Aug 2026· Electronics· Vol 15, pp. 3587· 0 citations· 20 references
TL;DR
A rolling-horizon migration-aware dynamic greedy control node placement algorithm (RH-MA-DGCNP) is proposed, which updates the CN placement and the affiliation between access-layer satellites and CNs at each reconfiguration epoch while jointly considering the handover delay and the migration delay caused by transferring control-affiliation states from previous serving CNs to new serving CNs.
Abstract
Low Earth Orbit (LEO) communication networks are an important component of non-terrestrial networks (NTNs) in sixth-generation (6G) communication systems. LEO satellites are characterized by low propagation delay and highly time-varying topology. Space-based mobility management can effectively reduce transmission delay; however, rapid network variation makes space-based control node deployment and reconfiguration difficult to model and solve. Focusing on dual-layer LEO Walker constellations, this paper investigates the joint optimization of control node deployment and dynamic reconfiguration, and formulates a 0–1 mixed-integer linear programming model with multiple practical constraints, aiming to minimize the total handover and migration delay. The model incorporates practical constraints such as the CN resource budget, unique management of access layer satellites, inter-layer reachability, feeder link connectivity, non-empty control node (CN) management, and onboard resource capacity. To support online deployment, we propose a rolling-horizon migration-aware dynamic greedy control node placement algorithm (RH-MA-DGCNP), which updates the CN placement and the affiliation between access-layer satellites and CNs at each reconfiguration epoch while jointly considering the handover delay and the migration delay caused by transferring control-affiliation states from previous serving CNs to new serving CNs. A comparison with exact current-epoch MILP solutions obtained by CPLEX on validation instances shows that RH-MA-DGCNP achieves small optimality gaps with shorter computation time. Simulation results show that RH-MA-DGCNP achieves the lowest mean handover delay among all benchmark schemes and the lowest cumulative total delay cost among the quasi-dynamic and dynamic benchmark schemes. The CDF of handover delay further indicates that RH-MA-DGCNP has a higher proportion of low-delay handover events and effectively suppresses extremely high-delay handover cases. Sensitivity analyses under different elevation angle thresholds and ground station deployments further show that RH-MA-DGCNP maintains its performance advantage over the benchmark schemes under different network settings.
Low Earth Orbit (LEO) satellite constellations are emerging as an important platform for distributed dataflow execution in space-terrestrial integrated networks. Existing studies largely treat routing and processing separately, while next-generation LEO systems are expected to process and transform data in transit by leveraging on-board computing and software-defined infrastructures. However, jointly optimizing routing and in-network processing in dynamic LEO satellite networks remains challenging because of time-varying connectivity, limited on-board resources, and bandwidth constraints. In this paper, we formulate the Dynamic LEO In-network Processing Dataflow Optimization (DLIDO) problem, which aims to maximize the throughput of processed dataflows by jointly optimizing routing paths and processing-resource allocation over a dynamic flow network. We present an approximation algorithm with a proven $(1-\epsilon)$ approximation guarantee for $0\lt \epsilon \leq 0.5$ , providing near-optimal throughput under dynamic processing and communication constraints. To further improve efficiency and practicality, we develop a 2-walk based iterative heuristic algorithm that substantially reduces runtime while maintaining strong empirical performance, and in some regimes provably optimal behavior. Extensive evaluations on realistic LEO network topologies show that both algorithms significantly outperform existing approaches in throughput and adaptability, highlighting a promising direction for dataflow-aware scheduling and optimization in dynamic satellite systems.
Xili Wan, Fuliang Yang, Xin-Jie Guan et al.· IEEE Transactions on Network...· 0 citations
A comprehensive and structured review of methods for MACNs, with particular emphasis on AI-driven solutions and their relationship to classical and hybrid alternatives, and offers insights into the design of AI-driven MACNs that are efficient, scalable, and adaptive to evolving network and service demands.
Shafkat Khan Siam, Muhammad Yeasir Arafat, Muhammad Morshed Alam et al.· Artificial Intelligence Revi...· 0 citations
The proposed multi-agent reinforcement learning policy attains slightly higher throughput with fewer handovers by offloading a fraction of the users to the MEO and GEO layers, an emergent multi-orbit behavior that drives its favorable throughput and handover trade-off.
Yassine Afif, Ashutosh Balakrishnan, Philippe Martins et al.· 0 citations
Efficient Non-Access Stratum (NAS) timer configuration is critical for reliable and energy-efficient Fifth Generation (5G) registration in Non-Terrestrial Network (NTN)-Terrestrial Network (TN) integrated systems, where Low Earth Orbit (LEO) satellite access introduces large registration bursts, heterogeneous propagation paths, and multi-hop satellite routing. Existing 3GPP NAS timers use fixed values, while prior closed-form timer models compute a global timer under network-level assumptions; both fail to capture user equipment (UE)-level differences in propagation delay, Access and Mobility Management Function (AMF) arrival position, and path reliability. In this paper, we propose a location-aware, UE-specific NAS timer optimization method for LEO NTN-TN integrated networks. The proposed method models the path-delay component using service-link geometry, ground-station distance, and Inter-Satellite Link (ISL) hop count, and adapts the endpoint-delay component according to each UE's expected AMF queue exposure and path reliability. Simulation results show that our method reduces registration latency, UE energy consumption, and avoidable registration attempts compared with fixed and global timer configurations, especially when timer over-provisioning causes unnecessary waiting.
Unmanned aerial vehicle (UAV)-mounted 5G New Radio base stations (gNBs) can augment terrestrial networks with an on-demand, repositionable Frequency Range 2 (FR2) capacity layer. This flexibility, however, couples the physical network topology with radio-resource management: UAV movement reshapes blockage, channel quality, and the set of effectively served users, while traffic demand, queues, and service requirements evolve at a much faster timescale. Existing Open Radio Access Network (O-RAN)-enabled UAV studies optimize trajectory, deployment, association, or resource allocation, but typically in isolation, without coordinating slow aerial control with fast per-user scheduling. We instead exploit O-RAN disaggregation, Key Performance Indicator (KPI) monitoring, and multi-timescale RAN Intelligent Controller (RIC) control to address this coupling: a Non-Real-Time RIC rApp uses aggregated KPIs and radio-environment context to jointly control tethered UAV placement and the enhanced Mobile Broadband (eMBB)/Ultra-Reliable Low-Latency Communication (URLLC) slice budget, while a Near-Real-Time RIC xApp allocates per-user resources within that budget. We realize this xApp as a permutation-equivariant DeepSets Soft Actor-Critic (D-SAC) scheduler that treats the users as an unordered set, trained in a Sionna RT ray traced channel. The resulting hierarchical controller improves eMBB SLA satisfaction by up to 17% and URLLC on-time delivery by up to 42% over classical and learned schedulers; the learned rApp further raises URLLC on-time delivery by up to 20% over baselines.
In temporary emergency communication coverage scenarios where terrestrial communication infrastructure is damaged or lacks sufficient capacity, UAVs equipped with base stations have emerged as an effective solution due to their flexible deployment and rapid response capability. However, in multi-UAV networks, the three-dimensional deployment of UAVs significantly affects air-to-ground link quality, while power allocation further determines the level of system interference and throughput performance. To address this issue, this paper considers a multi-UAV communication system and jointly takes into account user link reliability and service requirement satisfaction, thereby establishing a joint optimization model for QoS-constrained coverage and network throughput. To address the non-convex joint optimization problem, a problem-tailored dual-population cooperative NSGA-II framework, termed IDPC-NSGA-II, is developed. By coupling dual-population evolution, adaptive mutation, uncovered-user-guided local search, and interference-aware repair with the characteristics of multi-UAV emergency communications, the proposed method improves the trade-off between QoS-constrained coverage and network throughput. Simulation results in a representative emergency communication scenario show that the proposed method achieves a favorable trade-off between QoS-constrained coverage and throughput, and outperforms the compared algorithms under the considered network setting.
Gui-Fen Chen, Ruiyang Liu· Digital Signal and Computer...· 0 citations
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