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Constellation as a Service: Tailored Connectivity Management in Direct-Satellite-to-Device Networks

Feng Wang, Shengyu Zhang, Een-Kee Hong, Tony Q. S. Quek

arXiv:2507.00902v1eess.SYcs.AIeess.SP

TL;DR

Multi-constellation DS2D must manage interference, frequent handovers, and complex resource allocation more flexibly than single-constellation approaches. CaaS pools heterogeneous satellites, dynamically forms demand-specific sub-constellations, and combines GenAI beamforming with pre-configured handovers; simulations report nearly three times higher ATR at 40 UEs and over 50% lower handover frequency than the standalone strategy.

  • Problem

    Multi-constellation DS2D connectivity is challenged by overlapping-coverage interference, frequent handovers, rapid satellite movement, and complex resource management.

  • Method

    CaaS virtualizes heterogeneous constellations into a shared resource pool, dynamically forms regional sub-constellations, and uses GenAI beamforming with pre-configured handover sequences.

  • Results

    Nearly three times higher ATR at 40 UEs and over 50% lower HO frequency are reported for CaaS than the standalone strategy.

  • Takeaways & Limitations

    CaaS enables demand-tailored DS2D connectivity by combining satellites across orbital altitudes and reducing handover overhead in multi-constellation environments.

Abstract

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Direct-satellite-to-device (DS2D) communication is emerging as a promising solution for global mobile service extension, leveraging the deployment of satellite constellations. However, the challenge of managing DS2D connectivity for multi-constellations becomes outstanding, including high interference and frequent handovers caused by multi-coverage overlap and rapid satellite movement. Moreover, existing approaches primarily operate within single-constellation shell, which inherently limits the ability to exploit the vast potential of multi-constellation connectivity provision, resulting in suboptimal DS2D service performances. To address these challenges, this article proposes a Constellation as a Service (CaaS) framework, which treats the entire multi-constellation infrastructure as a shared resource pool and dynamically forms optimal sub-constellations (SCs) for each DS2D service region. The formation of each SC integrates satellites from various orbits to provide tailored connectivity based on user demands, guided by two innovative strategies: predictive satellite beamforming using generative artificial intelligence (GenAI) and pre-configured handover path for efficient satellite access and mobility management. Simulation results demonstrate that CaaS significantly improves satellite service rates while reducing handover overhead, making it an efficient and continuable solution for managing DS2D connectivity in multi-constellation environments.

I. INTRODUCTION

Multi-constellation DS2D can extend mobile connectivity, but overlapping coverage, changing satellite conditions, frequent handovers, and large-scale optimization make connectivity management difficult. CaaS addresses these challenges by pooling constellation resources and dynamically tailoring regional sub-constellations with predictive beamforming and pre-configured handovers.

  • Motivation: Multi-constellation DS2D extends mobile services toward ubiquitous connectivity, including remote and underserved areas.LEO constellations support direct device connections with low latency and high capacity.
  • Challenges: Multi-coverage creates interference and frequent handovers, while rapid movement, uneven traffic, and constellation scale complicate real-time optimization.These conditions increase the need for adaptive CSI estimation, beamforming, and mobility management.
  • Framework: CaaS virtualizes all satellite constellations into a shared resource pool for centralized control and dynamic connectivity allocation.Regional controllers use satellite traffic density and predictable orbits to support regional resource management.
  • Framework: CaaS dynamically forms regional sub-constellations from suitable satellites across multiple constellations and adjusts their connectivity configurations.This customization is intended to match regional DS2D service demands.
  • Contributions: GenAI-based predictive beamforming and pre-configured handover mechanisms provide the framework’s access and mobility management strategy.The two techniques target adaptive interference mitigation, service continuity, lower handover frequency, and reduced signaling overhead.

II. CONSTELLATION AS A SERVICE FRAMEWORK

The CaaS framework combines satellite-side radio management with pre-configured mobility management to maintain DS2D connectivity under dynamic multi-constellation coverage. Its design addresses interference, excessive handovers, and complex connectivity requirements through predictive CSI-driven beamforming and planned handover paths.

  • Framework overview: CaaS provides a multi-layer framework for managing DS2D connectivity across multiple satellite constellations.The framework is introduced to address connectivity management as industry and standardization efforts expand DS2D services.
  • Connectivity types: DS2D connectivity can be single, dual, or heterogeneous, with the selected type adapting to coverage, traffic load, and user requirements.Dual connectivity can use satellites from the same or different constellations, while heterogeneous connectivity combines satellite and terrestrial links.
  • Onboard radio management: Onboard satellite processing manages UE access, mobility, and throughput while connecting satellites to gateways and core networks through direct links or ISLs.This supports cooperative connectivity management across the space and ground segments.
  • Access management: GenAI-based CSI estimation and beamforming address rapidly changing channels, traffic demands, and interference from overlapping satellite coverage.The approach is designed for real-time adaptability in dynamic NTN conditions.
  • Mobility management: Pre-configured handover paths use predictable coverage dynamics to reduce decision latency, ping-pong handovers, and signaling overhead.The handover process advances preparation, monitoring, and execution according to predictable satellite movement and time of stay.

C. Open Constellation Control Layer

The open constellation control layer abstracts heterogeneous satellites into a shared pool and dynamically configures regional sub-constellations. Regional controllers and formation units align satellite selection and sub-constellation lifecycle management with traffic density and UE requirements.

  • Layer architecture: The control layer customizes and manages regional sub-constellations through a control unit and an SC formation unit.These components coordinate constellation resources, regional updates, satellite assignment, and SC lifecycle management.
  • Control unit: The control unit combines central and regional controllers with terrestrial control entities to capture satellite traffic and UE DS2D demands.It is responsible for constellation resource management and sub-constellation coordination.
  • SC formation unit: The SC formation unit dynamically configures regional sub-constellations from an open resource pool and updates them through interaction with regional controllers.The resource pool supports SC design, satellite assignment, interaction, and lifecycle management.
  • Dynamic configuration: Sub-constellation size is determined by regional traffic density, while composition is refined according to UE requirements.Satellite selection can favor higher-altitude satellites for longer coverage or VLEO satellites for lower latency and higher transmission efficiency.
  • Operational consequence: Virtualizing heterogeneous satellites enables operators to design sub-constellations around user requirements and use satellites at different orbital altitudes.The layer is presented as a way to improve multi-constellation resource utilization and DS2D service provision.

III. DS2D ONBOARD RADIO MANAGEMENT: SCALABILITY AND CONTINUITY

The onboard radio management layer addresses dynamic CSI, interference, and handover instability in multi-constellation DS2D networks. It combines transformer-based CSI prediction with interference-aware beamforming and handover metrics that account for service capability and satellite stay time.

  • Radio-management challenges: Multi-constellation DS2D experiences dynamic CSI from satellite movement, atmospheric variation, overlapping-coverage interference, and long propagation distances.These conditions complicate reliable radio-resource and mobility management.
  • Transformer-based CSI estimation: A transformer-based GenAI model estimates satellite CSI from time-dependent data, including incomplete or outdated information.Its self-attention mechanism weights historical CSI samples to generate CSIT for dynamic NTN conditions.
  • CSI training workflow: The CSI workflow uses pre-training, environment-specific fine-tuning, and iterative evaluation across varying NTN conditions.Historical channel gain, path loss, and Doppler-shift sequences form the foundation of training.
  • Interference management: Predicted CSI supports interference-matrix construction, power allocation, and beamforming to minimize inter-satellite interference.The evaluation phase connects CSI prediction with real-time interference management.
  • Handover management: CaaS selects cross-constellation handover targets using link capability and remaining time of stay, with user-specific metric weighting.Conditional handover advances preparation to accommodate signaling delays across satellite altitudes.

1) Spatial Satellite Signal Distributions:

High LEO-satellite mobility creates challenges for maintaining DS2D connectivity.

  • High LEO-satellite mobility challenges continuous DS2D connectivity maintenance.

2) Predictive Satellite Beamforming:

The paper combines predictive beamforming with a graph-based, pre-configured handover strategy to manage changing satellite coverage and reduce inefficient handovers.

  • Predictive Satellite Beamforming: Predictive beamforming uses historical CSI sequences and GenAI to generate future beamforming parameters.The approach predicts future CSIT instead of relying only on feedback from the last communication round.
  • Handover Graph Model: A handover graph represents satellites as time-ordered vertices, feasible coverage-overlap handovers as directed edges, and UE-specific benefits as weights.A path from the UE source vertex to the final satellite represents a possible handover sequence.
  • Pre-configured Handover: The source satellite computes cumulative benefits across candidate sequences so the UE can pre-select an optimized handover path.This design targets fewer handover detours and lower signaling overhead.

3) HO Procedure in Multi-constellations:

The proposed procedure prepares handovers in advance using predicted satellite movement and shared handover sequences, supporting continuous DS2D service with lower mobility overhead.

  • Handover Preparation: During advanced handover preparation, the source satellite predicts orbital movement, requests candidate satellites, and computes optimal handover sequences.The UE monitors handover benefits and makes decisions using the prepared information.
  • Sequence Sharing: The source satellite shares the handover sequence with involved satellites in advance to reduce signaling overhead and support seamless transitions.In dual-connectivity mode, both source satellites use multipath finding in the handover graph model to optimize UE sequences.
  • Integrated Mobility Management: The integrated mobility strategy minimizes handover frequency and signaling overhead while supporting GenAI CSI collection and continuous DS2D connectivity.The paper describes interaction between pre-configured handovers and optimized satellite access over long-term service provision.

1) Traffic Density Based Region Division:

CaaS divides the global area according to UE traffic density and selects satellites for regional sub-constellations using service, capacity, and mobility conditions.

  • Traffic Density Based Region Division: High-traffic areas are divided into smaller regions, while low-traffic areas use larger regions to balance connectivity-management load.Each region is overseen by a regional sub-constellation controller.
  • Satellite Selection: Sub-constellation initialization considers UE requirements including signal strength, latency, throughput, and connectivity period.
  • Satellite Selection: Satellite selection also considers coverage and capacity, including connection limits per satellite.
  • Satellite Selection: Satellite mobility is included to maintain seamless regional coverage and allocate sufficient resource-pool capacity for DS2D provisioning.

3) Optimized DS2D Connectivity:

CaaS customizes sub-constellations and DS2D connectivity management for each service region by combining satellite selection, beamforming, and pre-configured handover sequences. The evaluation compares this approach with standalone connectivity as UE density increases.

  • Connectivity management: CaaS applies advanced satellite access and mobility management within each sub-constellation to optimize beamforming and enhance handover continuity.Beamforming maximizes gain while minimizing interference, while pre-configured handover sequences reduce signaling overhead during rapid satellite movement.
  • Evaluation setup: The evaluation uses STK simulations with two LEO constellations, including Starlink- and OneWeb-like configurations.
  • Evaluation setup: Fig. 5 compares average transmission rates and handover frequency per DS2D connectivity as the number of UEs increases.
  • Sub-constellation design: CaaS dynamically selects satellites from both constellations to form sub-constellations and applies advanced connectivity management approaches.
  • Baseline: The standalone strategy keeps each UE within its default constellation and bases connectivity and handover decisions solely on signal strength.

Numerical results:

CaaS improves DS2D performance over the standalone strategy as UE density varies, increasing transmission rates and reducing handover frequency. The section also identifies spectrum sharing, multi-constellation orchestration, low-altitude connectivity, and further 6G integration as open directions.

  • Numerical results: Nearly 3× higher ATR at 40 UEs is achieved by CaaS than by the standalone strategy.The reported gain is attributed to optimized beamforming that assigns satellites and beams from two constellations according to signal conditions and UE demands.
  • Numerical results: Over 50% lower HO frequency is achieved by CaaS through pre-configured HO sequences.The sequences reduce ping-pong handovers caused by alternating coverage between constellations, particularly at high UE densities.
  • Open research issues and directions: Spectrum sharing within and between TN and NTN remains important for DS2D capacity, throughput, bandwidth, and seamless mobility.
  • Open research issues and directions: Flexible multi-constellation orchestration can assign traffic across MEO and LEO according to delay and stability requirements.MEO can also serve as a load-balancing partner for LEO.
  • Open research issues and directions: Low-altitude DS2D deployment must address UAV mobility, dynamic segment switching, and interference mitigation in co-existed TN and NTN.
  • Open research issues and directions: Further work is needed to integrate digital twins, reconfigurable intelligent surfaces, and onboard multicast-broadcast services into DS2D architectures.The stated goal is to optimize constellation operations and quality of service.

VI. CONCLUSIONS

The conclusion presents CaaS as a resource-pooling framework for tailoring DS2D services across heterogeneous constellations. It combines GenAI-based beamforming with pre-configured handover sequences, reports improved service rates and reduced mobility overhead, and identifies further investigation needs.

  • VI. CONCLUSIONS: CaaS virtualizes heterogeneous constellations into a resource pool so operators can customize DS2D services for different demands.
  • VI. CONCLUSIONS: GenAI-based beamforming and pre-configured satellite handover sequences drive regional sub-constellation configuration for interference and mobility management.
  • VI. CONCLUSIONS: Experimental results show that CaaS significantly increases DS2D service rates while reducing mobility overhead compared with static DS2D strategies.
  • VI. CONCLUSIONS: The article identifies several aspects requiring further investigation to fully unlock future DS2D service potential.
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