Digital Communications and Networks最新文献

{"title":"Wireless-optical switching and communication based on metasurfaces: From multi-parameter regulation to potential applications","authors":"Weijie Qiu ,&nbsp;Weigang Hou ,&nbsp;Jiahao Zhou ,&nbsp;Xiaoxue Gong ,&nbsp;Xiangyu He ,&nbsp;Lei Guo ,&nbsp;Pengxing Guo","doi":"10.1016/j.dcan.2025.06.002","DOIUrl":"10.1016/j.dcan.2025.06.002","url":null,"abstract":"<div><div>This review provides a comprehensive survey of the most recent developments in metasurfaces for applications in domains including wireless-optical switching and communications. In particular, we focus on discussion of multi-parameter optical field regulation and potential applications in system performance enhancement. By designing nanostructured arrays with specific geometries, metasurfaces can be used to effectively manipulate parameters including phase, amplitude, and polarization, thereby enabling the switching, transmission, testing, analysis, and processing of optical signals. Notably, the introduction of phase-change materials offers a novel approach that allows metasurfaces to achieve more flexible wireless-optical switching at higher speeds. In wireless-optical communication systems, multiplexing of the different degrees of freedom of the light beams can improve the data transmission capacity and rate significantly. Finally, we present our own metasurface design with its unique passive parallel beam splitting capacity, and we demonstrate the superiority of this design in applications including wireless-optical inter-rack connections in data centers and industrial inspection based on optical cross-connectors.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 649-661"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147852563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved PHY-layer authentication utilizing multi-modal features for mmWave MIMO UAV-enabled systems","authors":"Mu Niu ,&nbsp;Keshuang Han ,&nbsp;Xudong Zhong ,&nbsp;Baoquan Ren ,&nbsp;Pinchang Zhang ,&nbsp;Ji He","doi":"10.1016/j.dcan.2024.10.001","DOIUrl":"10.1016/j.dcan.2024.10.001","url":null,"abstract":"<div><div>This paper exploits multi-modal Physical (PHY)-layer features in terms of artificial fingerprint, In-phase/Quadrature (IQ) imbalance and Angle of Arrival (AoA) to propose a novel PHY-layer authentication framework for a Millimeter Wave (mmWave) Multiple-Input Multiple-Output (MIMO) Unmanned Aerial Vehicle (UAV)-enabled communication system. First, we resort to the AoA-based spatial fingerprint to effectively address the challenge of channel fingerprint instability induced by high-speed UAV mobility. To further enhance the low discriminability of hardware fingerprints caused by refined manufacturing techniques, artificial Gaussian noise is injected into the transmission signals to assist the receiver in better distinguishing between legitimate and illegitimate UAVs. Then, we jointly combine with inherent IQ imbalance and AoA features to design a hybrid authentication scheme and thus construct a multi-dimensional fingerprint space for a comprehensive characterization of UAV identities. To theoretically evaluate the effectiveness of the proposed authentication framework, the analytical closed-form expressions of performance metrics like false alarm and detection probabilities are also exactly derived based on the statistical signal processing technology and composite hypothesis testing. Finally, we provide large simulation results to validate the correctness and feasibility of the proposed theoretical models, and also discuss the relation between system security and communication service quality under different artificial fingerprint level.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 618-629"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147800840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent joint communication and computation scheme of UAV-assisted offloading in high speed rail scenarios","authors":"Xiqi Cheng ,&nbsp;Jingxuan Zhang ,&nbsp;Xiaodong Xu ,&nbsp;Shujun Han ,&nbsp;Bizhu Wang ,&nbsp;Mengyin Sun ,&nbsp;Ping Zhang","doi":"10.1016/j.dcan.2024.09.002","DOIUrl":"10.1016/j.dcan.2024.09.002","url":null,"abstract":"<div><div>With the continuous development of communication and computation technologies, large bandwidth services place higher demands on computing capacity and throughput in High-Speed Rail (HSR) scenarios. On the one hand, Millimetre-Wave enables high data transmission rates, but leads to high Doppler frequency deviation as well as large path loss. On the other hand, the development of Mobile Edge Computing greatly alleviates user computing congestion. In this paper, we propose the Adaptive Joint Communication and Computation Resource Allocation scheme to solve the energy optimization problem of a dual-band UAV and Mobile Relay relay-assisted HSR offloading system. This scheme works well to optimize the performance of the system through resource allocation. In addition, since the optimization problem is modeled as a Mixed-Integer Nonlinear Program, we propose the Pre- and Post-state connected Parameterized Deep Q-Network algorithm, which is based on Deep Reinforcement Learning approach, for offloading decision and bandwidth resource allocation. Simulation results show that the proposed algorithms result in lower system energy consumption, while ensuring a high task completion rate as well.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 594-606"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147800838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Service chain caching and task offloading in two-tier UAV-assisted vehicular edge computing networks: An attention-DRL method","authors":"Fengyan Wu ,&nbsp;Chao Yang ,&nbsp;Yanqun Tang ,&nbsp;Zhen Li ,&nbsp;Shengli Xie","doi":"10.1016/j.dcan.2024.10.005","DOIUrl":"10.1016/j.dcan.2024.10.005","url":null,"abstract":"<div><div>Unmanned Aerial Vehicle (UAV)-assisted Vehicular Edge Computing Networks (VECNs) have emerged as a promising solution to enhance service quality for ground vehicle users. However, the growing demands from users and the limited computing and storage resources of UAVs present significant challenges in designing an efficient edge service caching scheme to minimize latency. Moreover, the integration of service caching and task offloading complicates the support of complex tasks by a single UAV. To address these challenges, this paper proposes a novel two-tier UAV-assisted VECNs framework. In this framework, multi-rotor UAVs function as hovering nodes for computational offloading, while a fixed-wing UAV serves as a mobile auxiliary cloud platform, forming a cohesive UAV group. User tasks are structured into a task chain based on the available UAVs. We integrate a joint service chain caching and task offloading scheme that considers UAV computing and storage capacities, duplicate caching, and dynamic transmission latency. To optimize task chain completion latency, we propose an Attention-based Multi-Agent Deep Q-Network (A-MADQN) algorithm. This algorithm incorporates an attention mechanism to narrow the UAV selection space, enabling the selected UAVs to collaboratively make caching and task offloading decisions. Numerical results demonstrate that the proposed algorithm significantly enhances system processing efficiency and reduces task completion latency compared to the benchmark approaches.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 607-617"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147800841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EHKP-Res: an explainable dynamic security solution for medical healthcare","authors":"Xiaoyan Chen ,&nbsp;Jiahong Cai ,&nbsp;Weidong Xiao ,&nbsp;Yingzi Huo ,&nbsp;Jin Wang ,&nbsp;Wei Liang","doi":"10.1016/j.dcan.2024.11.006","DOIUrl":"10.1016/j.dcan.2024.11.006","url":null,"abstract":"<div><div>An increasing number of medical institutions and patients are adopting the practice of storing their data on medical cloud servers, which greatly facilitates the sharing of medical data. However, when many nodes are connected to the medical big data architecture, the overall system's security can be compromised. To address this, an explainable dynamic security protection scheme for medical healthcare, named EHKP-Res, has been proposed. This scheme aggregates data relevance features and integrates them with a deep residual network to predict the behavior of medical staff, addressing the issue of sudden changes in their behavioral credibility. To tackle the black-box problem, a model post-interpretation scheme using Bayesian networks to generate perturbed datasets has been proposed. It calculates sample weights and performs nonlinear fitting, deriving model interpretation through the eigenvalues of the instances. Experimental results show that the proposed Hidden KP-ABE method reduces time overhead by 14.95% compared to other access control methods and achieves a prediction accuracy of 98.34% for doctor trust metrics, effectively preventing malicious behavior by doctors.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 462-471"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147703261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing foreign fiber segmentation performance with DeepLab V3+ and GAN in industrial IoE environments","authors":"Shuo Yang ,&nbsp;Jingbin Li ,&nbsp;Yang Li ,&nbsp;Jing Nie ,&nbsp;Dian Guo ,&nbsp;Liqing Hu ,&nbsp;Yugang Feng ,&nbsp;Liansheng Zhang","doi":"10.1016/j.dcan.2025.03.005","DOIUrl":"10.1016/j.dcan.2025.03.005","url":null,"abstract":"<div><div>In industrial Internet of Everything (IoE) environments, the precise detection of tiny foreign fibers on the surface of bobbin yarns is crucial for ensuring the quality of textile products. However, detecting these fibers often exceeds the capabilities of both human vision and existing automation equipment. To address this challenge, this research proposes a novel foreign fiber segmentation method that integrates Generative Adversarial Networks (GANs) with an enhanced encoder-decoder architecture, significantly improving detection accuracy in industrial IoE scenarios. Specifically, we develop a dual-path attention encoding network that synergistically combines MobileNetV2's computational efficiency with ContextNet's multi-scale contextual awareness, thereby enhancing the extraction of contextual features for tiny foreign fibers. A hybrid channel-spatial attention mechanism is designed by parallel integration of channel-wise excitation and spatial attention mapping, which substantially strengthens the capture of discriminative features for tiny foreign fibers in high-resolution images. The decoding stage employs dense skip-connections to construct multi-scale feature propagation paths, optimizing detail preservation during upsampling processes. To tackle the severe class imbalance in fiber-background pixel distribution, this research introduces a Weighted Binary Cross-Entropy (WBCE) loss function with adaptive focal weighting. Experimental results demonstrate that the proposed DeepLab-DPA framework achieves 98.77% Accuracy, 85.93% MIoU, and balanced performance metrics (87.01% Precision, 86.84% Recall, 86.92% F1-Score), confirming its effectiveness for industrial fiber detection tasks.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 505-519"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147703269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experience replay with cohesive-subgraph awareness for continual graph learning in IoT","authors":"Zhenzhen Xie ,&nbsp;Qi Luo ,&nbsp;Yan Huang ,&nbsp;Yongqi Yin ,&nbsp;Jiaqi Zhang ,&nbsp;Junjie Pang","doi":"10.1016/j.dcan.2025.08.010","DOIUrl":"10.1016/j.dcan.2025.08.010","url":null,"abstract":"<div><div>In the Internet-of-Things (IoT) scenarios, Continual Graph Learning (CGL) has become a key technique for capturing the complex relational structures underlying diverse applications including sensor networks, road systems and biomedical networks. However, the structural changes in these evolving graphs introduce instability, making catastrophic forgetting a primary challenge for CGL. Experience replay is currently a promising method, as it strikes a balance between new and old knowledge. It also provides CGL models with a human-like memory capability. However, prior work rarely leverages the graph's intrinsic properties to proactively identify the critical patterns hiding in the evolving graphs. To this end, we propose a unified framework that integrates cohesion-subgraph awareness into existing CGL mechanisms. We propose a novel cohesive structure-aware experience replay framework that leverages intrinsic graph properties, such as <em>k</em>-core and <em>k</em>-truss metrics, to guide the selection of representative historical nodes for replay. Unlike conventional replay strategies that rely on random sampling or task-driven node selection, our approach systematically identifies structurally significant nodes that encapsulate the evolving patterns of streaming graphs. By integrating these cohesive subgraph properties into the experience replay process, our method effectively preserves critical historical knowledge while adapting to new graph structures with low computational overhead. The experimental results demonstrate that our method consistently outperforms existing replay strategies in mitigating catastrophic forgetting and maintaining classification performance. On the PubMed dataset, our <em>k</em>-core-based replay strategy improves the <em>F</em>1 score by 3.7% compared to random sampling, while reducing training time by up to 85% compared to full retraining. Similarly, on the Cora dataset, our approach achieves a 98.3% <em>F</em>1 score, surpassing baseline methods by 4.5%.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 417-427"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal beamwidth dsign of periodic reference signals for integrated sensing and communications in cellular V2X networks☆","authors":"Chunlei Sun ,&nbsp;Xuqing Liu ,&nbsp;Linpei Li ,&nbsp;Yuzheng Ren ,&nbsp;Dongzhe Li ,&nbsp;Haijun Zhang","doi":"10.1016/j.dcan.2026.03.009","DOIUrl":"10.1016/j.dcan.2026.03.009","url":null,"abstract":"<div><div>Recently, the technique of Integrated Sensing and Communication (ISAC) has gained great attentions and is expected to enable more advanced applications in cellular Vehicle-to-Everything (V2X). We intend to use the periodical reference signals, such as the synchronization signals in 5G, to actively detect targets at different azimuths via beam scanning. This paper mainly aims to optimize the beamwidth of these reference signals in cellular-V2X to facilitate both sensing and communication functions. Firstly, to address the issue of insufficient beam duration for accurate estimation of Doppler and the corresponding velocity as well, we combine multiple beam scanning cycles to support improved sensing performance. In this context, an innovative beamwidth optimization algorithm is proposed. Specifically, we deduce the Fisher Information Matrix (FIM) of the sufficient statistic associated with target azimuths. Considering the randomness of the target position, we build the objective function based on the expected trace of the derived FIM. Additionally, we formulate the constraint of channel coherent time for efficient communications. It is worth noting that this is the first time to optimize the beamwidth of periodic reference signals for joint communication and sensing in cellular V2X. Experimental results show that the optimal beamwidth varies with the steering azimuth of antennas and reaches its maximum when the beam points at the direction perpendicular to the roadside.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 550-559"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147745526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Communication-efficient federated multi-domain learning for network anomaly detection","authors":"Chunjiong Zhang ,&nbsp;Gaoyang Shan ,&nbsp;Byeong-hee Roh","doi":"10.1016/j.dcan.2024.11.014","DOIUrl":"10.1016/j.dcan.2024.11.014","url":null,"abstract":"<div><div>Efficient and accurate anomaly detection in a network is of great significance for maintaining network and device security. Most anomaly detection methods assume that different anomalous network data distributions are the same or similar and ignore data privacy preservation. In this paper, a novel Federated Learning (FL) is proposed that it can quickly detect different types of anomalies in Non-Independent and Identically Distributed (Non-IID) data. First, we design a multi-domain machine learning model for multi-domain data, named Aegean, which consists of two modules: an ensemble AutoEncoder (AE) and a Generative Adversarial Network (GAN). Second, because data from different domains are non-IID, we model the anomaly detection problem as a dual problem, which can be recast as a robust optimization problem. The robust optimization problem is non-convex and therefore difficult to solve. As a remedy, we formulate and solve a dual problem by taking the Lagrangian dual function of the original problem. Experiments demonstrate that Aegean significantly outperforms the current state-of-the-art methods, with a 16% F1 score improvement over that of a One-Class Support Vector Machine (OCSVM). The designed FL significantly reduces the communication overhead of FedAvg without sacrificing anomaly detection performance.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 472-481"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147703263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Path planning of autonomous underwater vehicle for data collection of the Internet of everything","authors":"Desheng Chen,&nbsp;Meng Xi,&nbsp;Jiabao Wen,&nbsp;Jingyi He,&nbsp;Huiao Dai,&nbsp;Wenjie Li","doi":"10.1016/j.dcan.2024.10.004","DOIUrl":"10.1016/j.dcan.2024.10.004","url":null,"abstract":"<div><div>Autonomous Underwater Vehicle (AUV) has become an important tool to accomplish various path planning tasks due to its high intelligence and good maneuverability. Aiming at the problem of data collection at underwater Internet of Everything (IoE) nodes, this paper constructs a complex 3D marine environment based on real marine current data, and proposes a path planning algorithm based on reinforcement learning to ensure that the AUV completes the data collection with a short path length. In particular, in order to address the problem of complex path planning tasks, the Parallel Dense neural Network (PDNet) is proposed to improve the performance of the agent by extracting the core features of the input state. In addition, to simplify the reward shaping, we constructed a marine environment with sparse rewards. Sparse rewards can greatly interfere with the agent's exploration and learning. To solve the sparse reward problem, the Hindsight Experience Replay (HER) is introduced, which not only solves the sparse reward problem, but also improves the sampling efficiency and convergence of the algorithm.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 520-529"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147703267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}