Tier 1 — Mission Critical

Real-time RAN Anomaly Detection

Sub-second inference on live radio telemetry to catch interference and faults before they cause outages.

Urgency score — priority vs. other Telecom & Tower use cases

9/10

Inference latency requirement for production deployment

<100ms

Mission Critical priority classification

Edge inference required — latency and sovereignty

Edge

9/10

Urgency Score

Priority vs. other Telecom & Tower use cases. Inference latency requirement <100ms for production deployment. T1 Mission Critical priority classification. Edge inference required — latency and sovereignty.

Overview

AI inference models process continuous streams of RAN KPIs — RSRQ, SINR, PRB utilization, handover failures — at the edge in real time. Anomalies are detected and classified in under 100ms, enabling automated remediation before subscribers notice degradation. Processes live RAN KPIs including RSRQ, SINR, PRB utilization, and handover failure rates. Detects and classifies anomalies in under 100ms — before subscriber impact. Enables automated remediation at edge, eliminating cloud round-trip latency. Trained on operator-specific baselines, not generic network benchmarks. Two chained inference calls: anomaly detection + fault classification within a single TTI window.

The Penalty Stakes

Why Cloud Cannot Solve This

Cloud round-trip latency (typically 50–200ms) exceeds the entire detection budget
Streaming RAN telemetry to cloud creates massive bandwidth cost and data sovereignty exposure
Generic pre-trained models miss operator-specific interference patterns and equipment anomalies
Remediation actions executed from cloud arrive too late to prevent subscriber-impacting degradation

Business Impact

Revenue / value

Network SLA compliance; reduced NPS churn from degraded experience

Key constraint

Inference must run at or near the RAN — cloud round-trips introduce too much latency for meaningful intervention

Infrastructure Requirements

Requires edge inference nodes colocated with BBUs or distributed at sector level. Models trained on operator-specific KPI baselines, not generic benchmarks. Anomaly detection + classification = two inference calls, sub-100ms combined.

Edge Inference ArchitectureDual-Model PipelineOperator-Specific BaselinesBBU ColocationNEXUS OSSub-100ms Latency

Why Trinidy for Real-time RAN Anomaly Detection

NEXUS OS deploys inference nodes colocated with RAN equipment — latency budget preserved end-to-end
NEXUS Foundry trains anomaly models on your live KPI streams — not vendor benchmark datasets
Two chained inference calls complete in under 100ms on T4 DevCo silicon
Remediation actions execute on-site within policy guardrails — no cloud dependency in the critical path
Model updates pushed across all sites without downtime via NEXUS OS fleet management