Digital Communications and Networks最新文献

{"title":"Lightweight deep reinforcement learning for dynamic resource allocation in vehicular edge computing","authors":"Dapeng Wu ,&nbsp;Sijun Wu ,&nbsp;Yaping Cui ,&nbsp;Ailing Zhong ,&nbsp;Tong Tang ,&nbsp;Ruyan Wang ,&nbsp;Xinqi Lin","doi":"10.1016/j.dcan.2025.06.005","DOIUrl":"10.1016/j.dcan.2025.06.005","url":null,"abstract":"<div><div>Vehicular Edge Computing (VEC) enhances the quality of user services by deploying wealth of resources near vehicles. However, due to highly dynamic and complex nature of vehicular networks, centralized decision-making for resource allocation proves inadequate within VECs. Conversely, allocating resources via distributed decision-making consumes vehicular resources. To improve the quality of user service, we formulate a problem of latency minimization, further subdividing this problem into two subproblems to be solved through distributed decision-making. To mitigate the resource consumption caused by distributed decision-making, we propose Reinforcement Learning (RL) algorithm based on sequential alternating multi-agent system mechanism, which effectively reduces the dimensionality of action space without losing the informational content of action, achieving network lightweighting. We discuss the rationality, generalizability, and inherent advantages of proposed mechanism. Simulation results indicate that our proposed mechanism outperforms traditional RL algorithms in terms of stability, generalizability, and adaptability to scenarios with invalid actions, all while achieving network lightweighting.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1530-1542"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529382","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":"Dynamic GNN-based multimodal anomaly detection for spatial crowdsourcing drone services","authors":"Junaid Akram ,&nbsp;Walayat Hussain ,&nbsp;Rutvij H. Jhaveri ,&nbsp;Rajkumar Singh Rathore ,&nbsp;Ali Anaissi","doi":"10.1016/j.dcan.2025.03.012","DOIUrl":"10.1016/j.dcan.2025.03.012","url":null,"abstract":"<div><div>We introduce a pioneering anomaly detection framework within spatial crowdsourcing Internet of Drone Things (IoDT), specifically designed to improve bushfire management in Australia's expanding urban areas. This framework innovatively combines Graph Neural Networks (GNN) and advanced data fusion techniques to enhance IoDT capabilities. Through spatial crowdsourcing, drones collectively gather diverse, real-time data across multiple locations, creating a rich dataset for analysis. This method integrates spatial, temporal, and various data modalities, facilitating early bushfire detection by identifying subtle environmental and operational changes. Utilizing a complex GNN architecture, our model effectively processes the intricacies of spatially crowdsourced data, significantly increasing anomaly detection accuracy. It incorporates modules for temporal pattern recognition and spatial analysis of environmental impacts, leveraging multimodal data to detect a wide range of anomalies, from temperature shifts to humidity variations. Our approach has been empirically validated, achieving an F1 score of 0.885, highlighting its superior anomaly detection performance. This integration of spatial crowdsourcing with IoDT not only establishes a new standard for environmental monitoring but also contributes significantly to disaster management and urban sustainability.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1639-1656"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529447","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":"Rate-splitting multiple access-assisted ISAC design in NAFD cell-free mMIMO systems","authors":"Jilong Wu ,&nbsp;Fuping Si ,&nbsp;Dongming Wang ,&nbsp;Pengcheng Zhu","doi":"10.1016/j.dcan.2025.05.014","DOIUrl":"10.1016/j.dcan.2025.05.014","url":null,"abstract":"<div><div>Integrated Sensing And Communication (ISAC) is regarded as a promising technology for facilitating the rapid advancement of Sixth-Generation (6G) due to its concurrent transmission of information and environmental sensing capabilities. Rate-Splitting Multiple Access (RSMA), through the utilization of Successive Interference Cancellation (SIC) and Rate-Splitting (RS) at the transceiver, can fulfill the sensing requirement and supersede individual radar sequence to mitigate the interference between communication and sensing. This paper investigates the transceiver design of the RSMA-assisted ISAC in a Network-Assisted Full-Duplex (NAFD) cell-free Massive Multiple-Input Multiple-Output (mMIMO) system. We first derive the expressions of the communication achievable data rate and radar sensing Signal to Interference plus Noise Ratio (SINR). Subsequently, an optimization problem is formulated to maximize the communication achievable data rate, subject to both radar sensing constraints and fronthaul constraints, an effective algorithm based on sparse beamforming scheme and Semi-Definite Relaxation (SDR) is then proposed to acquire the near-optimal transceiver. Numerical results demonstrate that the application of RSMA technology in ISAC systems can significantly enhance system performance, and reveal that Dual-Functionalities Radar-Communication (DFRC) scheme can achieve higher data rate than the traditional scheme.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1668-1678"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529448","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":"Design and performance analysis of reconfigurable intelligent surface and half-duplex amplify-and-forward relay hybrid network system","authors":"Feng Zheng ,&nbsp;Yiyuan Liang ,&nbsp;Bin Ni","doi":"10.1016/j.dcan.2025.06.003","DOIUrl":"10.1016/j.dcan.2025.06.003","url":null,"abstract":"<div><div>A Reconfigurable Intelligent Surface (RIS) can relay signals from the transmitter to the receiver. In this regard, RISs operate similarly to traditional relays. We design a Multiple-Input-Multiple-Output (MIMO) system with a hybrid network of RIS and Half-Duplex (HD) Amplify-and-Forward (AF) relay. We model the system's signal propagation and propose a new algorithm to get the system's Achievable Rate (AR) value. We complete simulations to evaluate the performance of the RIS and HD-AF relay hybrid network system compared to the system assisted by either the RIS or HD-AF relay. The simulations indicate that many factors can considerably influence the system performance. Selecting an optimal placement for the RIS and relay can result in the best performance for the RIS and HD-AF relay hybrid network system in situations where the direct link between the receiver and transmitter is absent.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1436-1446"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529498","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":"T2L: A traceable and trustable consortium blockchain for logistics","authors":"Ming He ,&nbsp;Haodi Wang ,&nbsp;Yunchuan Sun ,&nbsp;Rongfang Bie ,&nbsp;Tian Lan ,&nbsp;Qi Song ,&nbsp;Xi Zeng ,&nbsp;Matevz̆ Pustisĕk ,&nbsp;Zhenyu Qiu","doi":"10.1016/j.dcan.2022.06.015","DOIUrl":"10.1016/j.dcan.2022.06.015","url":null,"abstract":"<div><div>Traceability and trustiness are two critical issues in the logistics sector. Blockchain provides a potential way for logistics tracking systems due to its traits of tamper resistance. However, it is non-trivial to apply blockchain on logistics because of firstly, the binding relationship between virtue data and physical location cannot be guaranteed so that frauds may exist. Secondly, it is neither practical to upload complete data on the blockchain due to the limited storage resources nor convincing to trust the digest of the data. This paper proposes a traceable and trustable consortium blockchain for logistics <em>T</em>²<em>L</em> to provide an efficient solution to the mentioned problems. Specifically, the authenticated geocoding data from telecom operators’ base stations are adopted to ensure the location credibility of the data before being uploaded to the blockchain for the purpose of reliable traceability of the logistics. Moreover, we propose a scheme based on Zero Knowledge Proof of Retrievability (ZK BLS-PoR) to ensure the trustiness of the data digest and the proofs to the blockchain. Any user in the system can check the data completeness by verifying the proofs instead of downloading and examining the whole data based on the proposed ZK BLS- PoR scheme, which can provide solid theoretical verification. In all, the proposed <em>T</em>²<em>L</em> framework is a traceable and trustable logistics system with a high level of security.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1385-1393"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47475539","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 structured distributed learning framework for irregular cellular spatial-temporal traffic prediction","authors":"Xiangyu Chen ,&nbsp;Kaisa Zhang ,&nbsp;Gang Chuai ,&nbsp;Weidong Gao ,&nbsp;Xuewen Liu ,&nbsp;Yibo Zhang ,&nbsp;Yijian Hou","doi":"10.1016/j.dcan.2025.04.003","DOIUrl":"10.1016/j.dcan.2025.04.003","url":null,"abstract":"<div><div>Spatial-temporal traffic prediction technology is crucial for network planning, resource allocation optimizing, and user experience improving. With the development of virtual network operators, multi-operator collaborations, and edge computing, spatial-temporal traffic data has taken on a distributed nature. Consequently, non-centralized spatial-temporal traffic prediction solutions have emerged as a recent research focus. Currently, the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station. This method reduces additional burden on communication systems. However, this method has a drawback: it cannot handle irregular traffic data. Due to unstable wireless network environments, device failures, insufficient storage resources, etc., data missing inevitably occurs during the process of collecting traffic data. This results in the irregular nature of distributed traffic data. Yet, commonly used traffic prediction models such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) typically assume that the data is complete and regular. To address the challenge of handling irregular traffic data, this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic. To solve the aforementioned problems, this paper introduces split learning to design a structured distributed learning framework. The framework comprises a Global-level Spatial structure mining Model (GSM) and several Node-level Generative Models (NGMs). NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller. Firstly, the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables. Secondly, GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data. Finally, NGM generates future traffic based on latent temporal and spatial feature variables. The introduction of the time attention mechanism enhances the framework's capability to handle irregular traffic data. Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction, which compensates for missing information in local irregular traffic data. The proposed framework effectively addresses the distributed prediction issues of irregular traffic data. By testing on real world datasets, the proposed framework improves traffic prediction accuracy by 35% compared to other commonly used distributed traffic prediction methods.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1457-1468"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529380","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":"DWT-3DRec: DeepJSCC-based wireless transmission for efficient 3D scene reconstruction using CityNeRF","authors":"Shuang Cao ,&nbsp;Jie Li ,&nbsp;Ruiyun Yu ,&nbsp;Xingwei Wang ,&nbsp;Jianing Duan","doi":"10.1016/j.dcan.2025.06.010","DOIUrl":"10.1016/j.dcan.2025.06.010","url":null,"abstract":"<div><div>The Unmanned Aerial Vehicle (UAV)-assisted sensing–transmission–computing integrated system plays a vital role in emergency rescue scenarios involving damaged infrastructure. To tackle the challenges of data transmission and enable timely rescue decision-making, we propose DWT-3DRec—an efficient wireless transmission model for 3D scene reconstruction. This model leverages MobileNetV2 to extract image and pose features, which are transmitted through a Dual-path Adaptive Noise Modulation network (DANM). Moreover, we introduce the Gumbel Channel Masking Module (GCMM), which enhances feature extraction and improves reconstruction reliability by mitigating the effects of dynamic noise. At the ground receiver, the Multi-scale Deep Source–Channel Coding for 3D Reconstruction (MDS-3DRecon) framework integrates Deep Joint Source-Channel Coding (DeepJSCC) with Cityscale Neural Radiance Fields (CityNeRF). It adopts a progressive close-view training strategy and incorporates an Adaptive Fusion Module (AFM) to achieve high-precision scene reconstruction. Experimental results demonstrate that DWT-3DRec significantly outperforms the Joint Photographic Experts Group (JPEG) standard in transmitting image and pose data, achieving an average loss as low as 0.0323 and exhibiting strong robustness across a Signal-to-Noise Ratio (SNR) range of 5–20 dB. In large-scale 3D scene reconstruction tasks, MDS-3DRecon surpasses Multum in Parvo Neural Radiance Fields (Mip-NeRF) and Bungee Neural Radiance Field (BungeeNeRF), achieving a Peak Signal-to-Noise Ratio (PSNR) of 24.921 dB and a reconstruction loss of 0.188. Ablation studies further confirm the essential roles of GCMM, DANM, and AFM in enabling high-fidelity 3D reconstruction.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1370-1384"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529493","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":"DRL-based federated self-supervised learning for task offloading and resource allocation in ISAC-enabled vehicle edge computing","authors":"Xueying Gu ,&nbsp;Qiong Wu ,&nbsp;Pingyi Fan ,&nbsp;Nan Cheng ,&nbsp;Wen Chen ,&nbsp;Khaled B. Letaief","doi":"10.1016/j.dcan.2024.12.009","DOIUrl":"10.1016/j.dcan.2024.12.009","url":null,"abstract":"<div><div>Intelligent Transportation Systems (ITS) leverage Integrated Sensing and Communications (ISAC) to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles (IoV). This integration inevitably increases computing demands, risking real-time system stability. Vehicle Edge Computing (VEC) addresses this by offloading tasks to Road Side Units (RSUs), ensuring timely services. Our previous work, the FLSimCo algorithm, which uses local resources for federated Self-Supervised Learning (SSL), has a limitation: vehicles often can't complete all iteration tasks. Our improved algorithm offloads partial tasks to RSUs and optimizes energy consumption by adjusting transmission power, CPU frequency, and task assignment ratios, balancing local and RSU-based training. Meanwhile, setting an offloading threshold further prevents inefficiencies. Simulation results show that the enhanced algorithm reduces energy consumption and improves offloading efficiency and accuracy of federated SSL.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1614-1627"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528981","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":"Pivotal role of digital twins in the metaverse: A review","authors":"Siva Sai ,&nbsp;Pulkit Sharma ,&nbsp;Aanchal Gaur ,&nbsp;Vinay Chamola","doi":"10.1016/j.dcan.2024.12.003","DOIUrl":"10.1016/j.dcan.2024.12.003","url":null,"abstract":"<div><div>The ascent of the metaverse signifies a profound transformation in our digital landscape, ushering in a complex network of interlinked virtual domains and digital spaces. In this burgeoning metaverse, a paradigm shift is seen in how people engage, collaborate, and become immersed in digital environments. An especially intriguing concept taking root within this metaverse landscape is that of digital twins. Initially rooted in industrial and Internet of Things (IoT) contexts, digital twins are now making their mark in the metaverse, presenting opportunities to elevate user experiences, introduce novel dimensions of interaction, and seamlessly bridge the divide between the virtual and physical realms. Digital twins, conceived initially to replicate physical entities in real-time, have transcended their industrial origins in this new metaverse context. They no longer solely replicate physical objects but extend their domain to encompass digital entities, avatars, virtual environments, and users. Despite the vital contributions of digital twins in the metaverse, there has been no research that has explored the applications and scope of digital twins in the metaverse comprehensively. However, there are a few papers focusing on some particular applications. Addressing this research gap, we present an in-depth review of the pivotal role of application digital twins in the metaverse. We present 15 digital twin applications in the metaverse, ranging from simulation and training to emergency preparedness. This study outlines the critical limitations of integrating digital twins and metaverse and several future research directions.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1343-1355"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529494","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":"UAV-assisted full-duplex ISAC: Joint communication scheduling, beamforming, and trajectory optimization","authors":"Yuanshuo Gang,&nbsp;Yuexia Zhang,&nbsp;Xinyi Wang","doi":"10.1016/j.dcan.2025.03.001","DOIUrl":"10.1016/j.dcan.2025.03.001","url":null,"abstract":"<div><div>This paper proposes the Unmanned Aerial Vehicle (UAV)-assisted Full-Duplex (FD) Integrated Sensing And Communication (ISAC) system. In this system, the UAV integrates sensing and communication functions, capable of receiving transmission signals from Uplink (UL) users and echo signal from target, while communicating with Downlink (DL) users and simultaneously detecting target. With the objective of maximizing the Average Sum Rate (ASR) for both UL and DL users, a composite non-convex optimization problem is established, which is decomposed into sub-problems of communication scheduling optimization, transceiver beamforming design, and UAV trajectory optimization. An alternating iterative algorithm is proposed, employing relaxation optimization, extremum traversal search, augmented weighted minimum mean square error, and successive convex approximation methods to solve the aforementioned sub-problems. Simulation results demonstrate that, compared to the traditional UAV-assisted Half-Duplex (HD) ISAC scheme, the proposed FD ISAC scheme effectively improves the ASR.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1628-1638"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529446","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}