A Multi-dimensional Multi-choice Knapsack Framework for Efficient Resource Allocation in LEO Satellite Networks

Large-scale Internet of Things (IoT) connections in dynamic low Earth orbit (LEO) satellite networks face significant challenges in uplink resource scheduling. This paper proposes a framework for optimizing spectral efficiency. The framework satisfies heterogeneous quality of service (QoS) requireme...

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Vydané v:IEEE internet of things journal s. 1
Hlavní autori: Huang, Yi, Li, Jin, Wu, Yonghan, Fan, Weixuan, Wang, Danshi, Zhang, Min
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 2025
Predmet:
Computational modeling
differentiated quality of service (QoS)
dynamic programming (DP)
Heuristic algorithms
Lagrangian relaxation
low Earth orbit (LEO) Satellite Networks
Low earth orbit satellites
multi-dimensional multi-choice knapsack problem (MMKP)
Optimization
Orbits
Processor scheduling
Quality of service
Resource management
Satellite constellations
Satellites
uplink scheduling
ISSN:2327-4662, 2327-4662
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Popis
Shrnutí:Large-scale Internet of Things (IoT) connections in dynamic low Earth orbit (LEO) satellite networks face significant challenges in uplink resource scheduling. This paper proposes a framework for optimizing spectral efficiency. The framework satisfies heterogeneous quality of service (QoS) requirements and dynamic buffer constraints under time-varying IoT traffic bursts. It integrates three critical aspects. First, it considers the spatial geometric relationship between satellites and ground user equipment (UE), which determines the connection duration. Second, it achieves service-specific QoS priorities through an adaptive weighting mechanism. Third, it addresses time-varying traffic patterns. Under time-varying resource constraints, the high-dimensional scheduling optimization problem is modeled as a multi-dimensional multi-choice knapsack problem (MMKP). A satellite selection scheme is proposed to efficiently solve the MMKP with mixed constraints. This scheme simplifies the three-dimensional knapsack problem (KP) into a two-dimensional one by taking connection duration into account. This reduction explicitly accounts for the space and time limitations of satellite-ground links. It also integrates service-specific priorities. Meanwhile, the scheme enables each satellite to handle its own computations and resource allocation independently. A binary split dynamic programming (BSDP) algorithm is developed to solve the two-dimensional KP. To compare performance, two large-scale integer optimization methods-the Lagrangian Relaxation Algorithm (LRA) and Branch and Bound (B&B)-were used to solve the KP. The results were compared with a perception-based greedy resource block (RB) allocation for the original resource allocation problem. Extensive simulations based on Starlink demonstrate the effectiveness of the proposed solution. When serving over 4000 UEs, the MMKP solution achieves a 46% gain in QoS compared to the greedy benchmark. It also achieves a 60.7% throughput gain. Additionally, BSDP performs almost as well as B&B. BSDP has approximately two orders of magnitude lower computational cost than LRA.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3634354