1. Introduction: Why KPI Framework in NTN Needs Rethinking

In terrestrial networks, KPI monitoring is relatively stable due to fixed cell locations, predictable propagation, and consistent backhaul behavior. However, in Non Terrestrial Networks (NTN), especially LEO based systems, the network becomes highly dynamic due to:

• Moving satellite beams
• Variable propagation delay
• Doppler effects
• Intermittent coverage windows
• Gateway dependent performance variability

As a result, traditional KPI frameworks are insufficient. A new NTN aware KPI monitoring strategy is required, one that correlates radio, transport, and orbital dynamics.

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2. NTN vs Terrestrial KPI Monitoring: Key Differences

Aspect	Terrestrial Networks	NTN (LEO-Based)
Cell Stability	Static cells	Moving beams
Coverage	Continuous	Time varying (satellite visibility)
Latency	Low, stable	High, variable
Interference	Predictable	Beam overlap + dynamic
Backhaul	Fiber/microwave	Feeder link constrained
Mobility	UE driven	Network + satellite driven

Practical Insight:

In NTN, KPI degradation is often not purely RF related, it may originate from satellite movement, feeder link congestion, or gateway switching.

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3. Key NTN KPI Categories for Monitoring

A robust NTN KPI framework should be divided into the following domains:

3.1 Access and Accessibility KPIs

• RACH Success Rate
• Initial Access Success Rate
• Paging Success Rate
• Call Setup Success Rate

Optimization Relevance:

• Sensitive to timing misalignment and propagation delay
• Affected by satellite distance variation and Doppler

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3.2 Retainability KPIs

• Call Drop Rate
• Session Drop Rate
• Beam Handover Failure Rate

Optimization Relevance:

• Strongly impacted by beam transitions and satellite movement
• Requires correlation with beam footprint changes

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3.3 Mobility KPIs

• Beam Handover Success Rate
• Reselection Success Rate
• Handover Interruption Time

Optimization Relevance:

• Unlike terrestrial HO, failures may be due to beam disappearance, not just radio conditions

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3.4 Throughput and Capacity KPIs

• DL/UL Throughput
• PRB Utilization per Beam
• Spectral Efficiency
• User Throughput Distribution

Optimization Relevance:

• Strongly tied to:
  • Beam load distribution
  • Gateway capacity
  • Scheduling efficiency

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3.5 Latency and Transport KPIs

• End to End Latency
• HARQ RTT Impact
• Packet Delay Variation (Jitter)

Optimization Relevance:

• Critical for real time services
• Influenced by:
  • Satellite altitude
  • Gateway routing
  • Inter-satellite links

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3.6 Radio Quality KPIs

• RSRP / RSRQ / SINR Distribution
• BLER (DL/UL)
• MCS Distribution

Optimization Relevance:

• Beam edge users show rapid degradation
• SINR fluctuates due to beam movement

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4. Beam Level KPI Monitoring: The Core Shift in NTN

In NTN, beam becomes the new cell.

Instead of traditional cell level KPIs, monitoring must be:

• Beam specific
• Time segmented (based on satellite pass)
• Geo correlated (location vs beam footprint)

Key Beam Level KPIs:

• Beam Load (PRB utilization per beam)
• Beam Throughput
• Beam Edge Performance (SINR/BLER at edges)
• Beam Handover Rate

Practical Insight:

A beam showing high drop rate may not indicate RF issue, it may indicate:

• Beam exit timing misalignment
• Poor handover threshold tuning

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5. Time Domain KPI Analysis (Critical for NTN)

Unlike terrestrial networks, NTN KPIs must be analyzed in time slices:

• Satellite pass duration (e.g., 5–15 minutes for LEO)
• Peak vs non peak visibility windows
• Entry/exit phases of beam coverage

Example Observations:

• Access failures spike at beam entry
• Drops increase near beam exit
• Throughput peaks mid pass

Optimization Strategy:

• Tune parameters differently for:
  • Beam entry phase
  • Stable coverage phase
  • Beam exit phase

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6. Correlating Multi-Layer KPIs (RAN + Transport + Orbit)

A key challenge in NTN is cross layer dependency.

Example Correlation Scenarios:

• High latency + good SINR → Transport / gateway issue
• Good RF + low throughput → Scheduler or backhaul bottleneck
• High drops + beam transition → Mobility parameter issue

Recommended Correlation Layers:

• RAN KPIs (RF + MAC)
• Transport KPIs (latency, packet loss)
• Satellite metrics:
  • Beam ID
  • Satellite ID
  • Elevation angle

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7. KPI Visualization Strategy for NTN

Traditional dashboards are insufficient. NTN requires:

• Geo mapped KPI visualization (beam footprint overlay)
• Time based KPI heatmaps
• Beam wise performance dashboards
• Satellite pass based analytics

Recommended Views:

• Map view: SINR / throughput per location
• Timeline view: KPI vs satellite pass
• Beam comparison: load balancing analysis

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8. Common NTN KPI Issues and Root Causes

KPI Issue	Likely Root Cause
Low RACH Success	Timing offset, Doppler misalignment
High Drop Rate	Beam exit without proper HO
Low Throughput	Beam congestion / feeder link bottleneck
High Latency	Gateway routing / ISL path
Poor SINR at edges	Beam shaping / power imbalance

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9. Practical Optimization Guidelines

• Monitor KPIs at beam level, not cell level
• Always correlate with satellite movement timeline
• Separate analysis for:
  • Beam entry
  • Mid coverage
  • Beam exit
• Combine RF + transport + orbital data before conclusion
• Use percentile based KPIs (not averages) due to variability

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10. Conclusion: From Static Monitoring to Dynamic Intelligence

NTN KPI monitoring is no longer a passive activity. It requires:

• Dynamic, time aware analysis
• Beam centric monitoring approach
• Cross layer correlation
• Real time adaptation to satellite movement

For RF optimization engineers transitioning into NTN, mastering KPI interpretation in this dynamic environment is one of the most critical skills for real world deployments.

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