Self-Adaptive Wireless Communication: Leveraging ML And Agentic AI In Smart Telecommunication Networks

Abstract

Smart wireless telecommunication networks are increasingly incorporating various Machine Learning (ML) techniques for enhanced performance. These algorithms are anticipated to continue in the post-5G/6G era. Current mainstream telco networks rely on rules, thresholds, and simple heuristics for controlling complex processes and behaviors. As a result, many applications, such as forecasting key performance metrics, detecting unusual performance patterns (anomalies), and doing root cause analyses now require more elaborate AI algorithms for their automated realization. Although they have been successfully deployed in inspection tasks, ML and AI-enabled functions still mainly work in the “pilot frame”, meaning that when a function works well on a specific case, it needs to be re-trained, re-tested, or re-tuned for handling different instances. Deep Learning (DL) techniques are replacing traditional data-centric architectures, pipelines, and algorithms in many industries. They enable automatic feature extraction, state-of-the-art performance, and more interpretable results. However, it is also important to investigate novel DL architectures or training pipelines that can adapt themselves to very large, changing models and topological structures and be trained and evaluated continuously without stopping services. Enabling Self-Adaptive (SA) AI is among the next big challenges in digital telecommunications, including but not limited to the following endeavors and questions. What monitoring metrics, strategies, and methodologies are effective in inspection tasks of large ray algorithms or ML models? How can the potential cause space of Managerial Performance Confidentiality (MPCC)-related anomalies be narrowed down or partitioned for fault detection and root cause localization? Clustering and classification algorithms with clear interpretability characteristics will be investigated for this endeavor. In addition, Enabled State Estimation (ESE) is one of the most critical building blocks for enabling proactive, efficient, and powerful management and control of telecommunication networks . By modeling the spatio-temporal SST behavior of the entire network, it is possible to synchronize many important tasks in the time and data domains, which makes some complex-to-explain and complex-to-controlled scenarios manageable. Meanwhile, this paradigm also raises probing questions of how to implement ESE in low-cost and on-demand modes in flexible, multi-dimensional SaaS cases.