Digital Communications and Networks最新文献

{"title":"SEAttention-residual based channel estimation for mmWave massive MIMO systems in IoV scenarios","authors":"Junhui Zhao ,&nbsp;Ruixing Ren ,&nbsp;Yao Wu ,&nbsp;Qingmiao Zhang ,&nbsp;Wei Xu ,&nbsp;Dongming Wang ,&nbsp;Lisheng Fan","doi":"10.1016/j.dcan.2024.04.005","DOIUrl":"10.1016/j.dcan.2024.04.005","url":null,"abstract":"<div><div>To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) systems in Internet of Vehicles (IoV) scenarios, the paper proposes a deep learning (DL) algorithm, Squeeze-and-Excitation Attention Residual Network (SEARNet), which integrates Squeeze-and-Excitation Attention (SEAttention) mechanism and residual module. Specifically, SEARNet considers the channel information as an image matrix, and embeds a SEAttention module in residual module to construct the SEAttention-Residual block. Through a data-driven approach, SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism, thereby reducing noise interference and estimating the channel in an accurate and efficient manner. The simulation results show that compared to two traditional and two DL channel estimation algorithms, the proposed SEARNet can achieve a maximum reduction in normalized mean square error (NMSE) of 97.66% and 84.49% at SNR of -10 dB, 78.18% at SNR of 5 dB, and 43.51% at SNR of 10 dB, respectively.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 778-786"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141039821","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":"Computation and wireless resource management in 6G space-integrated-ground access networks","authors":"Ning Hui ,&nbsp;Qian Sun ,&nbsp;Lin Tian ,&nbsp;Yuanyuan Wang ,&nbsp;Yiqing Zhou","doi":"10.1016/j.dcan.2024.04.001","DOIUrl":"10.1016/j.dcan.2024.04.001","url":null,"abstract":"<div><div>In 6th Generation Mobile Networks (6G), the Space-Integrated-Ground (SIG) Radio Access Network (RAN) promises seamless coverage and exceptionally high Quality of Service (QoS) for diverse services. However, achieving this necessitates effective management of computation and wireless resources tailored to the requirements of various services. The heterogeneity of computation resources and interference among shared wireless resources pose significant coordination and management challenges. To solve these problems, this work provides an overview of multi-dimensional resource management in 6G SIG RAN, including computation and wireless resource. Firstly it provides with a review of current investigations on computation and wireless resource management and an analysis of existing deficiencies and challenges. Then focusing on the provided challenges, the work proposes an MEC-based computation resource management scheme and a mixed numerology-based wireless resource management scheme. Furthermore, it outlines promising future technologies, including joint model-driven and data-driven resource management technology, and blockchain-based resource management technology within the 6G SIG network. The work also highlights remaining challenges, such as reducing communication costs associated with unstable ground-to-satellite links and overcoming barriers posed by spectrum isolation. Overall, this comprehensive approach aims to pave the way for efficient and effective resource management in future 6G networks.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 768-777"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140791686","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":"Research on paging enhancements for 5G-A downlink transmission energy saving","authors":"Wenxin Ma ,&nbsp;Weidong Gao ,&nbsp;Jiaqi Liu ,&nbsp;Kaisa Zhang ,&nbsp;Xu Zhao ,&nbsp;Bingfeng Cui ,&nbsp;Shujuan Sun ,&nbsp;Shurong Li","doi":"10.1016/j.dcan.2024.07.005","DOIUrl":"10.1016/j.dcan.2024.07.005","url":null,"abstract":"<div><div>5G-Advanced (5G-A), an evolutionary iteration of 5G, effectively enhances 5G services. The increasing complexity in downlink services scenarios stresses the necessity for research into the integration of efficient communication with low-carbon solutions. Historically, there has been an emphasis on reliability and precision, at the expense of power consumption. Although energy-saving technologies like Idle mode-Discontinuous Reception (IDRX) and Paging Early Indication (PEI) have been introduced to reduce power consumption in UE, they have not been fully tailored to the paging characteristics of 5G-A downlink services. In this paper, we take full account of the impact of paging message density on energy saving measures and propose an enhanced paging technology, termed Predictive-PEI (PPEI), which is designed to reduce UE overhead while minimizing latency whenever possible. Towards this end, we design a dual threshold decision framework founded on machine learning, mainly involving two steps. We first use the LSTM-FNN neural network to forecast the arrival moment of upcoming paging messages based on past real information. Then, the output of the initial prediction is as the input of the next dual threshold decision algorithm, to determine the optimal moment for transmitting the PEI. The restrictive factors, encompass average delay threshold and cache capacity threshold, playing a role in decisions regarding paging message caching and decoding. Compared to the existing schemes, our PPEI scheme flexibly sends efficient PEI according to the actual paging characteristics by introducing machine learning, resulting in substantial power savings of up to 38.89% while concurrently ensuring effective latency control.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 818-828"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330038","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":"DB-FL: DAG blockchain-enabled generalized federated dropout learning","authors":"Sa Xiao ,&nbsp;Xiaoge Huang ,&nbsp;Xuesong Deng ,&nbsp;Bin Cao ,&nbsp;Qianbin Chen","doi":"10.1016/j.dcan.2024.09.005","DOIUrl":"10.1016/j.dcan.2024.09.005","url":null,"abstract":"<div><div>To protect user privacy and data security, the integration of Federated Learning (FL) and blockchain has become an emerging research hotspot. However, the limited throughput and high communication complexity of traditional blockchains limit their application in large-scale FL tasks, and the synchronous traditional FL will also reduce the training efficiency. To address these issues, in this paper, we propose a Directed Acyclic Graph (DAG) blockchain-enabled generalized Federated Dropout (FD) learning strategy, which could improve the efficiency of FL while ensuring the model generalization. Specifically, the DAG maintained by multiple edge servers will guarantee the security and traceability of the data, and the Reputation-based Tips Selection Algorithm (RTSA) is proposed to reduce the blockchain consensus delay. Second, the semi-asynchronous training among Intelligent Devices (IDs) is adopted to improve the training efficiency, and a reputation-based FD technology is proposed to prevent overfitting of the model. In addition, a Hybrid Optimal Resource Allocation (HORA) algorithm is introduced to minimize the network delay. Finally, simulation results demonstrate the effectiveness and superiority of the proposed algorithms.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 886-897"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330044","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":"Secure cognitive health monitoring using a directed acyclic graph-based and AI-enhanced IoMT framework","authors":"Shashank Srivastava ,&nbsp;Kartikeya Kansal ,&nbsp;Siva Sai ,&nbsp;Vinay Chamola","doi":"10.1016/j.dcan.2024.08.014","DOIUrl":"10.1016/j.dcan.2024.08.014","url":null,"abstract":"<div><div>Millions of people throughout the world struggle with mental health disorders, but the widespread stigma associated with these issues often prevents them from seeking treatment. We propose a novel strategy that integrates Internet of Medical Things (IoMT), DAG-based hedera technology, and Artificial Intelligence (AI) to overcome these challenges. We also consider the costs of chronic diseases such as Parkinson's and Alzheimer's, which often require 24-hour care. Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration, our system aims to provide low-cost, continuous support. Since IoMT data is large in volume, we need a blockchain network with high transaction throughput without compromising the privacy of patient data. To address this concern, we propose to use Hedera technology to ensure the privacy, and security of personal mental health information, scalability and a faster transaction confirmation rate. Overall, this research paper outlines a holistic approach to mental health monitoring that respects privacy, promotes accessibility, and harnesses the potential of emerging technologies. By combining IoMT, Hedera, and AI, we offer a solution that helps break down the barriers preventing individuals from seeking mental well-being support. Furthermore, comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%, which is more than 30% better than traditional models such as logistic regression.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 594-602"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330127","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":"Dynamically redactable blockchain based on decentralized Chameleon hash","authors":"Xinzhe Huang ,&nbsp;Yujue Wang ,&nbsp;Yong Ding ,&nbsp;Qianhong Wu ,&nbsp;Changsong Yang ,&nbsp;Hai Liang","doi":"10.1016/j.dcan.2024.10.013","DOIUrl":"10.1016/j.dcan.2024.10.013","url":null,"abstract":"<div><div>The immutability is a crucial property for blockchain applications, however, it also leads to problems such as the inability to revise illegal data on the blockchain and delete private data. Although redactable blockchains enable on-chain modification, they suffer from inefficiency and excessive centralization, the majority of redactable blockchain schemes ignore the difficult problems of traceability and consistency check. In this paper, we present a Dynamically Redactable Blockchain based on decentralized Chameleon hash (DRBC). Specifically, we propose an Identity-Based Decentralized Chameleon Hash (IDCH) and a Version-Based Transaction structure (VT) to realize the traceability of transaction modifications in a decentralized environment. Then, we propose an efficient block consistency check protocol based on the Bloom filter tree, which can realize the consistency check of transactions with extremely low time and space cost. Security analysis and experiment results demonstrate the reliability of DRBC and its significant advantages in a decentralized environment.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 757-767"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330055","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 blockchain-based anonymous and regulated e-taxing protocol for art trading","authors":"Xuejing Liu ,&nbsp;Hongli Liu ,&nbsp;Xingyu Liang ,&nbsp;Yuchao Yao","doi":"10.1016/j.dcan.2025.01.002","DOIUrl":"10.1016/j.dcan.2025.01.002","url":null,"abstract":"<div><div>Taxation, the primary source of fiscal revenue, has profound implications in guiding resource allocation, promoting economic growth, adjusting social wealth distribution, and enhancing cultural influence. The development of e-taxation provides a enhanced security for taxation, but it still faces the risk of inefficiency and tax data leakage. As a decentralized ledger, blockchain provides an effective solution for protecting tax data and avoiding tax-related errors and fraud. The introduction of blockchain into e-taxation protocols can ensure the public verification of taxes. However, balancing taxpayer identity privacy with regulation remains a challenge. In this paper, we propose a blockchain-based anonymous and regulatory e-taxation protocol. This protocol ensures the supervision and tracking of malicious taxpayers while maintaining honest taxpayer identity privacy, reduces the storage needs for public key certificates in the public key infrastructure, and enables self-certification of taxpayers' public keys and addresses. We formalize the security model of unforgeability for transactions, anonymity for honest taxpayers, and traceability for malicious taxpayers. Security analysis shows that the proposed protocol satisfies unforgeability, anonymity, and traceability. The experimental results of time consumption show that the protocol is feasible in practical applications.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 681-688"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330047","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 fast and accurate multi-keyword sorted searchable scheme based on blockchain","authors":"Jin Sun,&nbsp;Lu Wang,&nbsp;Mengna Kang,&nbsp;Kexin Ye","doi":"10.1016/j.dcan.2024.07.003","DOIUrl":"10.1016/j.dcan.2024.07.003","url":null,"abstract":"<div><div>The multi-keyword sorted searchable encryption is a practical and secure data processing technique. However, most of the existing schemes require each data owner to calculate and store the Inverse Document Frequency (IDF) value, and then dynamically summarize them into a global IDF value. This not only hinders efficient sharing of massive data but also may cause privacy disclosure. Additionally, using a cloud server as storage and computing center can compromise file integrity and create a single point of failure. To address these challenges, our proposal leverages the complex interactive environment and massive data scenarios of the supply chain to introduce a fast and accurate multi-keyword search scheme based on blockchain technology. Specifically, encrypted files are first stored in an Interplanetary File System (IPFS), while secure indexes are stored in a blockchain to eliminate single points of failure. Moreover, we employ homomorphic encryption algorithms to design a blockchain-based index tree that enables dynamic adaptive calculation of IDF values, dynamic update of indexes, and multi-keyword sorting search capabilities. Notably, we have specifically designed a two-round sorting search mode called “Match Sort + Score Sort” for achieving fast and accurate searching performance. Furthermore, fair payment contracts have been implemented on the blockchain to incentivize data sharing activities. Through rigorous safety analysis and comprehensive performance evaluation tests, our scheme has been proven effective as well as practical.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 711-723"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141697898","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":"Role of deep learning in cognitive healthcare: Wearable signal analysis, algorithms, benefits, and challenges","authors":"Md. Sakib Bin Alam ,&nbsp;Aiman Lameesa ,&nbsp;Senzuti Sharmin ,&nbsp;Shaila Afrin ,&nbsp;Shams Forruque Ahmed ,&nbsp;Mohammad Reza Nikoo ,&nbsp;Amir H. Gandomi","doi":"10.1016/j.dcan.2025.04.001","DOIUrl":"10.1016/j.dcan.2025.04.001","url":null,"abstract":"<div><div>Deep Learning (DL) offers promising solutions for analyzing wearable signals and gaining valuable insights into cognitive disorders. While previous review studies have explored various aspects of DL in cognitive healthcare, there remains a lack of comprehensive analysis that integrates wearable signals, data processing techniques, and the broader applications, benefits, and challenges of DL methods. Addressing this limitation, our study provides an extensive review of DL's role in cognitive healthcare, with a particular emphasis on wearables, data processing, and the inherent challenges in this field. This review also highlights the considerable promise of DL approaches in addressing a broad spectrum of cognitive issues. By enhancing the understanding and analysis of wearable signal modalities, DL models can achieve remarkable accuracy in cognitive healthcare. Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-term Memory (LSTM) networks have demonstrated improved performance and effectiveness in the early diagnosis and progression monitoring of neurological disorders. Beyond cognitive impairment detection, DL has been applied to emotion recognition, sleep analysis, stress monitoring, and neurofeedback. These applications lead to advanced diagnosis, personalized treatment, early intervention, assistive technologies, remote monitoring, and reduced healthcare costs. Nevertheless, the integration of DL and wearable technologies presents several challenges, such as data quality, privacy, interpretability, model generalizability, ethical concerns, and clinical adoption. These challenges emphasize the importance of conducting future research in areas such as multimodal signal analysis and explainable AI. The findings of this review aim to benefit clinicians, healthcare professionals, and society by facilitating better patient outcomes in cognitive healthcare.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 642-670"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330129","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":"Deep reinforcement learning based latency-energy minimization in smart healthcare network","authors":"Xin Su ,&nbsp;Xin Fang ,&nbsp;Zhen Cheng ,&nbsp;Ziyang Gong ,&nbsp;Chang Choi","doi":"10.1016/j.dcan.2024.06.008","DOIUrl":"10.1016/j.dcan.2024.06.008","url":null,"abstract":"<div><div>Significant breakthroughs in the Internet of Things (IoT) and 5G technologies have driven several smart healthcare activities, leading to a flood of computationally intensive applications in smart healthcare networks. Mobile Edge Computing (MEC) is considered as an efficient solution to provide powerful computing capabilities to latency or energy sensitive nodes. The low-latency and high-reliability requirements of healthcare application services can be met through optimal offloading and resource allocation for the computational tasks of the nodes. In this study, we established a system model consisting of two types of nodes by considering nondivisible and trade-off computational tasks between latency and energy consumption. To minimize processing cost of the system tasks, a Mixed-Integer Nonlinear Programming (MINLP) task offloading problem is proposed. Furthermore, this problem is decomposed into task offloading decisions and resource allocation problems. The resource allocation problem is solved using traditional optimization algorithms, and the offloading decision problem is solved using a deep reinforcement learning algorithm. We propose an Online Offloading based on the Deep Reinforcement Learning (OO-DRL) algorithm with parallel deep neural networks and a weight-sensitive experience replay mechanism. Simulation results show that, compared with several existing methods, our proposed algorithm can perform real-time task offloading in a smart healthcare network in dynamically varying environments and reduce the system task processing cost.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 795-805"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330056","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}