It is demonstrated that when designing user demand forecasting solutions for practical LEO deployments, prioritizing system scalability may be more valuable than chasing minor accuracy enhancements, suggesting that when designing system scalability solutions, prioritizing system scalability may be more valuable than chasing minor accuracy enhancements.
Abstract
In LEO satellite networks utilizing beam hopping (BH), resource allocation plans must be committed well in advance. This inherent operational delay necessitates predicting future user demand during the planning phase. Such predictive agility is particularly crucial for military applications, where unpredictable tactical environments demand low-latency, resilient communication links. However, existing forecasting models are typically evaluated based on standalone accuracy, ignoring their cross-layer impact on overall network performance. To address this gap, we evaluate two distinct demand forecasting solutions within a comprehensive, full-stack LEO satellite simulation compliant with DVB-S2X standards. Beyond prediction accuracy, we examine how incorporating user demand forecasts into BH plan generation impacts key network metrics, particularly delay and jitter. We evaluate these forecasting solutions alongside a static allocation baseline. Our results demonstrate that forecast-based dynamic planning reduces delay by 10-40% across the beams under certain load conditions compared to static allocation methods. Crucially, marginal improvements in predictive accuracy do not translate into proportional network metric gains. While the evaluated forecasting solutions differ by 14-16% in Normalized Mean Square Error (NMSE), this discrepancy yields less than a 1% reduction in delay and produces nearly identical jitter characteristics. These findings suggest that when designing user demand forecasting solutions for practical LEO deployments, prioritizing system scalability may be more valuable than chasing minor accuracy enhancements.
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.
Yang Liu, Wen Liu, Wenliang Lin et al.· Electronics· 0 citations
The results indicate that iScavenger provides configurable operating points in the latency–utilization trade-off, limiting additional Sticky-flow RTT while achieving higher background throughput than conservative baseline policies, and highlight the potential of short-term traffic-demand prediction for proactive contention management in ATSSS-enabled multi-access networks.
Shah M. Emad Uddin, Karl-Johan Grinnemo, Arunselvan Ramaswamy et al.· IEEE Open Journal of the Com...· 0 citations
Low Earth Orbit (LEO) satellite networks are expected to support global multimedia services in future sixth-generation (6G) systems, where streaming media constitutes a major traffic type with stringent Quality of Experience (QoE) requirements. However, existing satellite resource allocation schemes mainly optimize Quality of Service (QoS) metrics, overlooking streaming-specific characteristics such as playback buffering and user early departure, which may lead to severe QoE degradation and on-board energy waste. To address this issue, this paper proposes a QoE-oriented downlink power allocation scheme for on-demand streaming services in LEO satellite networks. We first formulate a long-term stochastic optimization problem that jointly minimizes playback stalling and energy waste caused by buffer overflow and user interruption, while guaranteeing long-term user QoE. By leveraging Lyapunov optimization, the original problem is transformed into deterministic per-slot subproblems. A low-complexity power allocation algorithm is then developed based on strong subgradient theory, with theoretical convergence guarantees established through the strong quasiconvexity analysis of the per-user objective function. Simulation results based on the practical OneWeb constellation demonstrate that the proposed algorithm substantially improves user Mean Opinion Score (MOS) while significantly reducing on-board energy waste. In particular, a microscopic analysis of per-user power dynamics reveals that the performance degradation of conventional QoS-centric schemes stems from their buffer-agnostic, channel-driven allocation, which causes severe resource imbalance and energy waste—an inherent limitation that the proposed scheme effectively circumvents through application-layer awareness.
Huazhi Feng, Feng Wang, Junyu Lai et al.· IEEE Open Journal of the Com...· 0 citations
The integration of Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) into 5G and upcoming 6G architectures introduces various challenges, including severe propagation delays, ultra-high base station mobility, and channel non-stationarity, complicating radio resource management of heterogeneous network slices. In this paper, we propose a deep reinforcement learning (DRL) meta-scheduler for twin-timescale resource allocation. Our solution adopts a decoupled Open Radio Access Network (RAN) architecture, in which a strategic 100 ms meta-scheduler selects scheduling policies for the different network slices using stale telemetry, while a fast-timescale MAC packet scheduler processes per-TTI user requests. The resulting Markov Decision Process captures non-stationary orbital dynamics and heterogeneous SLAs constraints via a TD3 agent. Simulation results under varying traffic load show that, unlike other solutions, the proposed meta-scheduler explicitly trades a statistically insignificant 1% capacity fraction (p>0.05) to strictly bound the variance and overall magnitude of RLC-layer queuing delay for Mission-Critical (MC) traffic. Crucially, it enforces this isolation without inducing the broadband slice starvation characteristic of standard maximum-CQI heuristics, establishing a robust foundation for 6G O-RAN NTN resource allocation.
Víctor Vilchez, T. P. C. de Andrade, Edward Hinojosa et al.· 0 citations
Network slicing is a fundamental technology in 5G and beyond networks, enabling multiple virtual networks to coexist over a shared physical infrastructure while supporting heterogeneous services with diverse quality‐of‐service (QoS) requirements. In this context, radio access network (RAN) slicing plays a critical role in allocating radio resource units to different service slices. A fundamental challenge in RAN slicing is the dynamic allocation of radio resources under fluctuating traffic demand and user mobility, where rare but severe tail‐latency events corresponding to extreme delays experienced by a small fraction of packets have a dominant impact on performance, particularly for latency‐critical ultrareliable low‐latency communication (URLLC) services. Although reinforcement learning has been extensively studied for RAN slicing, most existing RAN‐side approaches focus on optimizing average performance metrics and permit unconstrained exploration, which can result in instability and service‐level agreement (SLA) violations in safety‐critical scenarios. This paper proposes a tail‐latency‐aware proximal policy optimization (PPO) framework for dynamic multislice RAN resource allocation. The proposed approach integrates offline behavior cloning using ns‐3‐generated new radio (NR) traces with online policy refinement to enable continuous adaptation. The framework incorporates a slice‐aware state representation capturing traffic and mobility dynamics, an explicit tail‐latency penalty targeting the 95th‐percentile URLLC delay, a URLLC‐aware adaptive exploration mechanism, and an SLA‐driven safety layer with emergency resource reallocation. System‐level evaluations across three representative scenarios demonstrate that the proposed approach consistently outperforms baseline methods in overall SLA satisfaction and tail‐latency control, achieving above 92% overall SLA compliance and maintaining balanced compliance across all service slice types, particularly during sudden traffic spikes.
Unknown authors· International Journal of Com...· 0 citations
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
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