ICT Express最新文献_第3页

Enhancing network function parallelism in mobile edge computing using Deep Reinforcement Learning 利用深度强化学习增强移动边缘计算中的网络功能并行性

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-02-01 DOI: 10.1016/j.icte.2024.09.011

DongYu Lu , Shirong Long

引用次数: 0

RIS-aided free-space optics communications in A2G networks over inverted Gamma–Gamma turbulent channels 反向伽玛-伽玛湍流信道上的A2G网络中的ris辅助自由空间光学通信

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-02-01 DOI: 10.1016/j.icte.2024.09.016

Md. Abdur Rakib , Md. Ibrahim , A.S.M. Badrudduza , Imran Shafique Ansari , Md. Shahid Uz Zaman , Heejung Yu

引用次数: 0

Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction 用于提高能量预测精度的多元双向栅极循环单元

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-02-01 DOI: 10.1016/j.icte.2024.10.002

Quota Alief Sias, Rahma Gantassi, Yonghoon Choi

{"title":"Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction","authors":"Quota Alief Sias, Rahma Gantassi, Yonghoon Choi","doi":"10.1016/j.icte.2024.10.002","DOIUrl":"10.1016/j.icte.2024.10.002","url":null,"abstract":"<div><div>Energy prediction is an important process in energy management, especially regarding demand response. Energy predictions are often carried out for load forecasting or energy generation forecasting of renewable energy. This paper explains the implementation of multi-variables in the development of recurrence neural network models to predict load energy and generation energy. The proposed main model is a multi-variate bidirectional GRU combined with a periodic feature pattern. The proposed model will also be compared with the fundamental bidirectional models of the GRU and LSTM models. For load prediction, the variables used are all energy supply data and periodic features. Meanwhile, for photovoltaic generation energy predictions, additional weather data is used because energy generation is very dependent on solar radiation and ambient conditions. Load prediction data is built using daily and hourly energy prediction data. Meanwhile, solar energy prediction is constructed with data every minute. The results show that the proposed model obtains the best prediction results for all test data on a daily, hourly, or minute basis. The model also shows the fastest execution time performance compared to other models.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 1","pages":"Pages 80-86"},"PeriodicalIF":4.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421207","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}

引用次数: 0

Fast and fair split computing for accelerating deep neural network (DNN) inference 快速和公平的分割计算加速深度神经网络（DNN）推理

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-02-01 DOI: 10.1016/j.icte.2024.09.013

Dongju Cha , Jaewook Lee , Daeyoung Jung, Sangheon Pack

引用次数: 0

Learning to route and schedule links in reconfigurable networks 在可重构网络中学习路由和调度链路

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-02-01 DOI: 10.1016/j.icte.2024.07.001

Xiangdong Yi, Kwan-Wu Chin

引用次数: 0

A filter-and-refine approach to lightweight application traffic classification 轻量级应用流量分类的过滤与提炼方法

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-02-01 DOI: 10.1016/j.icte.2024.06.003

Ui-Jun Baek , Jee-Tae Park , Yoon-Seong Jang , Ju-Sung Kim , Yang-Seo Choi , Myung-Sup Kim

引用次数: 0

Fusion of self-attention mechanism for CSI feedback in massive MIMO systems 大规模MIMO系统中CSI反馈自注意机制的融合

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-02-01 DOI: 10.1016/j.icte.2024.10.010

Tingting Zhang , Youyun Xu , Changpeng Zhou

{"title":"Fusion of self-attention mechanism for CSI feedback in massive MIMO systems","authors":"Tingting Zhang , Youyun Xu , Changpeng Zhou","doi":"10.1016/j.icte.2024.10.010","DOIUrl":"10.1016/j.icte.2024.10.010","url":null,"abstract":"<div><div>In massive MIMO systems, obtaining accurate channel state information (CSI) is crucial for optimal channel coding and beamforming. However, traditional CSI feedback methods require high bandwidth and also consume a large amount of power and computing resources. To address these challenges, several compressed sensing-based techniques have been implemented in recent years. These techniques, however, are often iterative and computationally complex to implement in power-constrained user equipment. In this paper, we propose a novel fusion of the self-attention mechanism, called <em>FSAMNet</em>, to efficiently and accurately implement the CSI feedback task for massive MIMO systems. Our proposed FSAMNet adopts both the residual connections in the attention mechanism and a sequence of depth-separable convolutional layers to enhance the model’s performance and expressive ability. Specifically, we apply a multi-layer self-attention mechanism in the encoder part to achieve feature extraction and compression. In the decoder part, we use multiple convolutional layers and self-attention mechanisms to convert the embedding vector generated by the encoder back into the original image. Experimental results show that the performance of our proposed FSAMNet outperforms conventional benchmark schemes in terms of feedback network performance.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 1","pages":"Pages 124-128"},"PeriodicalIF":4.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421281","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}

引用次数: 0

Enhancing transparency and trust in AI-powered manufacturing: A survey of explainable AI (XAI) applications in smart manufacturing in the era of industry 4.0/5.0 提高人工智能制造的透明度和信任：工业4.0/5.0时代智能制造中可解释的人工智能（XAI）应用调查

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-02-01 DOI: 10.1016/j.icte.2024.12.001

Konstantinos Nikiforidis , Alkiviadis Kyrtsoglou , Thanasis Vafeiadis , Thanasis Kotsiopoulos , Alexandros Nizamis , Dimosthenis Ioannidis , Konstantinos Votis , Dimitrios Tzovaras , Panagiotis Sarigiannidis

引用次数: 0

Handover strategy for LEO satellite communication using graph neural network 基于图神经网络的低轨道卫星通信切换策略

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-02-01 DOI: 10.1016/j.icte.2025.01.009

Ji-Woon Lee , Byungju Lim , Ki-Hun Kim , Jong-Man Lee , Young-Seok Ha , Young-Jin Han , Young-Chai Ko

{"title":"Handover strategy for LEO satellite communication using graph neural network","authors":"Ji-Woon Lee , Byungju Lim , Ki-Hun Kim , Jong-Man Lee , Young-Seok Ha , Young-Jin Han , Young-Chai Ko","doi":"10.1016/j.icte.2025.01.009","DOIUrl":"10.1016/j.icte.2025.01.009","url":null,"abstract":"<div><div>Distributed handover (HO) strategy with low complexity can provide seamless communication in low earth orbit (LEO) satellite networks. However, it is difficult to consider load balancing in distributed HO strategy, which may results in HO failures. In this paper, we propose a graph neural network (GNN) based distributed HO strategy for LEO satellite communication to maximize sum rate by considering load balancing. We first propose target satellite selection method with GNN where each user equipment (UE) selects target satellite and requests HO to it. We then employ ACK decision policy to strictly satisfy load balancing of satellites where each satellite decides HO requests from UEs depending on its load condition. To validate the proposed GNN based HO, we use the System Tool Kit (STK) for modeling LEO satellites with 22 orbits and 72 satellites are in each orbit, and evaluate the HO process during 2400 s. From this constellation, we generate 9,600 samples by randomly deploying UEs on the ground and use them as dataset. Simulation results show that the proposed GNN based HO strategy outperforms conventional HO strategies by selecting an appropriate target satellite. We also demonstrate that load balancing is satisfied due to ACK decision policy and the scalability of proposed GNN architecture is ensured with different network sizes.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 2","pages":"Pages 239-244"},"PeriodicalIF":4.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704776","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}

引用次数: 0

Recurrent DQN for radio fingerprinting with constrained measurements collection 具有约束测量集的无线电指纹识别的循环DQN

IF 4.1 3区计算机科学

ICT Express Pub Date : 2025-02-01 DOI: 10.1016/j.icte.2024.08.001

Nicola Novello, Andrea M. Tonello

引用次数: 0