ICT Express最新文献

{"title":"Mobility-aware pilot assignment for UAV-only CF-mMIMO networks","authors":"Haider Salih Mahdi,&nbsp;Jafar Pourrostam","doi":"10.1016/j.icte.2026.07.001","DOIUrl":"10.1016/j.icte.2026.07.001","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) introduce severe pilot contamination challenges in cell-free massive multiple-input multiple-output (CF-mMIMO) networks due to high mobility and rapidly varying channel conditions. This paper proposes a mobility-aware pilot assignment framework for UAV-only CF-mMIMO systems. The proposed approach combines Kalman-filter-based trajectory prediction, mobility-aware reward design, and interference-aware greedy pilot assignment to proactively adapt pilot allocation based on predicted channel evolution. Simulation results show that the proposed scheme outperforms random and reactive pilot-assignment baselines while achieving SINR performance comparable to the Hungarian-inspired interference-aware benchmark. In particular, the proposed method improves the 5%-likely SINR by 5.91 dB and reduces serving-AP handover events.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 4","pages":"Pages 1008-1014"},"PeriodicalIF":4.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148709965","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":"A Systematic Survey of Sentiment Analysis in Low-Resource Southeast Asian Languages: Resources, Methods, and Evaluation Challenges","authors":"Le An Pha,&nbsp;Le Thi Thuy,&nbsp;Vu Nguyen Hong,&nbsp;Tuoi Thi Phan,&nbsp;Thien Khai Tran","doi":"10.1016/j.icte.2026.06.013","DOIUrl":"10.1016/j.icte.2026.06.013","url":null,"abstract":"<div><div>Sentiment analysis in low-resource Southeast Asian languages has attracted increasing attention because many languages still lack annotated corpora, linguistic tools, and standardized evaluation benchmarks. In this survey, we focus on the main dimensions of low-resource sentiment analysis, including available resources, major methodological families, comparative evaluation, and practical deployment challenges across Vietnamese, Thai, Indonesian, Malay, Khmer, Lao, and Burmese/Myanmar. We review lexicon-based, classical machine learning, neural, transformer-based, cross-lingual, and recent large language model approaches. We also examine how language characteristics, domain variation, and supervision settings affect model effectiveness, and discuss open issues such as benchmark fragmentation, reproducibility, and cross-domain robustness. The central critical insight is that low-resource sentiment analysis in Southeast Asia should be judged not only by model performance, but also by resource ecology, evaluation comparability, and deployment realism.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 4","pages":"Pages 933-956"},"PeriodicalIF":4.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710017","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":"Dueling Double DQN-based directional MAC protocol for THz-band responsive spaces","authors":"Jun Young Park,&nbsp;Woong Soo Na","doi":"10.1016/j.icte.2026.05.003","DOIUrl":"10.1016/j.icte.2026.05.003","url":null,"abstract":"<div><div>Immersive services such as virtual reality (VR) and holography demand multi-gigabit throughput and ultra-low latency. To meet these demands, THz-band directional communication is essential but vulnerable to LoS blockages in dynamic responsive spaces. To address this issue, we propose a Responsive-Space Directional MAC (RS-DMAC) based on Dueling Double DQN. RS-DMAC jointly learns the selection of beams and relay nodes using an observation-driven RL formulation based on communication-level measurements. Simulation results demonstrate that RS-DMAC achieves higher throughput, lower latency, and improved link reliability compared to conventional schemes for immersive service requirements, while maintaining a reasonable energy-efficiency trade-off.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 4","pages":"Pages 862-867"},"PeriodicalIF":4.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710175","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":"Toward green 6G RAN: A survey of energy-efficient mechanisms and 3GPP standardization efforts","authors":"John Shaibu,&nbsp;Hanol Oh,&nbsp;Minjae Seo,&nbsp;Hayeong Han,&nbsp;Gosan Noh,&nbsp;Taehoon Kim,&nbsp;Inkyu Bang","doi":"10.1016/j.icte.2026.06.015","DOIUrl":"10.1016/j.icte.2026.06.015","url":null,"abstract":"<div><div>Energy efficiency has emerged as a critical design objective for sixth-generation (6G) radio access networks (RAN) due to their significant environmental and economic impact. According to multiple industry and academic projections, 6G networks could consume two to three times more energy than fifth-generation (5G) networks, particularly since the RAN accounts for approximately 73% of total network energy consumption (within a commonly reported range of 70%–80%). This survey provides a comprehensive overview of energy-efficient mechanisms for 6G RAN across four key optimization domains: time-domain techniques (e.g., discontinuous reception/transmission and adaptive synchronization signaling), frequency-domain methods (e.g., bandwidth part operation and carrier activation), spatial-domain approaches (e.g., dynamic antenna adaptation and beamforming), and power-domain optimizations (e.g., power amplifier efficiency and dynamic power control). Beyond conventional mechanisms, the survey reviews emerging artificial intelligence (AI)/machine learning (ML)-driven optimization approaches, including traffic prediction, reinforcement learning, and federated learning frameworks, highlighting how 6G networks can achieve AI-native energy awareness from the design stage. As third generation partnership project (3GPP) standardization evolves from Release 15 through Release 21, with particular focus on the energy-native directions of Releases 19–21, energy-saving technologies are increasingly aligned with quantitative and measurable energy-efficiency requirements for 6G systems. Additionally, the survey examines several emerging enabling technologies expected to influence the energy efficiency of future networks, including reconfigurable intelligent surfaces (RIS), terahertz communications, and holographic multiple-input and multiple-output (MIMO). It also identifies several critical standardization challenges, such as AI/ML framework interoperability, the lack of consensus on energy measurement methodologies, and ongoing debates regarding unified energy-efficiency metrics. Our findings suggest that achieving the targeted 50% reduction in overall energy consumption for 6G will require coordinated advancements across multiple dimensions, including hardware efficiency, architectural design, and intelligent control mechanisms. These developments are expected to align with the 3GPP Release 21 specifications anticipated for 2027–2028.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 4","pages":"Pages 985-1007"},"PeriodicalIF":4.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148709964","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 for UAV-assisted uplink transmissions over probabilistic LoS channels","authors":"Lili Guo,&nbsp;Shibing Zhang,&nbsp;Cheng He,&nbsp;Xiaodong Ji","doi":"10.1016/j.icte.2025.09.007","DOIUrl":"10.1016/j.icte.2025.09.007","url":null,"abstract":"<div><div>This article studies a ground-to-air uplink transmission, where a fixed-wing unmanned aerial vehicle (UAV) is deployed to gather data from multiple ground users (GUs) over probabilistic line-of-sight channels. With the aim of achieving the maximal system energy efficiency, a joint optimization problem consisting of three variable blocks, i.e., time-slot scheduling, GUs’ transmit power control and the UAV’s trajectory design are studied. By utilizing the block coordinate descent technique, a joint optimization algorithm addressing the three variable blocks jointly is developed. Simulation results confirmed the effectiveness and efficiency of the proposed algorithm.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 4","pages":"Pages 1062-1067"},"PeriodicalIF":4.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148709969","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":"A comprehensive survey on facial expression generation: From GANs to LLM-guided multimodal models","authors":"Murad Hasan,&nbsp;Rabab Abdelfattah,&nbsp;Kareem Abdelfatah,&nbsp;Mostafa Fouda,&nbsp;Ahmed Sherif","doi":"10.1016/j.icte.2026.05.007","DOIUrl":"10.1016/j.icte.2026.05.007","url":null,"abstract":"<div><div>Facial expression generation (FEG) has emerged as a vital area in human–computer interaction, virtual avatars, and affective computing, aiming to synthesize natural and expressive facial behaviors across diverse interaction contexts. This survey presents a comprehensive analysis of recent advances in FEG, organized into six key paradigms: speech-driven expression generation, facial reaction generation, face video generation, facial animation, avatar-based generation, and text-driven expression generation. We review a wide range of model architectures, including VQ-VAEs, Generative Adversarial Networks (GANs), 3D Morphable Models (3DMMs), Transformers, and diffusion-based approaches, and compare their performance using commonly adopted evaluation metrics such as Fréchet Distance (FD), Peak Signal-to-Noise Ratio (PSNR), and expression realism measures. We further examine the emerging integration of Large Language Models (LLMs) into FEG pipelines. While recent studies suggest that LLMs can introduce high-level semantic reasoning and contextual awareness into facial expression generation, current approaches remain limited by the lack of standardized evaluation protocols, reliance on intermediate representations (e.g., motion tokens), and insufficient large-scale empirical validation. In addition, we provide a structured comparison of widely used datasets, highlighting variations in modality, annotation strategies, and interaction settings. Based on this analysis, we identify critical challenges in the field, including inconsistencies in evaluation metrics, limited personalization capabilities, and unresolved issues in cross-modal semantic alignment. This survey aims to serve as both a technical reference and a forward-looking roadmap for developing more robust, interpretable, and context-aware facial expression generation systems.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 4","pages":"Pages 810-824"},"PeriodicalIF":4.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710171","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":"Adversarial-aware multi-agent reinforcement learning for secure resource allocation in Heterogeneous Aerial Access IoT networks","authors":"Umar Sa’ad,&nbsp;Woongsoo Na,&nbsp;Nhu-Ngoc Dao,&nbsp;Sungrae Cho","doi":"10.1016/j.icte.2026.06.012","DOIUrl":"10.1016/j.icte.2026.06.012","url":null,"abstract":"<div><div>This paper proposes an Adversarial-Aware Multi-Agent Deep Deterministic Policy Gradient (AA-MADDPG) framework integrating adversarial risk analysis with deep reinforcement learning for robust resource allocation and offloading in heterogeneous aerial access networks. We model adversarial behavior using three rationality paradigms, including Nash equilibrium, level-k thinking, and prospect maximizing, employing Bayesian model averaging for robust adversarial action prediction. The framework enables collaborative decision-making among aerial access tiers and IoT devices while maintaining attack resilience. Simulations demonstrate that AA-MADDPG reduces task drop rates by 27% and energy consumption by 19% compared to baselines.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 4","pages":"Pages 957-962"},"PeriodicalIF":4.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710018","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":"A survey of OAM-encoded high-dimensional quantum key distribution: Foundations, experiments, and recent trends","authors":"Huan Zhang,&nbsp;Zhenyu Cao,&nbsp;Yu Sun,&nbsp;Hu Jin","doi":"10.1016/j.icte.2026.05.005","DOIUrl":"10.1016/j.icte.2026.05.005","url":null,"abstract":"<div><div>High-dimensional quantum key distribution (HD-QKD) enhances information efficiency and noise tolerance by encoding data in large Hilbert spaces. The orbital angular momentum (OAM) of light provides a scalable basis for such encoding and supports high-dimensional photonic communication. Practical OAM-based implementations remain constrained by challenges in state generation, transmission, and detection. This survey offers a consolidated overview of OAM-encoded HD-QKD, outlining fundamental principles, representative experiments, and system-level limitations. Recent progress in hybrid encodings, mode sorting, adaptive optics, and TF, CV, MDI, and DI frameworks is summarized with emphasis on practical feasibility.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 4","pages":"Pages 773-788"},"PeriodicalIF":4.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710169","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":"RansomFisher: Behavior-based ransomware detection without decoys","authors":"InHoe Ku,&nbsp;Iljun Jeong,&nbsp;Seunghun Han","doi":"10.1016/j.icte.2026.06.002","DOIUrl":"10.1016/j.icte.2026.06.002","url":null,"abstract":"<div><div>Ransomware remains a significant cyber threat for legacy and recent systems. This study revisits behavior-based detection and introduces RansomFisher, a novel decoyless ransomware detection system that monitors file access patterns. RansomFisher defines four features to characterize process behaviors and distinguish ransomware from benign applications: write ratio, bulk ratio, write count ratio, and continuous write count. RansomFisher was evaluated on eight recent ransomware samples and 96 benign processes, including four applications that exhibit ransomware-like behavior and 92 system services. RansomFisher detected ransomware with an average file loss of only 15.3 files before detection and a negligible average overhead of 3.7%.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 4","pages":"Pages 894-898"},"PeriodicalIF":4.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710178","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 for license plate recognition: A comprehensive survey of datasets, methods, and future directions","authors":"Dian Ning,&nbsp;Dong Seog Han","doi":"10.1016/j.icte.2026.05.020","DOIUrl":"10.1016/j.icte.2026.05.020","url":null,"abstract":"<div><div>License plate recognition (LPR) is an important component of intelligent transportation systems. With the rapid development of deep learning, LPR has evolved from handcrafted pipelines toward data-driven detection and recognition models with substantially improved accuracy and efficiency. However, the progress reported in existing studies is often difficult to interpret clearly due to considerable differences in dataset splits, evaluation protocols, preprocessing strategies, task definitions, and hardware settings. This review examines LPR from a system-level perspective. We discuss dataset bias, limitations of benchmarks, different evaluation criteria, and the growing importance of restoration-aware LPR, including super-resolution for degraded license plate images.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"12 4","pages":"Pages 874-893"},"PeriodicalIF":4.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710177","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}