AI for RF: From KPI Dashboards to Autonomous Decisions

The AI Opportunity in Telecom

Modern 5G networks generate terabytes of telemetry daily: counters, KPIs, traces, handover events, interference maps. Without machine intelligence, most of this data sits in storage and is never acted upon. AI changes the equation — transforming passive monitoring into proactive, autonomous network management.

Where AI Fits in the 5G Stack

• Planning & Design: Accelerated RF simulations, ML-assisted parameter recommendations, and anomaly detection in input datasets before a single antenna is installed.
• Operations: Predictive alarms, automated root cause analysis, intelligent ticket triage and resolution — reducing mean time to diagnose (MTTD) by 40–60%.
• Optimization: Closed-loop KPI tuning — SINR, throughput, handover success rate — without manual intervention. Antenna tilt, power control, and neighbor list management handled autonomously.
• Quality Audits: Data-driven, vendor-agnostic benchmarking with automated compliance scoring and deviation reports.
• Energy Efficiency: AI-driven sleep modes and power scaling reduce RAN energy spend by 15–30% without impacting coverage or capacity.

SIGMET: eSegura's AI Intelligence Layer

SIGMET (Signal Intelligence & Metrics Platform) is eSegura's proprietary AI engine. Trained on over 500 million RF data points across hundreds of deployments, SIGMET continuously monitors network KPIs, detects degradations before customers feel them, and autonomously applies tuning actions in real time.

SIGMET is not a passive dashboard. It is a closed-loop system that ingests live telemetry, runs inference against our production ML models, and issues parameter change recommendations — or executes them directly through vendor-neutral API integrations with Ericsson, Nokia, Samsung, and Mavenir RAN equipment.

Measurable Outcomes

• Average SINR improvement of 1.5–3 dB within 30 days of SIGMET deployment.
• 40–60% reduction in mean time to diagnose (MTTD) for network incidents.
• 20–25% reduction in drive test hours through AI-predicted coverage gaps.
• 15–30% energy savings via intelligent RAN sleep scheduling.
• SIGMET deployment from contract to live dashboard: under 14 days.