Digital Communications and Networks最新文献_第3页

Enabling intent-driven CoX mechanism in space-terrestrial network for multiple mission impossible 实现空间-地面网络中多种不可能任务的意图驱动的CoX机制

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.06.009

Ying Ouyang, Chungang Yang, Rongqian Fan, Tangyi Li

The Space-Terrestrial Network (STN) aims to deliver comprehensive on-demand network services, addressing the broad and varied needs of Internet of Things (IoT) applications. However, the STN faces new challenges such as service multiplicity, topology dynamicity, and conventional management complexity. This necessitates a flexible and autonomous approach to network resource management to effectively align network services with available resources. Thus, we incorporate the Intent-Driven Network (IDN) into the STN, enabling the execution of multiple missions through automated resource allocation and dynamic network policy optimization. This approach enhances programmability and flexibility, facilitating intelligent network management for real-time control and adaptable service deployment in both traditional and IoT-focused scenarios. Building on previous mechanisms, we develop the intent-driven CoX resource management model, which includes components for coordination intent decomposition, collaboration intent management, and cooperation resource management. We propose an advanced intent verification mechanism and create an intent-driven CoX resource management algorithm leveraging a two-stage deep reinforcement learning method to minimize resource usage and delay costs in cross-domain communications within the STN. Ultimately, we establish an intent-driven CoX prototype to validate the efficacy of this proposed mechanism, which demonstrates improved performance in intent refinement and resource management efficiency.

空间-地面网络（STN）旨在提供全面的按需网络服务，满足物联网（IoT）应用的广泛和多样化需求。但是，STN面临着业务多样性、拓扑动态性和传统管理复杂性等新的挑战。这就需要一种灵活和自主的网络资源管理方法，以便有效地将网络服务与可用资源结合起来。因此，我们将意图驱动网络（IDN）整合到STN中，通过自动资源分配和动态网络策略优化来实现多个任务的执行。这种方法增强了可编程性和灵活性，有助于在传统和物联网场景下实现实时控制和适应性业务部署的智能网络管理。在先前机制的基础上，我们开发了意图驱动的CoX资源管理模型，该模型包括用于协调意图分解、协作意图管理和合作资源管理的组件。我们提出了一种先进的意图验证机制，并利用两阶段深度强化学习方法创建了一个意图驱动的CoX资源管理算法，以最大限度地减少STN内跨域通信中的资源使用和延迟成本。最后，我们建立了一个意图驱动的CoX原型来验证该机制的有效性，该机制在意图细化和资源管理效率方面证明了改进的性能。

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引用次数: 0

Autonomous network management for 6G communication: A comprehensive survey 6G通信的自主网络管理：综合调查

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.07.001

Inam Ullah , Ali Arishi , Sushil Kumar Singh , Faisal Alharbi , Anwar Hassan Ibrahim , Muhammad Islam , Yousef Ibrahim Daradkeh , Chang Choi

The rapid advancement of 6G communication networks presents both considerable problems and opportunities in network management, necessitating sophisticated solutions that extend beyond conventional methods. This study seeks to investigate and evaluate autonomous network management solutions designed for 6G communication networks, highlighting their technical advantages and potential implications. We examine the role of Artificial Intelligence (AI), Machine Learning (ML), and network automation in facilitating self-organization, optimization, and decision-making within critical network domains, including spectrum management, traffic load balancing, fault detection, and security and privacy. We examine the integration of edge computing and Distributed Ledger Technologies (DLT), specifically blockchain, to improve trust, transparency, and security in autonomous networks. This study provides a comprehensive understanding of the technological developments driving fully autonomous, efficient, and resilient 6G network infrastructures by methodically analyzing existing methodologies, identifying significant research gaps, and exploring potential prospects. The results offer significant insights for researchers, engineers, and industry experts involved in the development and deployment of advanced autonomous network management systems.

6G通信网络的快速发展给网络管理带来了相当大的问题和机遇，需要超越传统方法的复杂解决方案。本研究旨在调查和评估为6G通信网络设计的自主网络管理解决方案，突出其技术优势和潜在影响。我们研究了人工智能（AI）、机器学习（ML）和网络自动化在促进关键网络域中的自组织、优化和决策方面的作用，包括频谱管理、流量负载平衡、故障检测、安全和隐私。我们研究了边缘计算和分布式账本技术（DLT）的集成，特别是区块链，以提高自治网络中的信任、透明度和安全性。本研究通过系统分析现有方法、确定重大研究差距和探索潜在前景，全面了解推动完全自主、高效和弹性的6G网络基础设施的技术发展。研究结果为参与先进自主网络管理系统开发和部署的研究人员、工程师和行业专家提供了重要的见解。

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引用次数: 0

F-norm based low-power motion recognition on wearable devices in the presence of outlier motions 基于f范数的可穿戴设备中存在异常运动的低功耗运动识别

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2024.08.012

Yin Long, Hongbin Xu, Yang Xiang

Motion recognition refers to the intelligent recognition of human motion using data collected from wearable sensors, which exceedingly has gained significant interest from both academic and industrial fields. However, temporary-sudden activities caused by accidental behavior pose a major challenge to motion recognition and have been largely overlooked in existing works. To address this problem, the multi-dimensional time series of motion data is modeled as a Time-Frequency (TF) tensor, and the original challenge is transformed into a problem of outlier-corrupted tensor pattern recognition, where transient sudden activity data are considered as outliers. Since the TF tensor can capture the latent spatio-temporal correlations of the motion data, the tensor MPCA is used to derive the principal spatio-temporal pattern of the motion data. However, traditional MPCA uses the squared F-norm as the projection distance measure, which makes it sensitive to the presence of outlier motion data. Therefore, in the proposed outlier-robust MPCA scheme, the F-norm with the desirable geometric properties is used as the distance measure to simultaneously mitigate the interference of outlier motion data while preserving rotational invariance. Moreover, to reduce the complexity of outlier-robust motion recognition, we impose the proposed outlier-robust MPCA scheme on the traditional MPCANet which is a low-complexity deep learning network. The experimental results show that our proposed outlier-robust MPCANet can simultaneously improve motion recognition performance and reduce the complexity, especially in practical scenarios where the real-time data is corrupted by temporary-sudden activities.

运动识别是指利用可穿戴传感器采集的数据对人体运动进行智能识别，目前已经引起了学术界和工业界的极大兴趣。然而，由意外行为引起的临时突然活动对运动识别构成了重大挑战，在现有工作中很大程度上被忽视。为了解决这一问题，将运动数据的多维时间序列建模为一个时间频率张量（TF），并将原始挑战转化为一个异常值损坏张量模式识别问题，其中瞬态突然活动数据被视为异常值。由于TF张量可以捕获运动数据的潜在时空相关性，因此使用MPCA张量来推导运动数据的主时空模式。然而，传统的MPCA使用f范数的平方作为投影距离度量，这使得它对异常运动数据的存在很敏感。因此，在提出的离群鲁棒MPCA方案中，使用具有理想几何特性的f范数作为距离度量，在保持旋转不变性的同时减轻离群运动数据的干扰。此外，为了降低异常鲁棒运动识别的复杂性，我们将所提出的异常鲁棒MPCA方案应用于传统的低复杂度深度学习网络MPCANet。实验结果表明，本文提出的离群鲁棒MPCANet在提高运动识别性能的同时降低了运动识别的复杂性，特别是在实时数据被临时突发活动破坏的实际场景中。

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引用次数: 0

Deep reinforcement learning-based forwarding node selection algorithm in Internet of vehicles 基于深度强化学习的车联网转发节点选择算法

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.08.012

Huibin Xu, Long Fang

Due to open communication environment, Internet of Vehicles (IoV) are vulnerable to many attacks, including the gray hole attack, which can disrupt the process of transmitting messages. And this results in the degradation of routing performance. To address this issue, a double deep Q-networks-based stable routing for resisting gray hole attack (DOSR) is proposed in this paper. The aim of the DOSR algorithm is to maximize the message delivery ratio as well as to minimize the transmission delay. For this, the distance ratio, message loss ratio, and connection ratio are taken into consideration when choosing a relay node. Then, to choose the relay node is formulated as an optimization problem, and a double deep Q-networks are utilized to solve the optimization problem. Experimental results show that DOSR outperforms QLTR and TLRP by significant margins: in scenarios with 400 vehicles and 10% malicious nodes, the message delivery ratio (MDR) of DOSR is 8.3% higher than that of QLTR and 5.1% higher than that of TLRP; the average transmission delay (ATD) is reduced by 23.3% compared to QLTR and 17.9% compared to TLRP. Additionally, sensitivity analysis of hyperparameters confirms the convergence and stability of DOSR, demonstrating its robustness in dynamic IoV environments.

由于开放的通信环境，车联网容易受到包括灰洞攻击在内的多种攻击，这些攻击会破坏信息的传输过程。这将导致路由性能的下降。为了解决这一问题，本文提出了一种基于双深度q网络的抗灰洞攻击稳定路由。DOSR算法的目标是最大化消息传递率和最小化传输延迟。为此，在选择中继节点时要考虑距离比、消息损失率和连接比。然后，将中继节点的选择表述为优化问题，利用双深度q网络求解优化问题。实验结果表明，DOSR比QLTR和TLRP有明显的优势：在400辆车和10%恶意节点的场景下，DOSR的消息传递率（MDR）比QLTR高8.3%，比TLRP高5.1%；平均传输延迟（ATD）比QLTR降低23.3%，比TLRP降低17.9%。此外，超参数的敏感性分析证实了DOSR的收敛性和稳定性，证明了其在动态车联网环境中的鲁棒性。

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引用次数: 0

Resource allocation for AI-native healthcare systems in 6G dense networks using deep reinforcement learning 使用深度强化学习的6G密集网络中人工智能原生医疗系统的资源分配

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.06.011

Jianhui Lv , Chien-Ming Chen , Saru Kumari , Keqin Li

Although 6G networks combined with artificial intelligence present revolutionary prospects for healthcare delivery, resource management in dense medical device networks stays a basic issue. Reliable communication directly affects patient outcomes in these settings; nonetheless, current resource allocation techniques struggle with complicated interference patterns and different service needs of AI-native healthcare systems. In dense installations where conventional approaches fail, this paper tackles the challenge of combining network efficiency with medical care priority. Thus, we offer a Dueling Deep Q-Network (DDQN) -based resource allocation approach for AI-native healthcare systems in 6G dense networks. First, we create a point-line graph coloring-based interference model to capture the unique characteristics of medical device communications. Building on this foundation, we suggest a DDQN approach to optimal resource allocation over multiple medical services by combining advantage estimate with healthcare-aware state evaluation. Unlike traditional graph-based models, this one correctly depicts the overlapping coverage areas common in hospital environments. Building on this basis, our DDQN design allows the system to prioritize medical needs while distributing resources by separating healthcare state assessment from advantage estimation. Experimental findings show that the suggested DDQN outperforms state-of-the-art techniques in dense healthcare installations by 14.6% greater network throughput and 13.7% better resource use. The solution shows particularly strong in maintaining service quality under vital conditions with 5.5% greater QoS satisfaction for emergency services and 8.2% quicker recovery from interruptions.

尽管6G网络与人工智能相结合为医疗保健提供了革命性的前景，但密集医疗设备网络中的资源管理仍然是一个基本问题。在这些情况下，可靠的沟通直接影响患者的预后；然而，当前的资源分配技术与人工智能原生医疗系统复杂的干扰模式和不同的服务需求作斗争。在密集的设施，传统的方法失败，本文解决了结合网络效率与医疗保健优先级的挑战。因此，我们为6G密集网络中的人工智能原生医疗系统提供了一种基于Dueling Deep Q-Network （DDQN）的资源分配方法。首先，我们创建了一个基于点线图着色的干扰模型来捕捉医疗设备通信的独特特征。在此基础上，我们提出了一种DDQN方法，通过将优势估计与医疗保健感知状态评估相结合，来优化多个医疗服务的资源分配。与传统的基于图的模型不同，这个模型正确地描述了医院环境中常见的重叠覆盖区域。在此基础上，我们的DDQN设计允许系统优先考虑医疗需求，同时通过将医疗状况评估与优势评估分开来分配资源。实验结果表明，在密集的医疗设施中，建议的DDQN比最先进的技术性能高出14.6%的网络吞吐量和13.7%的资源利用率。该解决方案在关键条件下保持服务质量方面表现得尤为出色，紧急服务的QoS满意度提高5.5%，中断后的恢复速度提高8.2%。

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引用次数: 0

Artificial intelligence enhanced edge server placement for workload balancing and energy efficiency in B5G networks 人工智能增强了B5G网络中工作负载平衡和能源效率的边缘服务器位置

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.08.009

Vaibhav Tiwari , Chandrasen Pandey , Shamila J. Francis , Ishan Budhiraja , Pronaya Bhattacharya , Zhu Zhu , Thippa Reddy Gadekallu

The Internet of Things (IoT) and allied applications have made real-time responsiveness for massive devices over the Internet essential. Cloud-edge/fog ensembles handle such applications' computations. For Beyond 5th Generation (B5G) communication paradigms, Edge Servers (ESs) must be placed within Information Communication Technology infrastructures to meet Quality of Service requirements like response time and resource utilisation. Due to the large number of Base Stations (BSs) and ESs and the possibility of significant variations in placing the ESs within the IoTs geographical expanse for optimising multiple objectives, the Edge Server Placement Problem (ESPP) is NP-hard. Thus, stochastic evolutionary metaheuristics are natural. This work addresses the ESPP using a Particle Swarm Optimization that initialises particles as BS positions within the geography to maintain the workload while scanning through all feasible sets of BSs as an encoded sequence. The Workload-Threshold Aware Sequence Encoding (WTASE) Scheme for ESPP provides the number of ESs to be deployed, similar to existing methodologies and exact locations for their placements without the overhead of maintaining a prohibitively large distance matrix. Simulation tests using open-source datasets show that the suggested technique improves ESs utilisation rate, workload balance, and average energy consumption by 36%, 17%, and 32%, respectively, compared to prior works.

物联网（IoT）和相关应用程序使得互联网上大量设备的实时响应变得至关重要。云边缘/雾集成处理这些应用程序的计算。对于超第五代（B5G）通信范例，必须将边缘服务器（ESs）放置在信息通信技术基础设施中，以满足响应时间和资源利用率等服务质量要求。由于大量基站（BSs）和ESs以及在物联网地理范围内放置ESs以优化多个目标的可能性存在显著差异，因此边缘服务器放置问题（ESPP）是np困难的。因此，随机进化元启发式是很自然的。这项工作使用粒子群优化来解决ESPP问题，该优化将粒子初始化为地理区域内的BS位置，以保持工作量，同时扫描所有可行的BS集作为编码序列。ESPP的工作负载阈值感知序列编码（WTASE）方案提供了要部署的ESs的数量，类似于现有的方法和它们放置的确切位置，而不需要维护一个过大的距离矩阵。使用开源数据集的模拟测试表明，与之前的工作相比，所建议的技术将ESs利用率、工作负载平衡和平均能耗分别提高了36%、17%和32%。

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引用次数: 0

Secure monitoring of Internet of vehicles in 6G networks through intelligent reflecting surfaces leveraging AI 通过利用人工智能的智能反射面，在6G网络中安全监控车联网

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.07.012

Sharanya Selvaraj , Balasubramanian Prabhu Kavin , Priyan Malarvizhi Kumar , Mohammed J.F. Alenazi , Zaid Bin Faheem , Jehad Ali

The ensemble of Information and Communication Technology (ICT) and Artificial Intelligence (AI) has catalysed many developments and innovations in the automotive industry. 6G networks emerge as a promising technology for realising Intelligent Transport Systems (ITS), which benefits the drivers and society. As the network is highly heterogeneous and robust, the physical layer security and node reliability of the vehicles hold paramount significance. This work presents a novel methodology that integrates the prowess of computer vision techniques and the Lightweight Super Learning Ensemble (LSLE) of Machine Learning (ML) algorithms to predict the presence of intruders in the network. Furthermore, our work utilizes a Deep Convolutional Neural Network (DCNN) to detect obstacles by identifying the Region of Interest (ROI) in the images. As the network utilizes mm-waves with shorter wavelengths, Intelligent Reflecting Surfaces (IRS) are employed to redirect signals to legitimate nodes, thereby mitigating the malicious activity of intruders. The experimental simulation shows that the proposed LSLE outperforms the state-of-the-art techniques in terms of accuracy, False Positive Rate (FPR), Recall, F1-Score, and Precision. A consistent performance improvement with an average FPR of 85.08% and accuracy of 92.01% is achieved by the model. Thus, in the future, detecting moving obstacles and real-time network traffic monitoring can be included to achieve more realistic results.

信息通信技术（ICT）和人工智能（AI）的融合促进了汽车行业的许多发展和创新。6G网络是实现智能交通系统（ITS）的一项有前途的技术，它使驾驶员和社会受益。由于网络具有高度异构性和鲁棒性，因此车辆的物理层安全性和节点可靠性至关重要。这项工作提出了一种新的方法，该方法集成了计算机视觉技术的实力和机器学习（ML）算法的轻量级超级学习集成（LSLE）来预测网络中入侵者的存在。此外，我们的工作利用深度卷积神经网络（DCNN）通过识别图像中的感兴趣区域（ROI）来检测障碍物。由于网络使用波长较短的毫米波，因此采用智能反射面（IRS）将信号重定向到合法节点，从而减轻入侵者的恶意活动。实验仿真表明，该方法在准确率、误报率（FPR）、查全率（Recall）、F1-Score和精密度（Precision）等方面都优于当前最先进的方法。结果表明，该模型的平均FPR为85.08%，准确率为92.01%。因此，在未来，可以加入移动障碍物检测和实时网络流量监控，以达到更真实的效果。

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引用次数: 0

Video distribution strategy based on software defined network at the wireless edge 基于软件定义网络的无线边缘视频分发策略

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.06.004

Tao Zhao , Yunjian Jia , Jihua Zhou , Xiangyu Liu , Ziwen Guo

Video distribution strategies in wireless edge networks can significantly reduce video transmission latency and system energy consumption, meeting emerging video services' high-rate, low-latency requirements. However, channel condition variability and dynamics caused by user-to-base-station distance and user mobility affect the Quality of Experience (QoE). To address this problem, this paper examines adaptive video streaming strategies under dynamic channel conditions to optimize user QoE. Specifically, to achieve centralized control of wireless edge networks and simplify the management and scheduling of communication resources, Software-Defined Networking (SDN) is adopted within the wireless edge network, and an SDN-based edge caching architecture is proposed. Based on the virtual queue of users receiving video and combining various video factors to quantify the user QoE metric, an optimization problem is established to maximize the time-averaged total user QoE. Subsequently, an adaptive video distribution algorithm is designed, and the optimal video quality selection strategy and power allocation strategy are obtained in conjunction with Lyapunov optimization theory. Therefore, simulation results indicate that our approach significantly reduces video playback interruptions and enhances user QoE.

无线边缘网络中的视频分发策略可以显著降低视频传输延迟和系统能耗，满足新兴视频业务对高速率、低延迟的需求。然而，由于用户到基站的距离和用户移动性引起的信道条件变化和动态影响了体验质量（QoE）。为了解决这一问题，本文研究了动态信道条件下的自适应视频流策略，以优化用户QoE。具体而言，为了实现对无线边缘网络的集中控制，简化通信资源的管理和调度，在无线边缘网络中采用了软件定义网络（SDN），并提出了一种基于SDN的边缘缓存架构。基于用户接收视频的虚拟队列，结合各种视频因素量化用户QoE指标，建立了一个优化问题，以最大化时间平均总用户QoE。随后，设计了一种自适应视频分配算法，结合Lyapunov优化理论，得到了最优视频质量选择策略和功率分配策略。因此，仿真结果表明，我们的方法显著减少了视频播放中断，提高了用户QoE。

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引用次数: 0

Shard-DAG: A scalable and secure block-DAG sharding scheme for AI-driven 6G networks Shard-DAG：针对ai驱动的6G网络的可扩展且安全的块dag分片方案

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.04.005

Yongkai Fan , Wenyuan Zhang , Guodong Wu , Le Zhang , Chengnian Long , Gang Tan , Neal N. Xiong

The ultra-high speed, ultra-low latency, and massive connectivity of the 6th Generation Mobile Network (6G) present unprecedented challenges to network security. In addition, the deep integration of Artificial Intelligence (AI) into 6G networks introduces AI-native features that further complicate the design and implementation of secure network architectures. To meet the security demands posed by the massive number of devices and edge nodes in 6G networks, a decentralized security architecture is essential, as it effectively mitigates the performance bottlenecks typically associated with centralized systems. Blockchain technology offers a promising trust mechanism among devices in 6G networks. However, conventional blockchain systems suffer from limited scalability under high-load conditions, making them inadequate for supporting a large volume of nodes and frequent data exchanges. To overcome these limitations, We propose Shard-DAG, a scalable architecture that structurally integrates Directed Acyclic Graphs (DAG) and sharding. Each shard adopts a Block-DAG structure for parallel block processing, effectively overcoming the performance bottlenecks of traditional chain-based blockchains. Furthermore, we introduce a DAG-based transaction ordering mechanism within each shard to defend against double-spending attacks. To ensure inter-shard security, Block-DAG adopts a black-box interaction approach to prevent cross-shard double-spending. Theoretical analysis and experimental evaluations demonstrate that Shard-DAG achieves near-linear scalability. In a network of

1, 200

nodes with 8 shards, Shard-DAG achieves peak throughput improvements of 14.64 times over traditional blockchains, 8.61 times over standalone Block-DAG, and 2.05 times over conventional sharded blockchains. The results validate Shard-DAG's ability to scale efficiently while maintaining robust security properties.

第六代移动网络（6G）的超高速、超低延迟和海量连接对网络安全提出了前所未有的挑战。此外，人工智能（AI）与6G网络的深度集成引入了AI原生功能，使安全网络架构的设计和实现进一步复杂化。为了满足6G网络中大量设备和边缘节点所带来的安全需求，分散的安全架构至关重要，因为它有效地缓解了通常与集中式系统相关的性能瓶颈。区块链技术在6G网络中提供了一种很有前景的设备间信任机制。然而，传统区块链系统在高负载条件下的可伸缩性有限，这使得它们无法支持大量节点和频繁的数据交换。为了克服这些限制，我们提出了分片-DAG，这是一种可扩展的架构，从结构上集成了有向无环图（DAG）和分片。每个分片采用block - dag结构进行并行块处理，有效克服了传统基于链的区块链的性能瓶颈。此外，我们在每个分片中引入了基于dag的交易排序机制，以防御双重支出攻击。为了保证分片间的安全性，Block-DAG采用黑盒交互方式，防止跨分片双花。理论分析和实验评估表明，分片- dag实现了近似线性的可扩展性。在拥有8个分片的1200个节点的网络中，分片- dag的峰值吞吐量比传统区块链提高了14.64倍，比独立的Block-DAG提高了8.61倍，比传统分片区块链提高了2.05倍。结果验证了分片- dag在保持强大安全属性的同时有效扩展的能力。

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引用次数: 0

Characterization and optimization of satellite complex networks based on hyperbolic space 基于双曲空间的卫星复杂网络表征与优化

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.09.001

Yuanzhi He , Huajun Fu , Di Yan , Shanshan Feng , Hongbo Chen , Xuebin Zhuang

In recent years, the rapid advancement of mega-constellations in Low Earth Orbit (LEO) has led to the emergence of satellite communication networks characterized by a complex interplay between high- and low-altitude orbits and by unprecedented scale. Traditional network-representation methodologies in Euclidean space are insufficient to capture the dynamics and evolution of high-dimensional complex networks. By contrast, hyperbolic space offers greater scalability and stronger representational capacity than Euclidean-space methods, thereby providing a more suitable framework for representing large-scale satellite communication networks. This paper aims to address the burgeoning demands of large-scale space–air–ground integrated satellite communication networks by providing a comprehensive review of representation-learning methods for large-scale complex networks and their application within hyperbolic space. First, we briefly introduce several equivalent models of hyperbolic space. Then, we summarize existing representation methods and applications for large-scale complex networks. Building on these advances, we propose representation methods for complex satellite communication networks in hyperbolic space and discuss potential application prospects. Finally, we highlight several pressing directions for future research.

近年来，随着近地轨道巨型星座的快速发展，以高低空轨道相互作用复杂、规模空前为特征的卫星通信网络应运而生。传统的欧几里得空间网络表示方法不足以捕捉高维复杂网络的动态和演化。与欧几里得空间方法相比，双曲空间具有更大的可扩展性和更强的表示能力，从而为大规模卫星通信网络的表示提供了更合适的框架。本文旨在通过对大规模复杂网络的表示学习方法及其在双曲空间中的应用进行全面综述，解决大规模空间-空地-地面综合卫星通信网络日益增长的需求。首先，我们简单地介绍了双曲空间的几个等效模型。然后，总结了现有的大型复杂网络表示方法及其应用。在此基础上，我们提出了双曲空间复杂卫星通信网络的表示方法，并讨论了潜在的应用前景。最后，提出了未来研究的几个紧迫方向。

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引用次数: 0