autonomy, explainable orchestration, decentralized intelligence coordination, and governance-aware communication optimization frameworks for forthcoming ultra-low latency digital infrastructures.
References
[1] Bennis, M., Debbah, M., & Poor, H. V. (2018). Ultrareliable and low-latency wireless communication: Tail, risk, and scale. Proceedings of the IEEE, 106(10), 1834–1853.
[2] Checko, A., Christiansen, H. L., Yan, Y., Scolari, L., Kardaras, G., Berger, M. S., & Dittmann, L. (2015). Cloud RAN for mobile networks—A technology overview. IEEE Communications Surveys & Tutorials, 17(1), 405–426.
[3] Foukas, X., Patounas, G., Elmokashfi, A., & Marina, M. K. (2017). Network slicing in 5G: Survey and challenges. IEEE Communications Magazine, 55(5), 94–100.
[4] Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications Surveys & Tutorials, 21(3), 2224–2287.
[5] Mao, Q., Hu, F., & Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 20(4), 2595–2621.
[6] Wang, S., Zhang, X., Zhang, Y., Wang, L., Yang, J., & Wang, W. (2017). A survey on mobile edge networks: Convergence of computing, caching and communications. IEEE Access, 5, 6757–6779.
[7] Kreutz, D., Ramos, F. M. V., Verissimo, P., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2015). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1), 14–76.
[8] Mijumbi, R., Serrat, J., Gorricho, J. L., Bouten, N., De Turck, F., & Boutaba, R. (2016). Network function virtualization: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 18(1), 236–262.
[9] Xu, Z., Tang, J., Meng, J., Zhang, W., Wang, Y., Liu, C., & Yang, H. (2018). Experience-driven networking: A deep reinforcement learning based approach. IEEE INFOCOM 2018, 1871–1879.
[10] Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.
[11] Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C. K., & Zhang, J. C. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications, 32(6), 1065–1082.
[12] Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39.
[13] 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.
[14] Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys & Tutorials, 19(3), 1628–1656.
[15] Nunes, B. A. A., Mendonca, M., Nguyen, X. N., Obraczka, K., & Turletti, T. (2014). A survey of software-defined networking: Past, present, and future of programmable networks. IEEE Communications Surveys & Tutorials, 16(3), 1617–1634.
[16] Han, B., Gopalakrishnan, V., Ji, L., & Lee, S. (2015). Network function virtualization: Challenges and opportunities for innovations. IEEE Communications Magazine, 53(2), 90–97.
[17] Atzori, L., Iera, A., & Morabito, G. (2017). Understanding the Internet of Things: Definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Networks, 56, 122–140.
[18] Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K. C., & Hanzo, L. (2017). Machine learning paradigms for next-generation wireless networks. IEEE Wireless Communications, 24(2), 98–105.
[19] Letaief, K. B., Chen, W., Shi, Y., Zhang, J., & Zhang, Y. J. A. (2019). The roadmap to 6G: AI empowered wireless networks. IEEE Communications Magazine, 57(8), 84–90.
[20] 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.
[21] Sun, Y., Peng, M., Zhou, Y., Huang, Y., & Mao, S. (2019). Application of machine learning in wireless networks: Key techniques and open issues. IEEE Communications Surveys & Tutorials, 21(4), 3072–3108.
[22] Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376.
[23] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
[24] Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT Press.
[25] Li, Q. (2026). QoS Assurance Mechanism for 5G Network Slicing Based on the Deep Reinforcement Learning PPO Algorithm. arXiv preprint arXiv:2605.03345.
[26] Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. (2019). Artificial neural networks-based machine learning for wireless networks: A tutorial. IEEE Communications Surveys & Tutorials, 21(4), 3039–3071.
[27] Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450–465.
[28] Xiao, Y., Han, G., Liu, C., & Li, J. (2020). A secure mobile crowdsensing game with deep reinforcement learning. IEEE Transactions on Information Forensics and Security, 15, 3564–3577.
[29] Lane, N. D., Bhattacharya, S., Georgiev, P., Forlivesi, C., & Kawsar, F. (2015). An early resource characterization of deep learning on wearables, smartphones and Internet-of-Things devices. Proceedings of the 2015 International Workshop on Internet of Things towards Applications, 7–12.
[30] Samek, W., Wiegand, T., & Muller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.
[31] Roman, R., Lopez, J., & Mambo, M. (2018). Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78, 680–698.
[32] Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of Things Journal, 3(6), 854–864.
[33] Hu, Y. C., Patel, M., Sabella, D., Sprecher, N., & Young, V. (2015). Mobile edge computing—A key technology towards 5G. ETSI White Paper, 11(11), 1–16.
[34] Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials, 19(4), 2322–2358.
[35] Lu, Y., Morris, K. C., & Frechette, S. (2016). Current standards landscape for smart manufacturing systems. National Institute of Standards and Technology, 1–39.
[36] Campolo, C., Molinaro, A., Scopigno, R., & Araniti, G. (2017). Vehicular ad hoc networks: Standards, solutions, and research. Springer.
[37] McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.
[38] Deng, R., Lu, R., Lai, C., Luan, T. H., & Liang, H. (2016). Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet of Things Journal, 3(6), 1171–1181.
[39] Varghese, B., & Buyya, R. (2018). Next generation cloud computing: New trends and research directions. Future Generation Computer Systems, 79, 849–861.
[40] Abbas, H., Ali, K., & Ahmed, M. (2021). Security challenges in edge computing: State of the art and future directions. Journal of Network and Computer Applications, 188, 103094.
[41] Jones, N. (2018). How to stop data centres from gobbling up the world’s electricity. Nature, 561(7722), 163–166.
[42] Rost, P., Mannweiler, C., Michalopoulos, D., Sartori, P., Sciancalepore, V., Sastry, N., & Holland, O. (2017). Network slicing to enable scalability and flexibility in 5G mobile networks. IEEE Communications Magazine, 55(5), 72–79.
[43] Richart, M., Baliosian, J., Serrat, J., & Gorricho, J. L. (2016). Resource slicing in virtual wireless networks: A survey. IEEE Transactions on Network and Service Management, 13(3), 462–476.
[44] Ayoubi, S., Limam, N., Salahuddin, M. A., Shahriar, N., Boutaba, R., Estrada-Solano, F., & Caicedo, O. M. C. (2018). Machine learning for cognitive network management. IEEE Communications Magazine, 56(1), 158–165.
[45] Wan, J., Tang, S., Shu, Z., Li, D., Wang, S., Imran, M., & Vasilakos, A. V. (2016). Software-defined industrial Internet of Things in the context of industry 4.0. IEEE Sensors Journal, 16(20), 7373–7380.
[46] Aceto, G., Persico, V., & Pescape, A. (2020). Industry 4.0 and health: Internet of Things, big data, and cloud computing for healthcare 4.0. Journal of Industrial Information Integration, 18, 100129.
[47] Siriwardhana, Y., Porambage, P., Liyanage, M., & Ylianttila, M. (2021). AI and 6G security: Opportunities and challenges. IEEE Open Journal of the Communications Society, 2, 2271–2291.
[48] Bouras, C., Kollia, A., & Papazois, A. (2019). SDN & NFV in 5G: Advancements and challenges. 2019 10th IFIP International Conference on New Technologies, Mobility and Security, 1–5.
[49] Helberger, N. (2019). On the democratic role of news recommenders. Digital Journalism, 7(8), 993–1012.
[50] Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21.
[51] Zhang, H., Liu, N., Chu, X., Long, K., Aghvami, H., & Leung, V. C. M. (2017). Network slicing based 5G and future mobile networks: Mobility, resource management, and challenges. IEEE Communications Magazine, 55(8), 138–145.
[52] Ferrag, M. A., Maglaras, L., Janicke, H., Jiang, J., & Shu, L. (2020). Authentication protocols for Internet of Things: A comprehensive survey. Security and Communication Networks, 2020, 1–41.
[53] Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016). The limitations of deep learning in adversarial settings. IEEE European Symposium on Security and Privacy, 372–387.
[54] Roman, R., Zhou, J., & Lopez, J. (2013). On the features and challenges of security and privacy in distributed Internet of Things. Computer Networks, 57(10), 2266–2279.
[55] Zhu, L., Liu, Z., & Han, S. (2019). Deep leakage from gradients. Advances in Neural Information Processing Systems, 32, 14774–14784.
[56] Zuboff, S. (2019). The age of surveillance capitalism. PublicAffairs.
[57] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
[58] Sterbenz, J. P. G., Hutchison, D., Çetinkaya, E. K., Jabbar, A., Rohrer, J. P., Schöller, M., & Smith, P. (2010). Resilience and survivability in communication networks. Computer Networks, 54(8), 1245–1265.
[59] Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers. Future Generation Computer Systems, 28(5), 755–768.
[60] Boyson, S. (2014). Cyber supply chain risk management: Revolutionizing the strategic control of critical IT systems. Technovation, 34(7), 342–353.
[61] Kshetri, N. (2017). Will blockchain emerge as a tool to break the poverty chain in the Global South? Third World Quarterly, 38(8), 1710–1732.
[62] Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., & Schafer, B. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707.
[63] Andrae, A. S. G., & Edler, T. (2015). On global electricity usage of communication technology: Trends to 2030. Challenges, 6(1), 117–157.
[64] Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650.
[65] Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine, 29(6), 82–97.
[66] Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009). The case for VM-based cloudlets in mobile computing. IEEE Pervasive Computing, 8(4), 14–23.
[67] Hilty, L. M., & Aebischer, B. (2015). ICT for sustainability: An emerging research field. ICT Innovations for Sustainability, 3–36.
[68] Han, S., Mao, H., & Dally, W. J. (2016). Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. International Conference on Learning Representations, 1–14.
[69] Ghamkhari, M., & Mohsenian-Rad, H. (2013). Data centers to offer ancillary services. IEEE SmartGridComm, 436–441.
[70] Parajuly, K., Kuehr, R., Awasthi, A., Fitzpatrick, C., Lepawsky, J., Smith, E., Widmer, R., & Zeng, X. (2019). Future e-waste scenarios. United Nations University, 1–84.
[71] van Dijk, J. (2020). The digital divide. Polity Press.
[72] Heeks, R. (2018). Information and communication technology for development. Routledge.
[73] Winner, L. (1980). Do artifacts have politics? Daedalus, 109(1), 121–136.
[74] Pasquale, F. (2015). The black box society. Harvard University Press.
[75] Eubanks, V. (2018). Automating inequality. St. Martin’s Press.
[76] Mueller, M. (2017). Will the Internet fragment? Sovereignty, globalization and cyberspace. Polity Press.
[77] Kuner, C. (2013). Transborder data flows and data privacy law. Oxford University Press.
[78] Couldry, N., & Mejias, U. A. (2019). The costs of connection. Stanford University Press.
[79] Nye, J. S. (2017). Deterrence and dissuasion in cyberspace. International Security, 41(3), 44–71.
[80] Clarke, R. (2019). Regulatory alternatives for AI. Computer Law & Security Review, 35(4), 398–409.
[81] Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control. Viking.
[82] O’Neil, C. (2016). Weapons of math destruction. Crown Publishing Group.
[83] Chowdhury, M. Z., Shahjalal, M., Ahmed, S., & Jang, Y. M. (2020). 6G wireless communication systems: Applications, requirements, technologies, challenges, and research directions. IEEE Open Journal of the Communications Society, 1, 957–975.
[84] Strinati, E. C., Barbarossa, S., Gonzalez-Jimenez, J. L., Ktenas, D., Cassau, J., Maret, L., & Dehos, C. (2021). 6G: The next frontier. IEEE Vehicular Technology Magazine, 16(1), 134–142.
[85] Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971.
[86] Indiveri, G., & Liu, S. C. (2015). Memory and information processing in neuromorphic systems. Proceedings of the IEEE, 103(8), 1379–1397.
[87] Khan, L. U., Yaqoob, I., Tran, N. H., Han, Z., & Hong, C. S. (2020). Network slicing: Recent advances, taxonomy, requirements, and open research challenges. IEEE Access, 8, 36009–36028.
[88] Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mane, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
[89] Nguyen, G., Dlugolinsky, S., Bobak, M., Tran, V., Garcia, A., Heredia, I., Malík, P., & Hluchý, L. (2019). Machine learning and deep learning frameworks and libraries for large-scale data mining: A survey. Artificial Intelligence Review, 52(1), 77–124.
[90] Wehner, S., Elkouss, D., & Hanson, R. (2018). Quantum internet: A vision for the road ahead. Science, 362(6412), eaam9288.
[91] IPCC. (2022). Climate change 2022: Impacts, adaptation and vulnerability. Cambridge University Press.
[92] Floridi, L. (2014). The fourth revolution: How the infosphere is reshaping human reality. Oxford University Press.
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