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.

Satellite-terrestrial integrated network (STIN) plays a crucial role in achieving 3-dimensional full-area coverage. STIN enables the Internet of Things (IoT) industry to realize the integrated space-air-ground communication. The stability of satellite-terrestrial communication and the quality of service (QoS) in low earth orbit optical satellite networks (LEO-OSNs) need to be improved, especially for satellite-based IoT (SIoT) services. To address these challenges, we propose the heuristics multi-physical channel switching and dual Hamming distance-based routing and wavelength assignment (RWA) scheme (HMPS-DHR). Based on dual HAPs deployment architecture and link conditions-aware signal-to-noise ratio (SNR) thresholds model, the multi-physical channels can be flexibly switched among free-space optical (FSO) laser links, Ka-band and S-band microwave links to ensure the stability of the satellite-terrestrial feedback links (FLs). Meanwhile, the traffic conflict gain-adaptive and load-aware dual Hamming distance RWA (TCG-LDHR) algorithm is proposed to optimize the routing, address the RWA problem, and enhance QoS. Simulation results demonstrate that the proposed HMPS-DHR effectively guarantees the communication success rates between satellite and ground at approximately 98.9% to 99.2%, and improves the QoS metrics involving total delay, average throughput, packet loss rate, and blocking rate, by 15.6% to 56.4% compared with the Dijkstra-FF and the ant colony optimization with adaptive load balance small window strategy under hop number loose constraint (ACO-ALB-SWS-HNLC), respectively. HMPS-DHR shows acceptable robustness to synchronization deviations despite unavoidable millisecond-level timing mismatches. Although the QoS performance of the proposed HMPS-DHR is slightly lower than that of the integrated multipath network coding (IMPNC) scheme, its computational complexity is significantly reduced.

Low Earth orbit (LEO) satellite networks, as a fundamental component of 6G networks, are designed to provide full coverage, low latency, and high quality of service (QoS) for satellite-terrestrial integrated networks (STIN). Topology representations and routing computation in dynamic LEO satellite networks have become key research focuses. However, balancing network dynamics with traffic load remains challenging due to inaccurate topology representation and inefficient routing in existing studies. To address this, we propose a timeslot-adaptive and traffic load-aware routing computation (TA-TLARC) scheme for two-layer LEO satellite networks. The two-layer LEO satellite networks consist of communication layer satellites (CLS) and relay and sensing layer satellites (RSLS). TA-TLARC adaptively adjusts timeslots based on traffic variations and utilizes distributed adjacency matrices for routing computation. Simulation results show that TA-TLARC achieves better performance than existing routing schemes in key QoS metrics such as routing success rate, delay, throughput, and packet loss rate. Although routing hops and power consumption increase within acceptable limits, the routing success rate of TA-TLARC remains 99.6% to 100%. The QoS performance, including delay, throughput, and packet loss rate, is improved by 10% to 40% compared to those of the comparative schemes under different traffic scenarios. The robustness of TA-TLARC is further analyzed and demonstrated to be acceptable under various failure conditions. The results demonstrate that the proposed TA-TLARC effectively addresses routing computation challenges and significantly improves QoS performance in two-layer LEO satellite networks.

Large-scale transmission network (LSTN) puts forward high requirements to 6G in quality of service (QoS). In the LSTN, bounded and low delay, low packet loss rates, and controllable bandwidth are required to provide guaranteed QoS, involving techniques from the network layer and physical layer. In those techniques, routing computation is one of the fundamental problems to ensure high QoS, especially for bounded and low delay. Routing computation in LSTN researches include the routing recovery based on searching and pruning strategies, individual-component routing and fiber connections, and multi-point relaying (MRP)-based topology and routing selection. However, these schemes reduce the routing time only through simple topological pruning or linear constraints, which is unsuitable for efficient routing in LSTN with increasing scales and dynamics. In this paper, an efficient and comprehensive {routing computation algorithm namely multi-factor assessment and compression for network topologies (MC) is proposed. Multiple parameters from nodes and links in networks are jointly assessed, and topology compression for network topologies is executed based on MC to accelerate routing computation. Simulation results show that MC brings space complexity but reduces time cost of routing computation obviously. In larger network topologies, compared with classic and advanced routing algorithms, the higher performance improvement about routing computation time, the number of transmitted service, average throughput of single routing, and packet loss rates of MC-based routing algorithms are realized, which has potentials to meet the high QoS requirements in LSTN.

Network slicing (NS) is a key technology to cost-effectively meet diverse service level agreement (SLA) demands of the Internet of Everything communication. Thanks to high-fidelity network modeling capabilities and flexible feedback optimization techniques, digital twins (DTs) and reinforcement learning (RL) have been applied to dynamic NS management. However, most existing DTs lack the ability of predictive uncertainty evaluations, and tend to be overconfident on the unknown network environment. For classical RL, it is exceedingly intractable to maintain high-stable NS performances in dynamic networks. To address those problems, we propose a DT-based low-risk NS (DT-LNS) framework and method using the safe RL. In the safe RL, a DT using deep neural networks with the data-model uncertainty analysis is adopted to predict NS performances and provide predictive uncertainties. Further, the RL is used to select low-risk NS configuration actions by preverifying the SLA violation risk of candidate actions from the RL and the reference action subspace via DTs. The proposed DT-LNS method can keep the high-SLA satisfaction rate (SSR), reduce the performance jitters, and improve the convergence speed. Compared with the six classic NS configuration methods, including round robin, deep Q network, advantage actor-critic, deep deterministic policy gradient, and advanced RL, assisted with the DT-based model pretraining and the state prediction, the average percentage gain of the proposed method is 7.84%, 93.58%, 65.63%, 84.20%, and 90.27%, regarding the performances of the average SSR, SSR jitter, delay jitter, data rate jitter, and the convergence speed, respectively.

Network slicing (NS) can enable diverse communication services for vertical industries. In radio access network slicing, differential service level agreement (SLA) guaranteeing is an essential resource management task. Benefit from powerful data analysis capabilities, deep learning (DL) is suitable for intelligent resource management under the cases of complex constraints and time-varying states. Thus, DL has been used to manage resources for NS recently. However, the training of these DL-assisted methods is time-consuming and it is difficult to keep high SLA satisfaction rates dynamically. To address this problem, we propose a hierarchical intelligent NS resource configuration method via organically integrating NS preconfiguration models based on deep neural networks and NS reconfiguration models using multiarm bandits. A factory automation system is established to evaluate our proposed methods on different industrial services. Simulation and experimental results demonstrate that our proposed methods outperform benchmarks comprehensively.