ICT Express最新文献

{"title":"Logistics distribution transportation vehicle route decision-making based on artificial neural network algorithm","authors":"Lifeng Wang,&nbsp;Yongjun Han,&nbsp;Xi Xu","doi":"10.1016/j.icte.2025.11.012","DOIUrl":"10.1016/j.icte.2025.11.012","url":null,"abstract":"<div><div>This paper proposes a logistics distribution route optimization model that integrates long short-term memory (LSTM) and feedforward neural networks (FNN). LSTM captures dynamic time-series features (e.g., traffic, weather), while FNN handles static features (e.g., customer location, historical demand). Their outputs are fused via a fully connected layer and passed through Softmax or Sigmoid to generate the optimal route. Experiments show the model significantly reduces delivery time and cost while improving customer satisfaction. For example, in Task 1—delivering 10 kg of food in Downtown Area A—it achieves an average delivery time of 29 minutes, a cost of 34 yuan, and a satisfaction rating of 4.9, demonstrating its effectiveness as a novel solution for route optimization.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1107-1111"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Age-aware channel selection and packet-size optimization for jamming-resilient cognitive radio networks under heavy-tailed spectrum availability","authors":"Haythem Bany Salameh,&nbsp;Malik Umar","doi":"10.1016/j.icte.2026.07.012","DOIUrl":"10.1016/j.icte.2026.07.012","url":null,"abstract":"<div><div>Cognitive radio allows secondary users to exploit idle licensed spectrum while protecting primary users (PUs), but transmissions fail if a PU returns or jamming occurs. Thus, packet size and channel assignment must be jointly optimized to balance throughput and interruption risk. Unlike prior work that assumes memoryless exponential PU activity and jammer-free intervals, in practice spectrum-occupancy measurements often deviate from the exponential distribution and may exhibit heavy-tailed behavior, making channel age informative. This paper develops a jamming-aware, age-aware contention-based optimization framework with success-probability and signal-to-noise ratio (SNR) constraints. It also derives exact success expressions and a closed-form packet-duration approximation. The main novelty is the coupled channel/packet decision under non-memoryless PU/jammer interruption, exact age-conditioned probability-of-success (PoS), and closed-form packet sizing with exact discrete projection. This provides a lightweight continuous packet-duration candidate, maps it to the practical discrete packet alphabet, and admits it only after exact PoS/SNR rechecking. Simulations show improved throughput over fixed-packet and memoryless methods under realistic PU and jamming distributions.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1183-1188"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient relay-assisted THz V2X networks with integrated sensing and communication","authors":"Xiaosheng Ni,&nbsp;Changjie Hu,&nbsp;Xiongbin Yu,&nbsp;Songyi Wang,&nbsp;Xiong Li,&nbsp;Jingpu Duan","doi":"10.1016/j.icte.2026.08.001","DOIUrl":"10.1016/j.icte.2026.08.001","url":null,"abstract":"<div><div>Terahertz (THz) V2X networks with integrated sensing and communication (ISAC) face severe blockage and energy constraints in urban environments. This letter studies relay-assisted communication with vehicle-side sensing QoS constraints. We formulate a joint beamforming optimization problem to minimize power consumption under strict communication and sensing constraints. An efficient iterative algorithm utilizing Matrix-Extended Generalized Lagrangian Dual Transformation (MEGLDT) and Block Coordinate Descent (BCD) is developed. Simulations demonstrate that the proposed scheme yields significant power savings compared to benchmarks, particularly in high-density scenarios. These findings validate the framework’s scalability and efficacy for next-generation V2X applications.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1241-1245"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GMDDPM-CT: Advancing network intrusion detection through diffusion-based sample synthesis and deep learning","authors":"Gan Zhu,&nbsp;Yongtao Yu,&nbsp;Zhenyuan Li,&nbsp;Yuanchen Dai","doi":"10.1016/j.icte.2026.01.014","DOIUrl":"10.1016/j.icte.2026.01.014","url":null,"abstract":"<div><div>With the increasing complexity of network security threats, Network Intrusion Detection Systems (NIDS) face heightened challenges in identifying minority-class attack samples within network traffic. Detecting these rare attack flows is essential for maintaining system security; however, their low frequency significantly hinders detection. To address this challenge, we introduce GMDDPM-CT, a novel method that integrates the Gaussian and Multinomial Denoising Diffusion Probabilistic Model (GMDDPM) for data balancing with a CNN-Transformer for classification. The GMDDPM component utilizes a diffusion model to generate synthetic samples for minority classes, addressing sample scarcity and enhancing data balance effectively. We conducted experiments on several network traffic datasets, including CIC-IDS2017, CICIoT 2023, NSL-KDD, and UNSW-NB15, by harnessing the CNN-Transformer’s powerful feature extraction capabilities. Our findings indicate that GMDDPM-CT consistently outperforms traditional data balancing methods across key performance metrics, demonstrating high efficacy in detecting minority-class traffic across various datasets. The innovative application of diffusion models to resolve network traffic data imbalance underscores GMDDPM-CT’s strong generalization ability, offering a promising solution for improving intrusion detection in complex network security environments.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1118-1123"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Channel estimation of massive MIMO UV communication with deep pyramid convolution blind denoising network","authors":"Huy Nguyen,&nbsp;Yeong Min Jang","doi":"10.1016/j.icte.2026.07.005","DOIUrl":"10.1016/j.icte.2026.07.005","url":null,"abstract":"<div><div>This paper presents a novel deep learning-based approach for channel estimation in ultraviolet (UV) MIMO systems using a Pyramid Convolutional Blind Denoising Network (CBDNet). The proposed architecture consists of a noise estimation subnetwork and a noise removal subnetwork to effectively handle complex, spatially varying noise in turbulent UV channels. By leveraging multi-scale feature extraction and residual learning, the model accurately estimates the channel matrix under low SNR conditions. Simulation results show that the proposed method achieved the MSE <span><math><mrow><mn>4.5</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></math></span> at 20 dB SNR and outperforms recent ML-based techniques, making it highly suitable for practical UV MIMO communication systems.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1170-1175"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Next generation blockchain architectures: An in depth study of cryptocurrency driven innovation","authors":"Jalaluddin Khan,&nbsp;Ghufran Ahmad Khan,&nbsp;Irfan Alam,&nbsp;Mohammad Rafeek Khan,&nbsp;Shagun Sharma,&nbsp;Tarak Hussain,&nbsp;Mohammad Ahmar Khan,&nbsp;P Manikya Prasuna,&nbsp;Abdulrahman Abdullah Alghamdi","doi":"10.1016/j.icte.2026.09.006","DOIUrl":"10.1016/j.icte.2026.09.006","url":null,"abstract":"<div><div>Blockchain has transformed computerized business applications over the last decade. This was initially intended to be a transparent record of Bitcoin transactions, it has already proved its ability to transform the world, offering trust, security, privacy, and anonymity. Data stored within a blockchain is unalterable and resistant to any alteration, as well as distribution throughout different locations within a system of a decentralized nature. The presence as well as the availability of blockchain are reliant upon strong cryptographic primitives since these are a critical component to the functionality thereof. In light of the continuously increasing cryptographic attacks, the challenge of developing strong evolving software and hardware technologies, the blockchain has managed to retain its position regarding security owing to its basic cryptography primitives. This article explores the conceptual background blockchain, some consensus mechanisms, including the potential impacts of quantum technologies. This research will explore the current level of scientific study on cryptography used in the technologies for blockchains, as well as aims to fill gaps, together with supply a full understanding of their importance in the security of the blockchain. The present investigation employs an exploratory qualitative research approach, which is informed by the most recent literature on the subjects. The results of this study offer a significant resource for the existing knowledge base and improve the understanding of blockchain, the use of cryptography along with cryptographic primitives in the context of blockchain for researchers, whether they are new or experienced. This enables them to recognize new possibilities and difficulties in this field with supporting case studies.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1246-1278"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Label-efficient head-motion-robust wearable radar hand gesture recognition via motion-domain radar–IMU fusion","authors":"Dokyeong Kang,&nbsp;Seongjoo Lee","doi":"10.1016/j.icte.2026.09.001","DOIUrl":"10.1016/j.icte.2026.09.001","url":null,"abstract":"<div><div>Hand gesture recognition (HGR) is an important input modality for augmented reality, assistive interfaces, industrial hands-free control, and human–computer interaction. Although wearable image sensors provide rich appearance information, they suffer from privacy concerns, illumination changes, limited field of view, hand occlusion, and high computational burden for always-on operation. Wearable radar is an attractive non-image alternative because it directly measures range, angle, and Doppler motion cues. However, when radar is mounted on a wearable platform, head motion changes the sensor coordinate system and degrades recognition robustness. This paper studies radar–IMU fusion and label-efficient training for head-motion-robust wearable radar HGR. We compare conventional fusion strategies, including radar-only learning, early fusion, late fusion, and attention fusion, with the proposed motion-domain protocol that converts radar observations into a Doppler-aware motion-XYZ representation. Under the same TCN backbone and subject-disjoint protocol, the proposed motion-domain protocol achieved the best four-class performance among the evaluated fusion strategies, with 85.97% macro-F1 and 81.69% moving-head macro-F1. It improved moving-head macro-F1 by 3.18 percentage points over early fusion and by 5.80 percentage points over attention fusion. Label-efficiency experiments showed a larger benefit under limited non-static head labels: with 0% non-static labels, early fusion achieved only 23.46% moving-head macro-F1, whereas the proposed protocol achieved 70.30%. These results indicate that raw IMU concatenation or attention-based sensor fusion is less effective than transforming radar observations into a physically meaningful motion domain before learning.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1279-1284"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based traffic prediction with optimized channel selection and enhanced security for 6G-enabled VANETs","authors":"Mustafa Maad Hamdi,&nbsp;Sami Abduljabbar Rashid,&nbsp;Nor Samsiah Sani,&nbsp;Ahmed Adil Nafea","doi":"10.1016/j.icte.2026.09.015","DOIUrl":"10.1016/j.icte.2026.09.015","url":null,"abstract":"<div><div>The rise of intelligent communication in the VANET network among connected devices has become more important and has gathered the attention of both the industrial and academic sectors. Due to the utilization of large-scale network architecture, the network traffic and energy consumption are increased which leads to the increase of communication costs. Therefore, the aim is to reduce the delay and consumed energy along with addressing the security vulnerabilities. For that purpose, this research proposes a Deep Learning-based Driven Traffic Prediction with Load Balancing Approach for VANETs-based Intelligent Transportation Systems (DLDTPL) method that combines sophisticated routing, optimization strategies, and security measures. The routing performance of the vehicles are concentrated through Multimedia-Aware Routing Protocol, which identifies optimal nodes based on five crucial criteria: distance, vehicle density, path, bandwidth, and MAC layer losses. The Optimized Channel-Aware Relay Model (OCARM) with an Enhanced Dingo Optimizer (EDO) to enhance vehicle traffic prediction while reducing computational effort. To ensure data security, the Sixth Generation-Enhanced Security Architecture (6G-ESA) utilizes Improved Elliptic Curve Cryptography (IECC). The integration of deep learning technique with the current security modules elevates the security performance through traffic forecasting and past data analysis. Additionally, the effectiveness of DLDTPL is comparatively assisted against the benchmark methods by calculating certain parameters like delivery rate, throughput, efficiency and security which are utilized on the NS2 platform using simulation of urban mobility (SUMO) tool. According to the final obtained results which is compared with the models like MORDM, HPOHDL, and IOKITT and the proposed findings achieves lowest communication cost, maximum delivery ratio with lower end to end delay that proves the routing performance, security and efficiency of the DLDTPL is better in the 6G environment.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1303-1318"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"End-to-end optimization based on residual neural networks for improved optical fiber communication","authors":"Omar Alnaseri,&nbsp;Mohammed Alaa Ala’anzy,&nbsp;Nasir Saeed","doi":"10.1016/j.icte.2025.10.010","DOIUrl":"10.1016/j.icte.2025.10.010","url":null,"abstract":"<div><div>High-capacity optical fiber transmissions increasingly face performance limits imposed by nonlinear transmission effects. This paper examines deep learning-based enhancements in optical fiber communication through an end-to-end autoencoder (AE) framework. A residual neural network (ResNet) architecture is employed to replace traditional dense layers, which uses shortcut connections for improved learning under nonlinear channel impairments. Using the achievable information rate (AIR) as the performance metric, the ResNet-based AE demonstrates 2.6% superior compared to dense-layer AE under high nonlinearity conditions. Although dense layer AEs offer lower complexity, the ResNet achieves 3.75% Q-factor improvements, making it a compelling choice for next-generation high-speed optical systems.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1285-1290"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy efficiency optimization and DQN-based power allocation in UAV-IRS assisted 6 G system","authors":"Preksha Shah,&nbsp;Akhil Gupta,&nbsp;Nitin Rakesh,&nbsp;Sudeep Tanwar,&nbsp;Giovanni Pau,&nbsp;Ahmed Altameem,&nbsp;Torki Altameem","doi":"10.1016/j.icte.2025.10.012","DOIUrl":"10.1016/j.icte.2025.10.012","url":null,"abstract":"<div><div>With the growing number of devices, applications, and services, energy-efficient communication has become critical in next-generation wireless networks. Terahertz (THz) bands provide vast bandwidth for high data rates, but their poor penetration causes user blockage. To address this, we consider a massive MIMO (mMIMO) THz non-orthogonal multiple access (NOMA) system assisted by an IRS-mounted UAV that reflects signals toward blocked users. We formulate the UAV trajectory design as an NP-hard problem and propose a low-complexity Track Selection Scheme (TSS) that minimizes UAV energy consumption. We develop a Deep Q-Network (DQN)-based NOMA power allocation scheme to further enhance performance and formulate an energy-efficiency maximization problem. The latter is solved using a combination of the Dinkelbach method and the Concave–Convex Procedure (CCCP). Simulation results demonstrate that the proposed schemes significantly reduce UAV energy consumption and improve system sum-rate and energy efficiency compared with benchmark algorithms.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 5","pages":"Pages 1099-1106"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}