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Beam Management in Low Earth Orbit Satellite Networks with Random Traffic Arrival and Time-varying Topology

Jianfeng Zhu, Yaohua Sun, Mugen Peng

TL;DR

The paper tackles beam management in dynamic LEO satellite networks with random traffic and time-varying topology. It develops a Lyapunov optimization framework that decomposes the long-term objective into epoch-level tasks, and further splits the per-epoch problem into three subproblems: serving beam allocation, beam service time allocation, and serving satellite allocation. These are solved via a conflict-graph based MWIS approach, a time-service balancing algorithm, and a simulated-annealing driven satellite assignment, respectively, with INR constraints simplified to off-axis angle rules. Numerical results show substantial reductions in average beam revisit time (approximately 20% relative to strong baselines) while maintaining stable inter-satellite handover rates, demonstrating scalable performance in mega-constellation scenarios.

Abstract

Low earth orbit (LEO) satellite communication networks have been considered as promising solutions to providing high data rate and seamless coverage, where satellite beam management plays a key role. However, due to the limitation of beam resource, dynamic network topology, beam spectrum reuse, time-varying traffic arrival and service continuity requirement, it is challenging to effectively allocate time-frequency resource of satellite beams to multiple cells. In this paper, aiming at reducing time-averaged beam revisit time and mitigate inter-satellite handover, a beam management problem is formulated for dynamic LEO satellite communication networks, under inter-cell interference and network stability constraints. Particularly, inter-cell interference constraints are further simplified into off-axis angle based constraints, which provide tractable rules for spectrum sharing between two beam cells. To deal with the long-term performance optimization, the primal problem is transformed into a series of single epoch problems by adopting Lyapunov optimization framework. Since the transformed problem is NP-hard, it is further divided into three subproblems, including serving beam allocation, beam service time allocation and serving satellite allocation. With the help of conflict graphs built with off-axis angle based constraints, serving beam allocation and beam service time allocation algorithms are developed to reduce beam revisit time and cell packet queue length. Then, we further develop a satellite-cell service relationship optimization algorithm to better adapt to dynamic network topology. Compared with baselines, numerical results show that our proposal can reduce average beam revisit time by 20.8% and keep strong network stability with similar inter-satellite handover frequency.

Beam Management in Low Earth Orbit Satellite Networks with Random Traffic Arrival and Time-varying Topology

TL;DR

The paper tackles beam management in dynamic LEO satellite networks with random traffic and time-varying topology. It develops a Lyapunov optimization framework that decomposes the long-term objective into epoch-level tasks, and further splits the per-epoch problem into three subproblems: serving beam allocation, beam service time allocation, and serving satellite allocation. These are solved via a conflict-graph based MWIS approach, a time-service balancing algorithm, and a simulated-annealing driven satellite assignment, respectively, with INR constraints simplified to off-axis angle rules. Numerical results show substantial reductions in average beam revisit time (approximately 20% relative to strong baselines) while maintaining stable inter-satellite handover rates, demonstrating scalable performance in mega-constellation scenarios.

Abstract

Low earth orbit (LEO) satellite communication networks have been considered as promising solutions to providing high data rate and seamless coverage, where satellite beam management plays a key role. However, due to the limitation of beam resource, dynamic network topology, beam spectrum reuse, time-varying traffic arrival and service continuity requirement, it is challenging to effectively allocate time-frequency resource of satellite beams to multiple cells. In this paper, aiming at reducing time-averaged beam revisit time and mitigate inter-satellite handover, a beam management problem is formulated for dynamic LEO satellite communication networks, under inter-cell interference and network stability constraints. Particularly, inter-cell interference constraints are further simplified into off-axis angle based constraints, which provide tractable rules for spectrum sharing between two beam cells. To deal with the long-term performance optimization, the primal problem is transformed into a series of single epoch problems by adopting Lyapunov optimization framework. Since the transformed problem is NP-hard, it is further divided into three subproblems, including serving beam allocation, beam service time allocation and serving satellite allocation. With the help of conflict graphs built with off-axis angle based constraints, serving beam allocation and beam service time allocation algorithms are developed to reduce beam revisit time and cell packet queue length. Then, we further develop a satellite-cell service relationship optimization algorithm to better adapt to dynamic network topology. Compared with baselines, numerical results show that our proposal can reduce average beam revisit time by 20.8% and keep strong network stability with similar inter-satellite handover frequency.
Paper Structure (26 sections, 42 equations, 10 figures, 2 tables, 3 algorithms)

This paper contains 26 sections, 42 equations, 10 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. SYSTEM MODEL
  3. Scenario description
  4. Inter-satellite handover strategy
  5. The modeling of inter-cell interference
  6. Problem Formulation And Transformation
  7. Problem formulation
  8. Serving satellite allocation constraints
  9. Beam allocation constraints
  10. Maximal INR constraints
  11. The objective function of beam management
  12. Problem formulation
  13. Problem transformation
  14. The simplification of INR constraints
  15. Problem Decomposition and Beam Management Algorithm Design
  16. ...and 11 more sections

Figures (10)

  • Figure 1: Figure (a) is the concerned multi-beam LEO satellite network scenario, and figure (b) provides a serving time allocation plan example of cell $c$.
  • Figure 2: The interference scenario between two satellite-cell pairs.
  • Figure 3: Beam management problem decomposition.
  • Figure 4: An example of constructed conflict graph.
  • Figure 5: Figure (a) is the unbalanced cell traffic demand expectation in simulations and (b) shows the average beam revisit time of cells.
  • ...and 5 more figures