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.

In optical transport networks (OTNs), proactive and accurate prediction of key performance parameters plays a crucial role in identifying potential failure of OTN equipment and guiding timely operational interventions, reducing downtime and improving overall system performance. However, the performance parameters in OTNs are complex and diverse. The reliance of existing models structure design on specific configurations limits generalizability across diverse equipment types. Moreover, the high computational resource consumption and memory footprints of these models may lead to inefficiency while hindering practical application and large-scale deployment. To address these challenges, this paper presents a general model, KD-TimeLLM, a cross-application of TimeLLM into OTN failure management, for performance parameter prediction of multiple equipment types in OTNs. By learning from its teacher model TimeLLM via a knowledge distillation strategy, KD-TimeLLM can achieve generalizability in performance parameter prediction while enhancing efficiency. We conducted evaluations across multiple metrics using data sets from different operators and various board types. Results show that KD-TimeLLM outperforms other models in predictive effects including the lowest MSE and MAE across all types of board data along with a scaled_RMSE value below 0.5, the varying number of performance parameters, and zero-shot prediction capability, highlighting its generalizability. Moreover, compared to its teacher model, KD-TimeLLM achieves comparable predictive effects with a significant reduction 99.99% in model parameters and an average reduction of 99.23% in inference time across eight different types of board data. Furthermore, compared to a multiple-model system, total inference time and memory footprint of KD-TimeLLM decreased by 94.79% and 89.65%, highlighting its effectiveness and efficiency.

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.

In modern optical networks, alarm analysis plays a pivotal role in detecting fault and guiding operators towards timely interventions. Timely and accurate root cause localization can effectively reduce downtime, prevent cascading failures, and enhance overall system performance. However, the increasing scale and complexity of networks have posed challenges to Root Cause Localization (RCL) due to the huge volume of alarms generated during fault occurrences. The development of automatic RCL methods helps reduce the time costs and human errors associated with manual analysis. Most existing RCL methods face challenges such as poor explainability, low adaptability to network changes, high learning costs and lack of interactivity, reducing their credibility and usability in production environments. This paper introduces a graph structure-enhanced large language model (LLM) for optical network fault detection, capable of performing explainable alarm RCL with improved adaptability and interactivity. Graph structures provide an intuitive means of expressing the relationships between topology and alarms, clearly visualizing alarm propagation paths, which aids in explainable RCL; LLM exhibits profound capabilities in semantic comprehension and language generation, which improves both explainability and interactivity. When integrated with algorithms, they hold promise for reducing operators learning costs, adding interactivity, and introducing new possibilities for the visualization and efficiency of complex tasks. We conducted evaluations and validations across multiple metrics using real alarm data collected from optical transport network (OTN). The results show that the proposed algorithm achieves an accuracy of over 86.8% across various complex scenarios; compared to the base model, the fine-tuned model exhibits an accuracy improvement of over 87%. Other results indicate that the proposed method enhances the explainability, adaptability and interactivity of fault detection in optical network, showcasing significant potential for automated and intelligent network operations.

Digital twin (DT) techniques have been proposed for the autonomous operation and lifecycle management of next-generation optical networks. To fully utilize potential capacity and accommodate dynamic services, the DT must dynamically update in sync with deployed optical networks throughout their lifecycle, ensuring low-margin operation. This paper proposes a dynamic-updating DT for the lifecycle management of optical networks, employing a hybrid approach that integrates data-driven and physics-informed techniques for fiber channel modeling. This integration ensures both rapid calculation speed and high physics consistency in optical performance prediction while enabling the dynamic updating of critical physical parameters for DT. The lifecycle management of optical networks, covering accurate performance prediction at the network deployment and dynamic updating during network operation, is demonstrated through simulation in a large-scale network. Up to 100 times speedup in prediction is observed compared to classical numerical methods. In addition, the fiber Raman gain strength, amplifier frequency-dependent gain profile, and connector loss between fiber and amplifier on C and L bands can be simultaneously updated. Moreover, the dynamic-updating DT is verified on a field-trial C+L-band transmission link, achieving a maximum accuracy improvement of 1.4 dB for performance estimation post-device replacement. Overall, the dynamic-updating DT holds promise for driving the next-generation optical networks towards lifecycle autonomous management.

To further improve the transmission efficiency and link stability for free space optical (FSO)-based satellite communication (SatCom) systems when transmitting large-scale remote sensing images, a scheme based on the integration of FSO and semantic communication (FSO-SC) is proposed, which employs a vector quantized variational autoencoder with spatial normalization to extract essential semantic features of images while preserving intricate details. Additionally, theMáalagadistribution model is utilized to simulate FSO channels with diverse turbulence conditions. Moreover, a comparative evaluation between the FSO-SC and traditional systems is conducted through 28 GBaud satellite-ground simulation with three modulation formats considering various effects. Compared to the traditional systems, without incurring additional bits for error corrections, the FSO-SC system achieves a power gain of over 3 dB while enabling transmission at zenith angles over 60°. Moreover, it achieves performance on par with state-of-the-art 4-receiver spatial diversity technology, while offering superior hardware and transmission efficiency. Furthermore, we conduct 10 Gbps real-time satellite-ground equivalent experiments to validate the practicality of the FSO-SC, where it achieves a 60% reduction in communication overhead compared to existing solutions while maintaining comparable received image quality and can reach a minimum receiver sensitivity gain of 4 dB. Simulation and experimental results demonstrate that the proposed FSO-SC scheme achieves high system efficiency and stability, holding promise as a viable solution for future SatCom.

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.

Visible light communication (VLC), which uses an irregular visible band to offer ultra-high bandwidth, ultra-high data rates, lower latency, low energy consumption and reduced implementation costs, is one of the most promising complementary wireless technologies for Sixth Generation Mobile Communications(6G). With the deployment of massive access points for bidirectional VLC network, the problem of user service quality is also brought correspondingly, such as how to increase user Quality of Experience (QoE) and reduce user access delay. In this paper, a space-wavelength-time division multiple access (SWT-MA) scheme is proposed based on Tri-color Light Emitting Diode(LEDs). The principle of SWT-MA scheme is descripted. The metrics of the VLC based on the SWT-MA scheme is analyzed in throughput, access delay in detail respectively. And finally, the experimental measurements of BER for each color signals are illustrated. The access scheme proposed in this paper solves the above problems by improving the data transmission rate through space division multiplexing and wavelength division multiplexing, and increasing the system capacity through time division multiplexing. The feasibility of this scheme is verified by theoretical analysis, simulation and experiment. We showed that the proposed access scheme is suitable for large-scale bidirectional VLC access networks and can be used for 6G.

In future 6G, bounded delay, ultra-high-reliability, and dedicated services will require high-performance network modeling techniques for the accuracy pre-validation such as network delay prediction. Recently, graph neural networks (GNNs) have been shown great potential for network delay prediction. GNNs are able to effectively capture complex topologies and node features in graph data by recursively aggregating the neighborhood information of nodes. To improve the ability to learn representations of graph data, GNNs are suitable for a variety of complex network modeling tasks with high flexibility and powerful scalability. However, the current GNN-based Routenet model can not capture the effect of the path on neighboring links, which is ineffective in complex topologies. To model the effects of the network path on neighboring links, this paper proposes a transformer-based GNN model named Transnet. In this model, the Transformer is first introduced to update the path and link states and describe the effects of the path on neighboring links based on the attention mechanism in the Transformer. Simulation results show that the delay prediction accuracy of the proposed Transnet obviously exceeds those of Routenet on multi-node topology in the Nsfnet and Synth50 datasets.

With the increasing demand for time-critical transmission in vertical industries, the deterministic network has been proposed to provide transmission services with deterministic latency and jitter. Currently, deterministic network research primarily focuses on the transport networks and physical layer of wired networks. However, there is still a lack of research on end-to-end (E2E) deterministic services covering wireless access, wired transmission, and network computing, especially in the dynamic networks. To address this challenge, we propose an efficient E2E service function chain (SFC) resource dynamic orchestration (ESRDO) framework integrating network slicing, edge computing, and deterministic network technologies. To ensure deterministic E2E latency and low latency violation rates, under the case of time-varied number of end users and dynamic channel conditions. Extensive simulation results show that our proposed ESRDO obviously outperforms traditional dynamic resource optimization methods based on the mixed integer nonlinear programming (MINLP).

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.

Telecom big data generated by telecom networks have a high economic value. Thus, telecom operators actively explore telecom big data transactions methods to minimize the possibility of leaking users’ privacy. The existing solutions do not allow the data sets to leave the database, instead only allow the buyers to send data mining algorithms to the telecom operator’s platform for training. However, this centralized platform has a high risk of tampering. In addition, the currently existing solutions cannot be used to accurately and quickly trace the information of telecom big data transactions. To address these limitations, we propose a blockchain-based reliable traceability system for telecom big data transactions using smart contracts and the InterPlanetary File System. Two types of smart contracts are developed to store transaction information for tracing. Access control strategies and a reapproval prevention strategy are designed for ensuring the safe operation of the system and avoiding the problem of favoritism and fraud. We use Ethereum as a verification platform to develop and evaluate this system. The implementation of functions, such as purchasing data sets, sending algorithms, obtaining results, and tracing transactions in the smart contract and the implementation of the proposed strategies are verified. The results demonstrate that the performance of the proposed system is better than the existing solutions, and the traceability response time is improved to the order of seconds, so as to realize the safe and efficient traceability of telecom big data transactions. In addition, Ethereum and Hyperledger Fabric v0.6 were discussed to provide insights for future development.

In recent years, with the rapid development of maritime activities, the demand for high-speed, reliable, low-latency, and full-coverage marine communication network (MCN) has become increasingly urgent. At present, maritime communication services are mainly provided by satellite networks, but suffers from many limitations, such as surge of data volume, complex communication environment, uneven distribution of traffic and user density and different requirements for maritime services. In order to solve these problems, mobile edge computing (MEC), space-air-ground-sea integrated network (SAGSIN), and blockchain are considered to be promising technologies for MCN enhancement. In this paper, the challenges faced by MCN are discussed, and the above three technologies are applied to solve these challenges. Finally, a space-air-ground integrated network (SAGIN) assisted MCN architecture based on edge computing is proposed, and the future research directions are also put forward.