Ai Powered Telemetry in Mobile Backbone Networks: Improving Route Stability and Performance Forecasting

Abstract

Due to cross-domain heterogeneity, unpredictable traffic patterns, and limited real-time visibility into network conditions, mobile backbone networks are increasingly experiencing performance degradation. This study presents a novel cross-domain AI-driven telemetry pipeline that facilitates intelligent, high-performance routing across the core network, transport, and radio access domains in order to address these issues. The suggested approach captures fine-grained network information, such as latency, link utilization, queue depth, and packet loss, in real time by combining streaming telemetry with a uniform cross-layer data aggregation paradigm. Using this telemetry, a lightweight AI-powered predictive routing engine forecasts congestion and uses adaptive path selection to dynamically improve routing choices. The suggested method greatly increases routing efficiency and network resilience by employing the Ant colony optimization (ACO) algorithm to provide improved proactive and context-aware traffic steering, in contrast to conventional reactive routing protocols. Within the mobile backbone, the innovation is found in the smooth integration of AI-driven decision intelligence and cross-domain telemetry. Experiments show significant gains in throughput stability, end-to-end latency, and resource usage, confirming the usefulness of the suggested framework for next-generation mobile networks.