An Intelligent AIOps Framework for Fault Detection and Network Optimization in 5G-A Systems - Brandon Kingsley; Paul Langford (Computational Intelligence Systems, 2026)
The transition from fifth-generation (5G) to 5G-Advanced (5G-A) telecommunications represents a significant leap in network complexity, characterized by massive machine-type communications, ultra-reliable low-latency links, and an increasingly heterogeneous infrastructure. As these systems scale, traditional manual network management and reactive fault detection mechanisms become insufficient, necessitating the integration of Artificial Intelligence for IT Operations (AIOps). This research paper proposes a comprehensive, intelligent AIOps framework designed specifically for the architectural demands of 5G-A systems. The framework emphasizes a shift from siloed monitoring to a holistic, cross-layer intelligence layer that facilitates proactive fault detection and autonomous network optimization. By examining the structural trade-offs between centralized and decentralized intelligence, the paper discusses how AIOps can mitigate the inherent volatility of millimetre-wave transmissions and massive MIMO configurations. The analysis extends beyond technical performance to address critical socio-technical implications, including governance, infrastructure sustainability, and policy frameworks required for autonomous network operation. We argue that the robustness of 5G-A relies not merely on hardware capability but on the cognitive capacity of the management layer to navigate multi-dimensional optimization spaces. The discussion highlights the necessity of a standardized AIOps governance model to ensure fairness in resource allocation across diverse service slices. Ultimately, this work provides a deep conceptual analysis of the deployment challenges and long-term evolutionary paths for intelligent operations in the next era of mobile connectivity.