Digital Communications and Networks最新文献

{"title":"Computing power networks for unmanned aerial vehicles: a hierarchical resources trading market","authors":"Xiaofei Wang ,&nbsp;Hui Deng ,&nbsp;Chao Qiu ,&nbsp;Zheyuan Chen ,&nbsp;Tao Luo ,&nbsp;Zhao Ming","doi":"10.1016/j.dcan.2024.12.002","DOIUrl":"10.1016/j.dcan.2024.12.002","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) are increasingly deployed across military and civilian domains due to their operational flexibility, low maintenance costs, and high mobility. With the growing complexity of UAV applications and tasks, robust support from computing power networks is essential. These networks, acting as resource integration paradigms, furnish UAVs with pooled resources to tackle extensive computational demands. In this paper, we develop a framework for trading computing power resources, modeling the transaction process through a three-stage Stackelberg game to facilitate sequential decision-making. We theoretically demonstrate the existence of a Nash equilibrium and introduce a Dynamic Game Reinforcement algorithm to identify optimal strategies. Our experimental results affirm the framework's efficacy and the superior performance of our algorithm. Additionally, we explore how variables like UAV quantity and network congestion influence the market dynamics of the computing power network.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 584-593"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147800839","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":"NPO-BAND: Number and placement optimization for backhaul-aware UAV network deployment","authors":"Sining Yang ,&nbsp;Bo Zhang ,&nbsp;Jinshu Su","doi":"10.1016/j.dcan.2024.11.016","DOIUrl":"10.1016/j.dcan.2024.11.016","url":null,"abstract":"<div><div>In emergency communication scenarios, exploiting Unmanned Aerial Vehicles (UAVs) as relays to provide wireless communication services for ground users has emerged as a promising application. A key challenge in this resource-constrained application is deploying the minimum number of UAVs to form an aerial backhaul network to ensure coverage, which composes the Number and Placement Optimization for the Backhaul-Aware Network Deployment (NPO-BAND) problem. In this paper, we first formulate the NPO-BAND problem based on the geometric disk coverage model. Then, we propose a low-complexity heuristic method to solve this NP-hard problem. The proposed method contains a Very Important Point-Choosing (VIPC) strategy and a Backhaul-Aware Local Coverage (BALC) algorithm. Specifically, the VIPC strategy weighs up the backhaul connectivity constraint and the ground user coverage to choose the VIP, while the BALC algorithm solves the extended 1-center problem to determine the deployment location of each UAV. Simulation results show that the proposed method can effectively reduce the number of deployed UAVs, saving up to 25%-50% of that compared to existing methods across varying numbers and area sizes in clustered distribution patterns of ground users.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 630-639"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147852539","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":"A comprehensive survey of artificial intelligence applications in UAV-enabled wireless networks","authors":"Li Zhou ,&nbsp;Hao Yin ,&nbsp;Haitao Zhao ,&nbsp;Jibo Wei ,&nbsp;Dewen Hu ,&nbsp;Victor C.M. Leung","doi":"10.1016/j.dcan.2024.11.005","DOIUrl":"10.1016/j.dcan.2024.11.005","url":null,"abstract":"<div><div>This comprehensive survey paper examines the applications of Artificial Intelligence (AI) in Unmanned Aerial Vehicle (UAV)-enabled wireless networks. With the increasing demand for efficient and adaptive communication systems, the integration of AI with UAV networks promises to revolutionize various aspects of wireless communication. The paper first outlines the background and motivation behind AI integration, highlighting the potential for enhanced network performance, autonomy, and adaptability. It then delves into the key AI applications across different network layers, including data sensing and collection, placement and trajectory optimization, radio resource management, routing and topology control, edge computing and caching, as well as security and privacy enhancement. For each application, the paper discusses relevant AI techniques, main findings, optimization objects, and the potential benefits and challenges. The survey also identifies open issues, such as the practical implementation gap, standardization issues, and real-world application barriers, and proposes future directions to address these challenges and further advance the field. In conclusion, the integration of AI with UAV-enabled Wireless Networks (UWNs) holds tremendous potential for transforming wireless communication, enabling new applications and services with unprecedented capabilities.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 561-583"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147743639","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":"Distributed context-aware Transformer-enabled dynamic energy consumption prediction for smart building networks","authors":"Rui Dai,&nbsp;Ge Bai","doi":"10.1016/j.dcan.2025.03.006","DOIUrl":"10.1016/j.dcan.2025.03.006","url":null,"abstract":"<div><div>The integration of communication networks and artificial intelligence enables the effective collection of data over smart building networks, facilitating more accurate predictions of Building Energy Consumption (BEC). However, existing schemes for BEC prediction suffer from limited dynamic adaptability, risks of privacy leakage, and the inability to accurately capture actual energy consumption patterns. To improve prediction accuracy while ensuring privacy and dynamic adaptability, we propose a novel BEC prediction design that incorporates dynamic threshold participation and privacy-preserving mechanisms. Specifically, we design a three-tier network architecture integrated with threshold participation tokens to support dynamic access and dropout of building entities during the BEC model construction process. Furthermore, we develop a Context-Aware Transformer (CAT) network integrated into Federated Learning (FL) to enhance feature sensitivity and facilitate the sharing of knowledge derived from Internet of Things (IoT) data and BEC features. Finally, we evaluate the performance of our design using real-world data, and the results demonstrate that our design achieves superior performance in distributed BEC prediction.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 640-648"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147852564","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":"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":"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}