Agentic AI for Autonomous Telecom Network Management
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I3P113Keywords:
Agentic AI, Autonomous Networks, 5G, 6G, Self-Healing, Fault Detection, Resource AllocationAbstract
The fast development of 5G and the introduction of 6G networks have posed new challenges that have never been seen in network control, such as optimization of network resources, detection of faults, and the quality of services. Conventional human-based management models are not able to support such demands because of the complexity of networks, dynamic traffic and high demand in performance. A game changer to network self-management is Agentic Artificial Intelligence (AI) which entails goal oriented autonomous agents. The paper discusses how agentic AI can be utilized in autonomous telecom networks, the role of agentic AI in the resources allocation, fault detection and self healing. Major frameworks, architectures and methodologies used to implement agentic AI are considered with examples and figures and tables presented summarizing the performance in networks. Moreover, the paper focuses on the minimization of human intervention and high reliability and efficiency. The outcomes indicate that agentic AI has the potential to considerably improve the network performance, optimize the operational expenses, and fast-track the implementation of the 5G/6G solutions, leading to the development of the completely autonomous telecom networks.
References
[1] H. Zhang, N. Liu, X. Chu, K. Long, A. Aghvami and V. C. M. Leung, “Viewpoint of 5G,” IEEE Access, vol. 3, pp. 1486–1497, 2015.
[2] J. G. Andrews et al., “What Will 5G Be?,” IEEE J. Sel. Areas Commun., vol. 32, no. 6, pp. 1065–1082, Jun. 2014.
[3] M. Agiwal, A. Roy and N. Saxena, “Next Generation 5G Wireless Networks: A Comprehensive Survey,” IEEE Commun. Surveys Tuts., vol. 18, no. 3, pp. 1617–1655, Third Quarter 2016.
[4] N. Wang et al., “Machine Learning for Networking: Workflow, Advances and Opportunities,” IEEE Netw., vol. 34, no. 2, pp. 106–113, Mar./Apr. 2020.
[5] A. Taleb et al., “On Multi Access Edge Computing: A Survey of the Emerging 5G Network Edge Architecture & Orchestration,” IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1657–1681, Third Quarter 2017.
[6] M. Chen, U. Challita, W. Saad, C. Yin and M. Debbah, “Artificial Neural Networks for Wireless Networks: A Tutorial,” IEEE Commun. Surveys Tuts., vol. 21, no. 4, pp. 3039–3071, Fourth Quarter 2019.
[7] W. Saad, M. Bennis and M. Chen, “A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems,” IEEE Netw., vol. 34, no. 3, pp. 134–142, May/Jun. 2020.
[8] M. Giordani, M. Polese, A. Roy, D. Castanheira and M. Zorzi, “Toward 6G Networks: Use Cases and Technologies,” IEEE Commun. Mag., vol. 58, no. 3, pp. 55–61, Mar. 2020.
[9] S. Sesia, I. Toufik and M. Baker, LTE – The UMTS Long Term Evolution: From Theory to Practice, 2nd ed., Wiley, 2011.
[10] M. Mackay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, 2003.
[11] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed., MIT Press, 2018.
[12] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed., Pearson, 2010.
[13] P. Stone et al., “Multiagent Systems: A Survey from a Machine Learning Perspective,” Auton. Robots, vol. 8, pp. 345–383, 2000.
[14] J. N. Tsitsiklis and B. Van Roy, “An Analysis of Temporal Difference Learning with Function Approximation,” IEEE Trans. Autom. Control, vol. 42, no. 5, pp. 674–690, May 1997.
[15] V. Mnih et al., “Human Level Control Through Deep Reinforcement Learning,” Nature, vol. 518, no. 7540, pp. 529–533, Feb. 2015.
[16] D. Silver et al., “Mastering the Game of Go with Deep Neural Networks and Tree Search,” Nature, vol. 529, no. 7587, pp. 484–489, Jan. 2016.
[17] T. G. Dietterich, “Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition,” J. Artif. Intell. Res., vol. 13, pp. 227–303, 2000.
[18] L. Busoniu, R. Babuska and B. De Schutter, Multi Agent Reinforcement Learning: An Overview, Springer, 2008.
[19] G. Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, 1999.
[20] A. H. Mohsenian Rad and V. W. S. Wong, “Autonomous Vehicular Networks Using Mobile Edge Computing and Reinforcement Learning,” IEEE Trans. Veh. Technol., vol. 67, no. 6, pp. 5257–5269, Jun. 2018.
[21] N. Nikaein et al., “Network Function Virtualization for 5G: Challenges and Opportunities,” IEEE Commun. Mag., vol. 53, no. 2, pp. 90–97, Feb. 2015.
[22] S. K. Das et al., “Autonomic Networking — A Survey,” IEEE Commun. Mag., vol. 45, no. 2, pp. 56–63, Feb. 2007.
[23] X. Qiu and W. Lou, “Optimal Self Organizing Resource Management for Wireless Networks,” IEEE Trans. Mobile Comput., vol. 11, no. 12, pp. 2063–2076, Dec. 2012.
[24] 3GPP TS 32.500, “Self Organizing Networks (SON) Concepts and Requirements,” Release 16, 2020.
[25] F. Salim, A. Checko and H. L. Christiansen, “Machine Learning Techniques for Self Organizing Networks: A Survey,” IEEE Commun. Surveys Tuts., vol. 21, no. 2, pp. 1712–1742, Second Quarter 2019.
[26] T. Taleb et al., “On Multi Access Edge Computing: A Survey of the Emerging 5G Network Edge Architecture & Orchestration,” IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1657–1681, 2017.
[27] S. Huang, W. Saad, A. Gyorgy and D. T. Hoang, “Collaborative Deep Reinforcement Learning for Base Station Activation in Wireless Networks,” IEEE Trans. Wireless Commun., vol. 18, no. 9, pp. 4629–4645, Sep. 2019.
[28] Y. Zhang, N. Zhang and W. Wang, “Energy Efficient Traffic Offloading in Mobile Networks: A Reinforcement Learning Approach,” IEEE Trans. Veh. Technol., vol. 69, no. 8, pp. 8623–8636, Aug. 2020.
[29] Q. Ye, B. Rong, Y. Chen, M. Al Shalash, C. Caramanis and J. G. Andrews, “User Association for Load Balancing in Heterogeneous Cellular Networks,” IEEE Trans. Wireless Commun., vol. 12, no. 6, pp. 2706–2716, Jun. 2013.
[30] F. Zhou et al., “Predictive Resource Allocation for 5G Networks,” IEEE Netw., vol. 33, no. 4, pp. 34–41, Jul./Aug. 2019.
[31] M. Peng, Y. Li, Z. Ding and S. Mao, “RAN Based Cooperative Machine Learning for Wireless Networks,” IEEE Wireless Commun., vol. 26, no. 5, pp. 28–35, Oct. 2019.
[32] Y. Kim et al., “A Survey on Fault Management in LTE Networks,” IEEE Commun. Surveys Tuts., vol. 17, no. 1, pp. 174–191, First Quarter 2015.
[33] J. Han, Y. Zhang and W. Ren, “Network Fault Detection and Diagnosis Using Machine Learning: A Survey,” IEEE Access, vol. 7, pp. 101459–101478, 2019.
[34] Y. Li, M. Chen and J. Liu, “Software Defined Network Function Virtualization: A Survey,” IEEE Access, vol. 3, pp. 2542–2553, 2015.
[35] Y. Xu et al., “Big Data Analytics for Anomaly Detection in Telecommunication Networks,” IEEE Network, vol. 29, no. 2, pp. 15–21, Mar./Apr. 2015.
[36] T. Taleb and A. Ksentini, “Follow Me Cloud: Interworking Federated Clouds and Distributed Mobility Networks,” IEEE Netw., vol. 32, no. 1, pp. 50–55, Jan./Feb. 2018.
[37] M. Peng, K. Zhang, J. Jiang, J. Wang and W. Wang, “Carrier Deployment for Ultra Dense Networks: A Reinforcement Learning Approach,” IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 11050–11060, Nov. 2018.
[38] F. Restuccia, S. D’Oro and T. Melodia, “Learning to Communicate in Wireless Networks: Fundamentals, Opportunities, and Challenges,” IEEE Commun. Surveys Tuts., vol. 21, no. 3, pp. 2254–2290, Third Quarter 2019.
[39] X. Wang, M. Chen, T. Taleb, A. Ksentini and V. C. Leung, “Cache in the Air: Exploiting Content Caching and Delivery Techniques for 5G Systems,” IEEE Commun. Mag., vol. 52, no. 2, pp. 131–139, Feb. 2014.
[40] N. Abderrahim, G. Pujolle and A. Mifdaoui, “SDN/NFV Integration for 5G Networks: A Survey,” IEEE Access, vol. 8, pp. 191105–191128, 2020.
[41] M. F. Tuysuz, A. Billah and M. D. De Amorim, “A Survey of Machine Learning in Self Organizing Networks,” IEEE Access, vol. 7, pp. 117833–117865, 2019.
[42] Y. Sun, M. Peng, S. Mao and S. Yan, “Machine Learning Assisted 5G for Connected Intelligent Systems: Lifecycle and Prospects,” IEEE Commun. Mag., vol. 57, no. 10, pp. 112–118, Oct. 2019.
[43] B. Li, Z. Zhao, Z. Han, R. Q. Hu and Y. Qian, “A Survey of Security Issues in Software Defined Networks,” IEEE Commun. Surveys Tuts., vol. 20, no. 1, pp. 325–366, First Quarter 2018.
[44] Y. Shi, J. Zhang and K. B. Letaief, “Group Sparse Beamforming for Green Cloud RAN,” IEEE Trans. Wireless Commun., vol. 13, no. 5, pp. 2809–2823, May 2014.
[45] X. Li, M. Chen and X. Jiang, “Self Learning Security in 5G Networks: A Survey,” IEEE Access, vol. 7, pp. 68420–68442, 2019.
[46] T. Luo, H. Xu and L. J. Cimini, “Energy Efficient Traffic Offloading in Cloud Radio Access Networks,” IEEE Trans. Wireless Commun., vol. 14, no. 5, pp. 2511–2523, May 2015.
[47] A. Gkelias and R. Jantti, “Exploiting Cooperative Communications for Wireless Sensor Networks: A Survey,” IEEE Commun. Surveys Tuts., vol. 16, no. 3, pp. 1387–1418, Third Quarter 2014.
[48] M. Peng, S. Yan, K. Zhang and C. Wang, “Fog Computing Based Radio Access Networks: Issues and Challenges,” IEEE Netw., vol. 30, no. 4, pp. 46–53, Jul./Aug. 2016.
[49] X. Cao, “A Survey on Trust Management for SDN and NFV Networks,” IEEE Commun. Surveys Tuts., vol. 21, no. 4, pp. 3509–3537, Fourth Quarter 2019.
[50] E. C. Strinati et al., “6G: The Next Frontier: From Heterogeneous Networks to Intelligent Network Ecosystem,” IEEE Commun. Mag., vol. 58, no. 3, pp. 23–29, Mar. 2020.
[51] D. Lopez Pacheco et al., “Efficiency and Scalability Challenges of 5G Architecture Toward 6G,” IEEE Access, vol. 9, pp. 148358–148378, 2021.
[52] S. Sardellitti, G. Scutari and S. Barbarossa, “Joint Optimization of Radio and Computational Resources for Multicell Mobile Edge Computing,” IEEE Trans. Signal Inf. Process. Netw., vol. 1, no. 2, pp. 89–103, Jun. 2015.
[53] A. Galindo Serrano and L. Giupponi, “Distributed Q Learning for Interference Control in OFDMA Wireless Networks,” IEEE Trans. Veh. Technol., vol. 59, no. 4, pp. 1823–1834, May 2010.
[54] Y. Li, V. C. M. Leung and V. K. N. Lau, “Application of Machine Learning Techniques for Resource Management in Wireless Networks,” IEEE Commun. Surveys Tuts., vol. 21, no. 4, pp. 2558–2583, Fourth Quarter 2019.
[55] M. Chen, “Artificial Intelligence Meets 5G: Use Cases, Challenges, and Future Trends,” IEEE Commun. Standards Mag., vol. 3, no. 1, pp. 36–43, Mar. 2019.
[56] S. Zhou and Z. Niu, “Energy Efficiency in Cognitive Radio and Cooperative Networks,” IEEE Veh. Technol. Mag., vol. 11, no. 2, pp. 37–43, Jun. 2016.
[57] K. Ahmed et al., “Survey on Machine Learning Techniques for Fault Detection and Diagnosis in Wireless Communication Networks,” IEEE Access, vol. 7, pp. 74104–74122, 2019.
[58] Z. Ali et al., “AI enabled Intelligent Slicing for 5G and Beyond Networks,” IEEE Access, vol. 8, pp. 172839–172865, 2020.
[59] Y. Xiao, “Security and Privacy in 5G Networks: Challenges and Opportunities,” IEEE Commun. Standards Mag., vol. 3, no. 1, pp. 36–43, Mar. 2019.
[60] A. Rathore et al., “AI Empowered 6G Networks: Use Cases, Architecture, and Challenges,” IEEE Access, vol. 8, pp. 128004–128023, 2020.
[61] K. Premkumar, S. Venkatesan, B. A. Aurobinda and M. Singh, “Reinforcement Learning for Resource Allocation in Wireless Networks: A Survey,” IEEE Access, vol. 9, pp. 162114–162140, 2021.
[62] R. Popovic et al., “Autonomous Networks: Self Organizing Systems and Their Application to Future Generation Networks,” IEEE Commun. Mag., vol. 56, no. 9, pp. 168–177, Sep. 2018.
