FTA-NTN: Fairness and Throughput Assurance in Non-Terrestrial Networks

2026-01-27

AI Summary

This study addresses the longstanding trade-off between system throughput and user fairness in non-terrestrial networks (NTNs) by proposing a novel multi-objective optimization framework that jointly optimizes both metrics for the first time. The framework integrates a multi-layer Walker Delta constellation, a parameterized user mobility model, adaptive K-Means clustering, and Bayesian optimization to achieve efficient and balanced resource allocation under practical system constraints. Experimental results demonstrate that, in a 500-user scenario, the proposed approach attains a total system throughput of 9.88 Gbps with an average fairness index of 0.42, validating the optimality of a hybrid LEO (9 planes × 15 satellites) and MEO (7 planes × 3 satellites) configuration. These findings align with 3GPP NTN evaluation guidelines and represent a significant departure from conventional throughput-centric design paradigms.

Technology Category

• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Designing optimal non-terrestrial network (NTN) constellations is essential for maximizing throughput and ensuring fair resource distribution. This paper presents FTA-NTN (Fairness and Throughput Assurance in Non-Terrestrial Networks), a multi-objective optimization framework that jointly maximizes throughput and fairness under realistic system constraints. The framework integrates multi-layer Walker Delta constellations, a parametric mobility model for user distributions across Canadian land regions, adaptive K-Means clustering for beamforming and user association, and Bayesian optimization for parameter tuning. Simulation results with 500 users show that FTA-NTN achieves over 9.88 Gbps of aggregate throughput with an average fairness of 0.42, corresponding to an optimal configuration of 9 planes with 15 satellites per plane in LEO and 7 planes with 3 satellites per plane in MEO. These values align with 3GPP NTN evaluation scenarios and representative system assumptions, confirming their relevance for realistic deployments. Overall, FTA-NTN demonstrates that throughput and fairness can be jointly optimized under practical constraints, advancing beyond throughput-centric designs in the literature and offering a scalable methodology for next-generation NTN deployments that supports efficient and equitable global connectivity.

Problem

• Non-Terrestrial Networks
• Throughput
• Fairness
• Resource Allocation
• Multi-objective Optimization

Innovation

• multi-objective optimization
• non-terrestrial networks
• fairness-throughput tradeoff
• adaptive K-Means clustering
• Bayesian optimization