1. Introduction: Why Throughput Optimization is Challenging in NTN

In terrestrial networks, throughput optimization is primarily driven by radio conditions, scheduling efficiency, and modulation schemes. However, in Non Terrestrial Networks (NTN), performance is impacted by additional constraints such as long propagation delay, Doppler effects, beam mobility, and uplink power limitations.

As a result, even with good signal quality, throughput may remain suboptimal. NTN shifts the problem from radio only optimization to end to end performance engineering.

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2. End to End Throughput Perspective

Throughput in NTN is influenced by multiple layers:

• Physical Layer: MCS and coding
• MAC Layer: Scheduling
• Transport Layer: Delay and retransmissions
• Network Layer: Gateway and core latency

A bottleneck in any layer can limit overall throughput, making cross layer optimization essential.

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3. Downlink vs Uplink Throughput Comparison

Aspect	Downlink (DL)	Uplink (UL)
Power Source	Satellite	UE
Main Limitation	Beam load / scheduling	UE transmit power
Coverage Impact	Moderate	High
Throughput Stability	Higher	More variable
Key Challenge	Congestion	Power limitation

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4. Impact of Modulation and Coding Scheme (MCS)

MCS defines how efficiently data is transmitted.

• High MCS → High throughput but requires good SINR
• Low MCS → Robust but lower throughput

In NTN:

• SINR fluctuates due to beam movement
• Doppler impacts decoding
• Conservative MCS reduces throughput but avoids retransmissions

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5. Scheduling Challenges in NTN

Schedulers rely on channel feedback, which is delayed in NTN due to high latency.

Aspect	Terrestrial Network	NTN
Feedback Delay	Very low	High
Channel Stability	Stable	Dynamic
Scheduling Accuracy	High	Reduced
Resource Efficiency	Optimized	Suboptimal

This leads to inefficient scheduling decisions and reduced throughput.

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6. Impact of Delay on Throughput

Latency significantly affects throughput in NTN.

Impact Area	Effect
HARQ	Slower retransmission cycles
TCP	Reduced window efficiency
Scheduling	Delayed decisions
User Experience	Increased buffering

Key Insight: Many NTN scenarios are latency limited rather than radio limited.

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7. HARQ and Retransmission Impact

HARQ ensures reliability through retransmissions, but in NTN:

• Retransmission cycles are longer
• Delay increases overall transmission time
• Excessive retransmissions reduce throughput

Optimization requires balancing reliability and delay impact.

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8. Coding Efficiency Trade Off

Coding improves reliability but impacts throughput.

Coding Type	Benefit	Impact
Strong Coding	Fewer errors	Lower throughput
Weak Coding	Higher throughput	More retransmissions

In NTN, avoiding retransmissions is often more beneficial than maximizing peak throughput.

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9. Beam Load Impact on Throughput

Throughput is directly affected by user distribution across beams.

• High load → Reduced per user throughput
• Low load → Higher throughput availability

Load balancing across beams is critical for maintaining consistent performance.

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10. KPI Indicators for Throughput Optimization

Key KPIs include:

• DL throughput
• UL throughput
• BLER
• Spectral efficiency
• Resource utilization
• Retransmission rate

Correlation between these KPIs is essential for accurate analysis.

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11. Root Cause Mapping

Symptom	Likely Cause
Low DL throughput	Beam congestion / scheduling inefficiency
Low UL throughput	UE power limitation
High BLER	Incorrect MCS
High retransmissions	Delay + aggressive MCS
Good SINR, low throughput	Latency limitation

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12. Practical Optimization Strategies

• Tune MCS thresholds for balance between throughput and reliability
• Implement delay aware scheduling
• Optimize HARQ parameters (processes, timers)
• Improve load balancing across beams
• Reduce transport latency where possible

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13. Conclusion: Throughput Optimization Requires Multi-Layer Thinking

Throughput optimization in NTN is not limited to radio tuning.

It requires:

• Cross-layer coordination
• Delay-aware optimization
• Intelligent scheduling
• Beam-aware load management

The key shift for engineers is moving from signal based optimization to system level performance engineering.

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