The transition to Sixth Generation (6G) wireless infrastructure introduces unprecedented complexity, characterized by massive densification, reliance on the Terahertz (THz) spectrum, dynamic cell topologies driven by Reconfigurable Intelligent Surfaces (RIS), and stringent requirements for Ultra-Reliable Low-Latency Communication (URLLC). Traditional reactive or fixed-schedule maintenance protocols are fundamentally inadequate for sustaining the necessary service continuity and energy efficiency in this hyper-connected, heterogeneous environment. This paper explores the critical role of Artificial Intelligence (AI)—specifically Deep Learning (DL), Reinforcement Learning (RL), and Federated Learning (FL)—in pioneering an advanced Predictive Maintenance (PdM) paradigm for 6G. The proposed AI-driven PdM system leverages real-time, multi-modal sensor data streams (including thermal signatures, spectral anomalies, traffic load, and physical wear data) to establish high-fidelity Digital Twins of network components. AI algorithms function as the core engine, utilizing anomaly detection and complex pattern recognition to forecast impending hardware failures, spectral drift, or component degradation with high temporal accuracy. By shifting maintenance from scheduled guesswork to targeted, data-driven action, AI-PdM minimizes network downtime, significantly reduces Operational Expenditure (OPEX), and maximizes the lifespan and performance of expensive, energy-intensive THz equipment. This infrastructure is not merely managed; it becomes self- aware and autonomously reliable, an absolute prerequisite for realizing the ambitious goals of 6G.

AI, 6G, Predictive maintenance, Terahertz, Digital Twin

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1. Imoize AL, Adedeji O, Tandiya N, Shetty S. 6G enabled smart infrastructure for sustainable society: Opportunities, challenges, and research roadmap. Sensors. 2021 Mar 2;21(5):1709.
2. Khan LU, Han Z, Saad W, Hossain E, Guizani M, Hong CS. Digital twin of wireless systems: Overview, taxonomy, challenges, and opportunities. IEEE Communications Surveys & Tutorials. 2022 Aug 11;24(4):2230-54.
3. Kuruvatti NP, Habibi MA, Partani S, Han B, Fellan A, Schotten HD. Empowering 6G communication systems with digital twin technology: A comprehensive survey. IEEE access. 2022 Oct 19;10:112158-86.
4. Khan LU, Saad W, Niyato D, Han Z, Hong CS. Digital-twin-enabled 6G: Vision, architectural trends, and future directions. IEEE Communications Magazine. 2022 Feb 11;60(1):74-80.
5. Muteba F. Digital twin (DT)-based predictive maintenance of a 6G communication network. Procedia Computer Science. 2024 Jan 1;238:544-9.
6. De Alwis C, Kalla A, Pham QV, Kumar P, Dev K, Hwang WJ, Liyanage M. Survey on 6G frontiers: Trends, applications, requirements, technologies and future research. IEEE Open Journal of the Communications Society. 2021 Apr 7;2:836-86.
7. Cui Y, Yuan W, Zhang Z, Mu J, Li X. On the physical layer of digital twin: An integrated sensing and communications perspective. IEEE Journal on Selected Areas in Communications. 2023 Sep 19;41(11):3474-90.
8. Ucar A, Karakose M, Kırımça N. Artificial intelligence for predictive maintenance applications: key components, trustworthiness, and future trends. Applied Sciences. 2024 Jan 20;14(2):898.
9. Alqasi MA, Alkelanie YA, Alnagrat AJ. Intelligent infrastructure for urban transportation: The role of artificial intelligence in predictive maintenance. Brilliance: research of artificial intelligence. 2024 Nov 11;4(2):625-37.
10. Prabha S, Upadhyay R, Goswami PK. The smart predictive network maintenance model using AI/ML time series analysis in cloud networks. In2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) 2024 Jan 29 (pp. 1-7). IEEE.

The transition to Sixth Generation (6G) wireless infrastructure introduces unprecedented complexity, characterized by massive densification, reliance on the Terahertz (THz) spectrum, dynamic cell topologies driven by Reconfigurable Intelligent Surfaces (RIS), and stringent requirements for Ultra-Reliable Low-Latency Communication (URLLC). Traditional reactive or fixed-schedule maintenance protocols are fundamentally inadequate for sustaining the necessary service continuity and energy efficiency in this hyper-connected, heterogeneous environment. This paper explores the critical role of Artificial Intelligence (AI)—specifically Deep Learning (DL), Reinforcement Learning (RL), and Federated Learning (FL)—in pioneering an advanced Predictive Maintenance (PdM) paradigm for 6G. The proposed AI-driven PdM system leverages real-time, multi-modal sensor data streams (including thermal signatures, spectral anomalies, traffic load, and physical wear data) to establish high-fidelity Digital Twins of network components. AI algorithms function as the core engine, utilizing anomaly detection and complex pattern recognition to forecast impending hardware failures, spectral drift, or component degradation with high temporal accuracy. By shifting maintenance from scheduled guesswork to targeted, data-driven action, AI-PdM minimizes network downtime, significantly reduces Operational Expenditure (OPEX), and maximizes the lifespan and performance of expensive, energy-intensive THz equipment. This infrastructure is not merely managed; it becomes self- aware and autonomously reliable, an absolute prerequisite for realizing the ambitious goals of 6G.

AI, 6G, Predictive maintenance, Terahertz, Digital Twin

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Imoize AL, Adedeji O, Tandiya N, Shetty S. 6G enabled smart infrastructure for sustainable society: Opportunities, challenges, and research roadmap. Sensors. 2021 Mar 2;21(5):1709.
2. Khan LU, Han Z, Saad W, Hossain E, Guizani M, Hong CS. Digital twin of wireless systems: Overview, taxonomy, challenges, and opportunities. IEEE Communications Surveys & Tutorials. 2022 Aug 11;24(4):2230-54.
3. Kuruvatti NP, Habibi MA, Partani S, Han B, Fellan A, Schotten HD. Empowering 6G communication systems with digital twin technology: A comprehensive survey. IEEE access. 2022 Oct 19;10:112158-86.
4. Khan LU, Saad W, Niyato D, Han Z, Hong CS. Digital-twin-enabled 6G: Vision, architectural trends, and future directions. IEEE Communications Magazine. 2022 Feb 11;60(1):74-80.
5. Muteba F. Digital twin (DT)-based predictive maintenance of a 6G communication network. Procedia Computer Science. 2024 Jan 1;238:544-9.
6. De Alwis C, Kalla A, Pham QV, Kumar P, Dev K, Hwang WJ, Liyanage M. Survey on 6G frontiers: Trends, applications, requirements, technologies and future research. IEEE Open Journal of the Communications Society. 2021 Apr 7;2:836-86.
7. Cui Y, Yuan W, Zhang Z, Mu J, Li X. On the physical layer of digital twin: An integrated sensing and communications perspective. IEEE Journal on Selected Areas in Communications. 2023 Sep 19;41(11):3474-90.
8. Ucar A, Karakose M, Kırımça N. Artificial intelligence for predictive maintenance applications: key components, trustworthiness, and future trends. Applied Sciences. 2024 Jan 20;14(2):898.
9. Alqasi MA, Alkelanie YA, Alnagrat AJ. Intelligent infrastructure for urban transportation: The role of artificial intelligence in predictive maintenance. Brilliance: research of artificial intelligence. 2024 Nov 11;4(2):625-37.
10. Prabha S, Upadhyay R, Goswami PK. The smart predictive network maintenance model using AI/ML time series analysis in cloud networks. In2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) 2024 Jan 29 (pp. 1-7). IEEE.