AI for Telecom Network Optimisation

Machine learning models trained on your network telemetry data and historical fault records to predict equipment failures and service degradation before they cause customer impact. For LTE and 5G NR networks, cell-level KPIs monitored include RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), SINR (Signal to Interference plus Noise Ratio), CQI (Channel Quality Indicator), PRB (Physical Resource Block) utilisation, handover success rate, and call drop rate -- the standard 3GPP KPI set that captures both coverage quality and capacity pressure at the cell level. Statistical baseline modelling uses a 28-day rolling Z-score baseline per KPI per cell, with anomaly detection triggering when Z exceeds 2.5 -- a threshold calibrated to balance sensitivity against alert fatigue based on your network's historical variance. For NR 5G NSA (Non-Standalone) and SA (Standalone) architectures, X2/Xn interface KPIs covering secondary cell addition and EN-DC (E-UTRA-NR Dual Connectivity) bearer setup success rates are included alongside LTE anchor cell KPIs to detect degradation patterns specific to dual-connectivity operation. Drive test data from TEMS Discovery or NEMO Outdoor is ingested and correlated with network-side KPIs to validate predictions against customer-experienced signal quality. Prediction output: probability of fault within a defined time window with the contributing signals surfaced for NOC engineer review. Early warning that shifts the NOC response from reactive to proactive.

Demand forecasting by network segment, geographic cell, and time period for proactive capacity management. Cell-level PRB utilisation trends are decomposed into organic growth, seasonal patterns, and event-driven spikes so the capacity model distinguishes durable demand growth from temporary peaks. SON (Self-Organising Network) parameter optimisation recommendations are generated automatically for cells where antenna tilt, transmit power, or load-balancing parameters are identified as contributing to utilisation imbalance -- for Ericsson, Nokia, and Huawei network elements, vendor-specific SON parameter sets are used to generate configuration recommendations that can be reviewed and applied via the NMS API (Ericsson Network Manager, Nokia NetAct, Huawei iMaster NCE). Fronthaul and backhaul capacity analysis covers the transport layer alongside radio capacity -- identifying cells where radio capacity is adequate but backhaul bandwidth is the limiting factor before a radio upgrade is commissioned. Capacity headroom reports identify segments approaching utilisation limits based on projected growth rate, giving your planning team a 90-day and 180-day forward view of where capacity investment is needed. SLA compliance tracking covers MTTR (Mean Time to Repair), MTTD (Mean Time to Detect), and network availability SLA targets per cell and per region, reported against your commercial SLA commitments. The evidence layer for capital expenditure prioritisation decisions.

Root cause acceleration for network incidents by correlating fault signals across network layers, equipment types, and geographic regions automatically. Network topology graph analysis maps neighbour cell relationships (the X2/Xn interface topology for LTE and NR), physical adjacency between sites, and shared transport segments -- so when a degradation pattern affects a cluster of cells, the system can determine whether the fault is at a shared upstream node (transport, baseband, power) or is cell-specific. When a performance incident is detected, the system queries historical fault patterns for similar signal combinations, correlated upstream and downstream KPI changes within the network topology graph, and equipment maintenance history from your OSS (NetCracker, Amdocs, or Ericsson OSS) to surface the most likely root cause candidates. OSS/BSS integration with NetCracker, Amdocs, Ericsson OSS, and Nokia NetAct brings in maintenance windows, configuration change events, and alarm history alongside the telemetry data so the system can identify whether a recent configuration change or planned maintenance correlates with an incident onset. Root cause hypotheses are presented with supporting evidence and confidence scores rather than requiring engineers to manually correlate data from five different monitoring systems. MTTD is reduced by surfacing relevant context within minutes of incident onset rather than requiring manual data assembly across platforms.

Quality of Service monitoring across your key performance indicators for LTE and 5G NR: call setup success rate, voice MOS (Mean Opinion Score), data throughput per cell and per user, PDCP layer packet loss rate, latency (user-plane round-trip time), RSRP, RSRQ, and SINR distribution across the coverage area. For each KPI at each cell level, a 28-day rolling Z-score baseline captures the normal variation pattern for that cell at that time of day and day of week. Anomaly detection triggers when the Z-score for a KPI exceeds 2.5 standard deviations from the rolling baseline -- sensitive enough to catch developing degradation before it crosses the static alarm thresholds in your NMS, but specific enough to avoid the alert fatigue that comes from threshold-based monitoring that fires on every minor fluctuation. Impact severity classification assesses each anomaly by affected user count (estimated from active session data), revenue exposure (based on the service type and SLA tier), and SLA compliance risk (MTTD and MTTR budget remaining before breach) to determine triage priority. The QoS intelligence that gives your NOC team a prioritised, evidence-based picture of service quality across every cell in the network without manually checking individual KPI dashboards in the NMS.

Operational dashboards for NOC teams, engineering managers, and senior leadership -- each role configured with the view relevant to their function. NOC operator view: real-time network health overview showing active incidents by severity, sites with degraded KPIs (RSRP, SINR, call drop rate, PRB utilisation above 80%), and maintenance windows in progress. Cell performance heat maps display throughput, utilisation, and QoS by geographic area so coverage gaps and congestion hotspots are visible without querying the NMS directly. Engineering view: trending KPI charts per cell over selectable time windows, handover success rate analysis across neighbour pairs, and drive test data overlay for coverage validation against network-side KPIs. Historical trend views support SLA reporting -- availability per cell, per site, and per region calculated against MTTR and network availability SLA targets. Incident timeline view for post-incident analysis shows the sequence of KPI changes, alarm events, configuration changes, and engineer actions from detection through resolution. Executive reporting: network availability percentage, customer-impacting event count, MTTD and MTTR trends, and SLA compliance status over the reporting period. Dashboards are built on your existing data infrastructure -- ingesting from your OSS, NMS, and telemetry store rather than requiring a separate data warehouse in most implementations.

Integration with your existing incident management and ticketing systems -- ServiceNow (via REST API and Flow Designer), Jira Service Management, BMC Remedy, or your custom ITSM platform. Automatic incident creation when AI detection triggers above a defined severity threshold, with the relevant KPI data (RSRP, SINR, call drop rate, PRB utilisation), Z-score anomaly details, root cause hypotheses, and affected cell list pre-populated in the ticket so the assigned engineer has the diagnostic context immediately without querying the NMS separately. Incident correlation prevents duplicate tickets when the same underlying fault generates alarms from multiple monitoring sources -- the system identifies that a common upstream cause links multiple cell alarms and creates a single parent incident with child correlations rather than five separate tickets for five cells that share a failed transport node. Escalation routing is based on fault type, affected customer count, SLA exposure, and time-of-day so a major outage during business hours follows a different path than a degradation event at 3am. MTTD and MTTR are tracked per incident and reported against your SLA targets over rolling 30-day, 90-day, and 12-month periods. Post-incident analysis exports the full incident timeline -- detection, first response, diagnosis, resolution -- for customer SLA reporting and internal review. The connection between AI detection and the human response workflow that acts on it, without requiring a change to your existing NOC processes.