IET Communications最新文献

Energy Efficiency Maximization for a Relay Assisted Parasitic Symbiotic Radio Network 中继辅助寄生共生无线网络的能源效率最大化

IF 1.5 4区计算机科学

IET Communications Pub Date : 2025-06-16 DOI: 10.1049/cmu2.70058

Xi Song, Dongsheng Han, Liqin Shi, Yinghui Ye, Xiaoli Chu

{"title":"Energy Efficiency Maximization for a Relay Assisted Parasitic Symbiotic Radio Network","authors":"Xi Song, Dongsheng Han, Liqin Shi, Yinghui Ye, Xiaoli Chu","doi":"10.1049/cmu2.70058","DOIUrl":"https://doi.org/10.1049/cmu2.70058","url":null,"abstract":"Relay-assisted symbiotic radio (SR) has been recently proposed to overcome the blocking of the direct link from the primary transmitter (PT) or backscatter node (BN) to the destination node (DN). However, the energy efficiency (EE), which is an important performance metric for SR networks, has been largely ignored in existing studies of the relay-assisted SR. To fill the gap, this work maximizes the EE of a relay-assisted parasitic SR network, which comprises a PT, a BN, a relay node (RN), and a DN. More specifically, we formulate a mixed-integer programming optimization problem that maximizes the system EE by jointly optimizing the transmit power of the PT, the power reflection coefficient of the BN, the transmit power and the power allocation ratio at the RN as well as the successive interference cancellation (SIC) decoding order at the DN. We decompose the formulated non-convex problem into two subproblems corresponding to the two different SIC decoding orders, respectively. For each subproblem, we convert its objective function from a fractional form into a subtractive form by using a Dinkelbach-based method, and then utilize the block coordinate descent (BCD) method to further decouple it into two subsubproblems that are proved to be convex. Based on the obtained solutions, we devise an iterative algorithm to solve each subproblem by solving its two subsubproblems alternately. The optimal solution to the subproblem with a higher system EE returns a near-optimal solution to the original problem. Simulation results demonstrate the rapid convergence of the proposed algorithms and validate the significant advantages of our proposed algorithms over the baseline schemes.","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144292762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Optimising Multiplayer VR Game Experience Based on Intelligent Computing 基于智能计算的多人VR游戏体验优化

IF 1.5 4区计算机科学

IET Communications Pub Date : 2025-06-16 DOI: 10.1049/cmu2.70056

Ling Yang, Daibo Xiao

{"title":"Optimising Multiplayer VR Game Experience Based on Intelligent Computing","authors":"Ling Yang, Daibo Xiao","doi":"10.1049/cmu2.70056","DOIUrl":"https://doi.org/10.1049/cmu2.70056","url":null,"abstract":"Multiplayer virtual reality (VR) games represent the increasing future direction of VR game development. However, currently it still falls short in terms of the fluidity of social interaction, response speed, and sense of immersion, which affects players' engagement and satisfaction. This paper proposes an optimisation design for multiplayer VR game systems that integrates BP neural networks based on an intelligent computing framework. By applying the fast convergence of traditional BP algorithms and enhancing it with genetic algorithms to improve global search capabilities and avoid local optima, ensuring more accurate and efficient neural network training for enhanced VR gaming experiences. This paper compares the performance of traditional neural networks and evolutionary neural networks through extensive simulation and testifies to the engagement experience of players through quantitative analysis. The results show that evolutionary neural networks outperform traditional neural networks in system performance, such as severe latency and technical lagging. The paper also finds that technological preparedness significantly affects behaviour engagement through embodied social presence, emotional and cognitive engagement. Based on these findings, this paper suggests strategies to optimise the user experience of multiplayer VR games by improving game technology quality, enriching content, maintaining continuous communication with players, and establishing reasonable incentive mechanisms.","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144292846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

On Age of Information and Energy-Transfer in a STAR-RIS-Assisted System star - ris辅助系统的信息时代与能量传递

IF 1.5 4区计算机科学

IET Communications Pub Date : 2025-06-12 DOI: 10.1049/cmu2.70054

Mohammad Reza Kavianinia, Mohammad Mehdi Setoode, Mohammad Javad Emadi

{"title":"On Age of Information and Energy-Transfer in a STAR-RIS-Assisted System","authors":"Mohammad Reza Kavianinia, Mohammad Mehdi Setoode, Mohammad Javad Emadi","doi":"10.1049/cmu2.70054","DOIUrl":"https://doi.org/10.1049/cmu2.70054","url":null,"abstract":"Battery-limited devices and time-sensitive applications are considered as key players in forthcoming wireless sensor network. So, the main goal of the network is two-fold; charge battery-limited devices, and provide status updates to users where information-freshness matters. In this paper, a multi-antenna base station (BS) in assistance of simultaneously-transmitting-and-reflecting reconfigurable intelligent surface (STAR-RIS) transmits power to energy harvesting (EH) devices while controlling status update performance at information users by analysing age of information (AoI) metric. Therefore, we derive a scheduling policy at BS, and analyse joint transmit beamforming and amplitude-phase optimization at BS and STAR-RIS, respectively, to reduce average sum-AoI for the time-sensitive information users while satisfying minimum required energy at EH users. Moreover, two different energy-splitting and mode-switching policies at STAR-RIS are studied. Then, by use of an alternating optimization algorithm, the optimization problem is studied and non-convexity of the problem is tackled by using the successive convex approximation technique. Through numerical results, AoI-metric and EH requirements of the network are analysed versus different parameters such as number of antennas at BS, size of STAR-RIS, and transmitted power to highlight how we can improve two-fold performance of the system by utilizing STAR-RIS compared to the conventional RIS structure.","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Channel Estimation for Indoor Terahertz UM-MIMO: A Deep Learning Perspective for 6G Applications 室内太赫兹UM-MIMO的信道估计：6G应用的深度学习视角

IF 1.5 4区计算机科学

IET Communications Pub Date : 2025-06-03 DOI: 10.1049/cmu2.70053

Sakhshra Monga, Gunjan Garg, Nitin Saluja, Olutayo Oyeyemi Oyerinde

{"title":"Channel Estimation for Indoor Terahertz UM-MIMO: A Deep Learning Perspective for 6G Applications","authors":"Sakhshra Monga, Gunjan Garg, Nitin Saluja, Olutayo Oyeyemi Oyerinde","doi":"10.1049/cmu2.70053","DOIUrl":"https://doi.org/10.1049/cmu2.70053","url":null,"abstract":"The emergence of terahertz (THz) communication in ultra-massive multiple-input multiple-output (UM-MIMO) systems presents new challenges for accurate and efficient channel estimation, particularly under hybrid-field propagation conditions. Conventional estimation techniques struggle to meet the demands of such high-dimensional systems, especially in the presence of limited radio frequency (RF) chains and mixed near- and far-field effects. To address these limitations, this paper proposes a deep learning-based framework that combines a fully connected neural network (FCNN) for linear channel estimation with a convolutional neural network (CNN) for non-linear refinement. The architecture is designed to adapt to diverse propagation environments while maintaining computational efficiency. Simulation studies based on realistic THz scenarios demonstrate that the proposed approach significantly improves estimation accuracy, achieving up to 90% reduction in normalized mean squared error (NMSE) compared to traditional and advanced estimation techniques. The robustness of the model under varying signal-to-noise ratios and noise power levels underscores its potential for deployment in future 6G THz communication networks.","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

AMCF-Net: A Novel Adaptive Multi-Channel Fusion Network for Computer-Aided Diagnosis of Lung Nodules in Chest Computed Tomography AMCF-Net：一种用于胸部ct肺结节计算机辅助诊断的新型自适应多通道融合网络

IF 1.5 4区计算机科学

IET Communications Pub Date : 2025-06-02 DOI: 10.1049/cmu2.70052

Nan Wang, Yu Gu, Lidong Yang, Baohua Zhang, Jing Wang, Xiaoqi Lu, Jianjun Li, Dahua Yu, Ying Zhao, Xin Liu, Siyuan Tang, Qun He

{"title":"AMCF-Net: A Novel Adaptive Multi-Channel Fusion Network for Computer-Aided Diagnosis of Lung Nodules in Chest Computed Tomography","authors":"Nan Wang, Yu Gu, Lidong Yang, Baohua Zhang, Jing Wang, Xiaoqi Lu, Jianjun Li, Dahua Yu, Ying Zhao, Xin Liu, Siyuan Tang, Qun He","doi":"10.1049/cmu2.70052","DOIUrl":"https://doi.org/10.1049/cmu2.70052","url":null,"abstract":"Malignant lung nodules can significantly affect patients' normal lives and, in severe cases, threaten their survival. Owing to the heterogeneity of computed tomography scans and the varying sizes of nodules, physicians often face challenges in diagnosing this condition. Therefore, a novel adaptive multi-channel fusion network (AMCF-Net) is proposed for computer-aided diagnosis of lung nodules. First, a Multi-Channel Fusion Model module is designed, which divides the channels into two parts in specific proportions, effectively extracting multi-scale channel information while reducing network parameters. After the feature maps output at each layer of the AMCF-Net, a novel adaptive depth-wise separable convolution with a squeeze-and-excitation module is designed to adaptively integrate the feature maps of various stages of the AMCF-Net, ensuring that the key lesions of lung nodules are not lost during classification. Finally, a hybrid loss scheme based on an adaptive mixing ratio is proposed to solve the problem of an imbalanced number of positive and negative nodule samples in the dataset. The model achieved the following test results: an accuracy of 90.22%, a specificity of 98.19%, an F1-score of 86.57%, a sensitivity of 86.49%, and a G-mean of 87.72%. Compared with other advanced networks, AMCF-net delivers high-precision lung nodule classification with minimal inference cost. Related codes have been released at: https://github.com/GuYuIMUST/AMCF-net.","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Cloud-Fog Cooperative Computation Offloading and Resource Allocation in Heterogeneous Networks Based on Genetic Algorithm 基于遗传算法的异构网络云雾协同计算卸载与资源分配

IF 1.5 4区计算机科学

IET Communications Pub Date : 2025-06-02 DOI: 10.1049/cmu2.70051

Qiang Wang, Chenming Zhu, Su Pan, Min Zhong, Zibo Li

{"title":"Cloud-Fog Cooperative Computation Offloading and Resource Allocation in Heterogeneous Networks Based on Genetic Algorithm","authors":"Qiang Wang, Chenming Zhu, Su Pan, Min Zhong, Zibo Li","doi":"10.1049/cmu2.70051","DOIUrl":"https://doi.org/10.1049/cmu2.70051","url":null,"abstract":"In this paper, we investigate the computation offloading and resource allocation strategy of the coexistence and synergy between fog computing and cloud computing in heterogeneous networks. Consider that the reported schemes have prohibitive complexity when achieving the optimal computation offloading strategy in cloud-fog cooperative heterogeneous networks, an improved genetic algorithm (IGA) is proposed in this paper, which can maintain a low computation complexity while obtaining the optimal solution. In the IGA algorithm, we propose to use a penalty function to express the constraint conditions of the optimisation problem and use a non-uniform mutation operator to accelerate the convergence speed. Besides, an improved method of parameter self-adaptation and a perturbation method of mutation probability based on population fitness standard deviation are proposed to optimise the genetic algorithm. The numerical results show that the proposed genetic algorithm can obtain a lower average cost of the system while keeping a smaller computational cost.","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Keywords Extraction Technology for Few-Shot Learning in Customer Service 客户服务中少镜头学习的提取技术

IF 1.5 4区计算机科学

IET Communications Pub Date : 2025-05-22 DOI: 10.1049/cmu2.70049

Xiaol Ma, Bangxing Yang, Dequan Du, RuQiang Zhao, Congjian Deng

引用次数: 0

Relay-Assisted Communications Over Multi-Cluster Fluctuating Two-Ray Faded Channels 多簇波动双射线衰落信道中继辅助通信

IF 1.5 4区计算机科学

IET Communications Pub Date : 2025-05-20 DOI: 10.1049/cmu2.70050

Haider Mehdi, Zakir Hussain, Syed Muhammad Atif Saleem, Syed Areeb Ahmed

引用次数: 0

A DRL-Based Algorithm for DNN Partition, Subtask Offloading and Resource Allocation in Multi-Hop Computing Nodes with Cloud 基于drl的多跳云计算节点DNN分区、子任务卸载和资源分配算法

IF 1.5 4区计算机科学

IET Communications Pub Date : 2025-05-20 DOI: 10.1049/cmu2.70048

Ruiyu Yang, Zhili Wang, Yang Yang, Sining Wang

{"title":"A DRL-Based Algorithm for DNN Partition, Subtask Offloading and Resource Allocation in Multi-Hop Computing Nodes with Cloud","authors":"Ruiyu Yang, Zhili Wang, Yang Yang, Sining Wang","doi":"10.1049/cmu2.70048","DOIUrl":"https://doi.org/10.1049/cmu2.70048","url":null,"abstract":"Nowadays, deep neural network (DNN) partition is an effective strategy to accelerate deep learning (DL) tasks. A pioneering technology, computing and network convergence (CNC), integrates dispersed computing resources and bandwidth via the network control plane to utilize them efficiently. This paper presents a novel network-cloud (NC) architecture designed for DL task inference in CNC scenario, where network devices directly participate in computation, thereby reducing extra transmission costs. Considering multi-hop computing-capable network nodes and one cloud node in a chain path, leveraging deep reinforcement learning (DRL), we develop a joint-optimization algorithm for DNN partition, subtask offloading and computing resource allocation based on deep Q network (DQN), referred to as POADQ, which invokes a subtask offloading and computing resource allocation (SORA) algorithm with low complexity, to minimize delay. DQN searches the optimal DNN partition point, and SORA identifies the next optimal offloading node for next subtask through our proposed NONPRA (next optimal node prediction with resource allocation) method, which selects the node that exhibits the smallest predicted increase in cost. We conduct some experiments and compare POADQ with other schemes. The results show that our proposed algorithm is superior to other algorithms in reducing the average delay of subtasks.","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

An Efficient Neural Network Algorithm for Physical Layer Spoofing Attack Detection 一种高效的物理层欺骗攻击检测神经网络算法

IF 1.5 4区计算机科学

IET Communications Pub Date : 2025-05-15 DOI: 10.1049/cmu2.70043

Min Zhang, JinTao Cai

引用次数: 0