IET Communications最新文献

{"title":"Joint Weak Signal Detection and Carrier Frequency Offset Estimation for Communication in Multistatic Collaborative Passive Radar","authors":"Xiaomao Cao,&nbsp;Hong Ma,&nbsp;Hua Zhang,&nbsp;Jiang Jin","doi":"10.1049/cmu2.70100","DOIUrl":"https://doi.org/10.1049/cmu2.70100","url":null,"abstract":"<p>Communication among stations of a multitstatic collaborative passive radar (MCPR) is the prerequisite for networking detection. To tackle the problems of high missed detection probability and poor carrier frequency synchronization in inter-station communication of an MCPR under a low signal-to-noise ratio (SNR), we propose a virtual array-based method to jointly detect communication signals and estimate their starting position and carrier frequency offset (CFO) at the receiving end. It takes advantage of the a priori information of the training sequence to construct SNR-improved virtual sampled signals. On this basis, a large quantity of virtual array snapshots is constructed from the short training sequence by using the method of combinatorics, which benefits us to use the array signal processing theory in communications and reduces the signal processing cost by sharing the same hardware module with the radar signal processing unit. Moreover, to reduce the computational burden, we introduce the root multiple signal classification (root-MUSIC) algorithm to handle the virtual array snapshots. Numerical analyses conducted on the minimum shift keying (MSK) signals validate the feasibility and effectiveness of the proposed method under low SNR.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317221","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}

{"title":"Performance Enhancement of Indoor VLC Systems Using DPSS-Based DCO-GFDM Modulation","authors":"Amin Emami,&nbsp;Gholamreza Baghersalimi,&nbsp;Hossein Goorani","doi":"10.1049/cmu2.70101","DOIUrl":"https://doi.org/10.1049/cmu2.70101","url":null,"abstract":"<p>Visible light communication (VLC) is a promising solution for future wireless communication systems due to its high data rate, wide bandwidth, and enhanced security features. However, challenges such as high peak-to-average power ratio (PAPR) and out-of-band (OOB) spectral leakage limit its performance. In this study, we propose the integration of discrete prolate spheroidal sequences (DPSS) with direct current optical generalised frequency division multiplexing (DCO-GFDM) to enhance the performance of indoor VLC systems. A comparative analysis between traditional DCO-OFDM and the proposed DCO-GFDM scheme is conducted under both line-of-sight (LOS) and non-line-of-sight (NLOS) channel conditions. Simulation results show that the proposed method achieves approximately 2.5 dB reduction in PAPR and 45% reduction in OOB leakage compared to conventional DCO-OFDM, while maintaining a similar bit error rate (BER) performance. Moreover, the DCO-GFDM scheme demonstrates higher spectral efficiency without significant degradation in BER, achieving a BER below 10⁻³ at a signal-to-noise ratio (SNR) of 20 dB in both LOS and NLOS scenarios. These improvements underline the effectiveness of the DPSS-based approach in enhancing the reliability and spectral efficiency of indoor VLC systems.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317222","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}

{"title":"WH-XGBoosting: A Multi-Stage Intrusion Detection Framework for Securing Communication in Electric Vehicle Smart Grid Networks","authors":"Venkatasamy Thiruppathy Kesavan,&nbsp;Gopi Ramasamy,&nbsp;Md. Jakir Hossen,&nbsp;Emerson Raja Joseph","doi":"10.1049/cmu2.70097","DOIUrl":"https://doi.org/10.1049/cmu2.70097","url":null,"abstract":"<p>Electric vehicles (EVs) are mostly linked with the smart grids that cause diverse cyberattacks such as denial of services (DoS), data manipulations and network intrusions, which affect the grid ecosystem's reliability, efficiency and security. The multi-stage intrusion detection framework is created to explore the various resources, power consumption metrics, and network traffic to identify and mitigate cyberattacks. The adoption of EVs in grid systems creates dynamic security issues and complexity while exchanging information. The research difficulties are addressed by developing the whale-optimised XGBoosting machine learning (WH-XGBoosting), which can identify and mitigate the threats by attaining scalability and low latency. The framework uses diverse features and segmentation procedures to reduce redundancy and overfitting issues. In addition, the whale optimisation process selects optimised values and hyperparameters that improve the detection rate. Then, a boosting algorithm is applied to classify the incoming data, with a minimum false positive rate and maximum detection rate. The framework uses the whale optimisation process to select the optimized features and classifier hyperparameter updating process that enhance the overall intrusion detection accuracy. The discussed system collects the input from CICEVSE2024 and processes it using high-level feature analysis, which helps predict the intruder with a maximum recognition rate (99.12%) compared to existing methods. The system ensures robust, reliable, and scalable solutions for various cyber threats in grid ecosystems.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317235","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}

{"title":"CDBi-LSTM: A Hybrid Deep Learning Model With Attention-Based Fusion for Efficient DDoS Detection in IoT Environments","authors":"Anthony Jacklingo Kwame Quansah Junior,&nbsp;Eric Tutu Tchao,&nbsp;Eliel Keelson,&nbsp;Andrew Selasi Agbemenu,&nbsp;Henry Nunoo-Mensah,&nbsp;Bright Yeboah-Akowuah","doi":"10.1049/cmu2.70094","DOIUrl":"https://doi.org/10.1049/cmu2.70094","url":null,"abstract":"<p>We present composite deep bidirectional long short-term memory (CDBi-LSTM), a compact flow-level detector for Internet of Things (IoT) distributed denial of service (DDoS) attacks that couples a <i>CNN</i> stream and a <i>BiLSTM</i> stream, equips <i>each stream</i> with <i>self–attention</i> and <i>residual connections</i>, and combines them via <i>attention-based fusion</i>. To reflect heterogeneous deployments while avoiding dataset bias, we train and evaluate <i>separately</i> on three public benchmarks: CICDDoS2019, NF-BoT-IoT-v3, and NF-ToN-IoT-v3, under a consistent methodology. The model attains excellent performance: 99.95% accuracy on CICDDoS2019 (binary) and 99.85% (7-class), 99.99% on NF-BoT-IoT-v3, and 99.85% on NF-ToN-IoT-v3, with very low false positives/negatives confirmed by confusion matrices. Loss curves show fast and stable convergence. A complexity analysis demonstrates edge viability: MB–scale footprint (<span></span><math>\u0000 <semantics>\u0000 <mo>≈</mo>\u0000 <annotation>$approx$</annotation>\u0000 </semantics></math>1.38–1.52 MB; 361k–398k parameters), tiny RAM deltas at load (1.70–1.98 MB), and CPU latency in the tens of milliseconds (61.9–69.0 ms). An ablation study isolates the contributions of per-stream self-attention, per-stream residuals, and gated fusion, revealing favourable accuracy-efficiency trade-offs relative to simpler variants. On CICDDoS2019, the method is competitive with or surpasses the state of the art while providing concrete runtime and memory guarantees. Together, these results indicate that CDBi-LSTM is both accurate and deployment-ready for real-time IoT defence, with a clear path to further optimisation and cross-hardware validation.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317024","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}

{"title":"Deep Reinforcement Learning for Interference Alignment and Power Allocation in Wireless Avionics Intra-Communications","authors":"Yuedong Zhuo,&nbsp;Qiao Li,&nbsp;Guangshan Lu,&nbsp;Feng He","doi":"10.1049/cmu2.70087","DOIUrl":"https://doi.org/10.1049/cmu2.70087","url":null,"abstract":"<p>Wireless avionics intra-communications (WAIC) technologies play an important role in the real-time transmission between airborne equipment. As the number of deployed WAIC nodes increases, severe inter-user and inter-cabin interference occurs, degrading system performance and fairness. In this paper, a deep reinforcement learning-based interference alignment and power allocation (DRL-IAPA) scheme is proposed for multi-user WAIC system. By integrating interference alignment with a deep deterministic policy gradient (DDPG) algorithm, the DRL-IAPA scheme can mitigate interference, optimize power allocation, and ensure fairness among users under stringent latency and power constraints. Simulation results show that DRL-IAPA improves spectral efficiency and fairness over traditional and heuristic-based methods, and demonstrates scalability and consistent performance across various network configurations. Compared with other representative DRL algorithms, such as proximal policy optimization and soft actor–critic, our proposed DDPG-based approach exhibits faster convergence, higher achievable reward, and better applicability in dynamic WAIC environments.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317023","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}

{"title":"Access Point Selection and Localization for Cluster-Based Realization of a Device-to-Device Cell-Free 6G Communications Network","authors":"Iakovos Ioannou,&nbsp;Marios Raspopoulos,&nbsp;Prabagarane Nagaradjane,&nbsp;Christophoros Christophorou,&nbsp;Andreas Gregoriades,&nbsp;Vasos Vassiliou","doi":"10.1049/cmu2.70096","DOIUrl":"https://doi.org/10.1049/cmu2.70096","url":null,"abstract":"<p>The increasing demand for ultra-reliable, low-latency, and high-throughput connectivity in dense urban environments presents significant challenges for next-generation 6G networks. Traditional cellular networks, with their fixed cell boundaries and centralized base station control, are inadequate to meet the dynamic needs of such environments. A promising solution is the cell-free network architecture, where a distributed set of access points (APs) jointly serve users without fixed cell boundaries. However, efficient access point selection and accurate user localization are crucial to achieving high performance in such networks. This paper presents a decentralized approach using Belief-Desire-Intention eXtended (BDIx) agents for dynamic AP selection and localization within a cluster-based cell-free 6G communications network. Various clustering algorithms (K-means, DBSCAN, self-organizing maps, MeanShift, ClusterGAN, and Autoencoders) are evaluated for their ability to optimize network throughput, energy efficiency, and spectral utilization. A hybrid localization framework, such as centroid-based, differential circles, and multilateration methods, is employed to achieve accurate user positioning. The results demonstrate that machine learning-based clustering methods, notably Gaussian mixture model (GMM), self-organizing map (SOM), and ClusterGAN, offer significant improvements in throughput (up to 46.3%) and power reduction (up to 32.8%) over traditional methods. Regarding localization, deep learning models such as MLP, CNN, and TCN outperform deterministic methods, achieving sub-meter accuracy with minimal errors (MeanDist &lt; 1 m, <span></span><math>\u0000 <semantics>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 <annotation>$R^2$</annotation>\u0000 </semantics></math> &gt; 0.999). Overall, the proposed solution enhances system scalability, energy efficiency, and positioning accuracy, establishing a promising foundation for future 6G networks. In our reference implementation, we instantiate the pipeline with a GMM for AP/UE clustering and a multilayer perceptron (MLP) regressor for localization.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316877","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}

{"title":"Performance Analysis of Multipath Adaptive Wideband Interference Cancellation for QPSK Modulated Signals Based on Multitap LMS Loop","authors":"Zhipeng Liu,&nbsp;Yunhao Jiang","doi":"10.1049/cmu2.70081","DOIUrl":"https://doi.org/10.1049/cmu2.70081","url":null,"abstract":"<p>With the advancement of wireless communication technologies, co-site broadband interference has become a critical issue for independent communication platforms such as satellites and space stations, while the strong self-interference cancellation in Fifth Generation (5G) full-duplex systems remains an urgent challenge. For the cancellation structure addressing multipath self-interference, the dual least mean squares (LMS) loop hardware architecture exhibits high complexity and cost when the number of channels increases. Moreover, limited research has been conducted on interference cancellation for Quadrature Phase-Shift Keying (QPSK) signals, particularly regarding the complete analytical expression of its interference cancellation. This study focuses on QPSK signals and establishes weight differential equations based on dual-LMS-loop and single-LMS-loop radio frequency (RF) interference cancellation architectures, deriving a comprehensive analytical expression for interference cancellation. For indoor multipath interference cancellation scenarios, the relationship between interference cancellation ratio and the number of taps, delay interval, system gain, and interference signal bandwidth is analysed. The results demonstrate that the single-LMS-loop system exhibits superior interference suppression performance and system compactness compared to the dual-LMS-loop system for QPSK signal cancellation, with simulations verifying its feasibility and effectiveness.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316747","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}

{"title":"Blockchain Empowered Knowledge Resource Protection Model and Its Application","authors":"Weigang Ma,&nbsp;Jiaqi Qi,&nbsp;Minying Ye,&nbsp;Yibo Zhang","doi":"10.1049/cmu2.70083","DOIUrl":"https://doi.org/10.1049/cmu2.70083","url":null,"abstract":"<p>An increasing number of knowledge resources are stored and disseminated in digital form, resulting in new challenges for Intellectual Property Rights (IPR) protection, including difficulty in establishing rights, difficulty in defending rights, and difficulty in incurring high costs. The proposed system in this paper aims to use Internet of Things (IoT) devices to collect knowledge resource data, store it in the Interplanetary File System (IPFS) network, and mint non-fungible tokens (NFTs) in the blockchain, simplifying the process of IPR confirmation and protection while reducing costs. Additionally, blockchain transactions are delivered to the blockchain service network (BSN) to enhance network credibility. Experimental results show an average file storage time of 0.05 s, a 13% reduction in the average time for property rights registration, and a reduction in maintenance costs.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272715","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}

{"title":"Deep Learning-Driven Semantic Communication With Attention Modules","authors":"Zahra Mohammadi,&nbsp;Mohammad Ali Amirabadi,&nbsp;Mohammad Hossein Kahaei","doi":"10.1049/cmu2.70090","DOIUrl":"https://doi.org/10.1049/cmu2.70090","url":null,"abstract":"<p>In this study, an innovative architecture is proposed to enhance the performance of semantic communication networks by leveraging deep learning and joint source-channel coding. A fundamental challenge in this field is the strong dependence of conventional networks on a fixed signal-to-noise ratio (SNR) during training, which leads to performance degradation under varying channel conditions. To address this limitation, we introduce a novel attention-based approach that enables dynamic adaptation to different SNR levels, ensuring more stable and optimized communication performance. The proposed model learns more generalized features that exhibit greater resilience to channel variations. To evaluate its effectiveness, extensive simulations were conducted, comparing the performance of the proposed architecture with DeepSC, a state-of-the-art benchmark model in the field. While the baseline model, trained at a single SNR, experiences performance drops under mismatched conditions, the proposed model, trained across a range of SNRs, achieves improvement of 16.2%, 30.8%, 42.8%, and 53.8% for 1, 2, 3, and 4-gram precisions, respectively, in bilingual evaluation understudy score and an 11.4% increase in sentence similarity across challenging low-SNR conditions. Furthermore, the model maintains robust performance with 48% less training data, highlighting its efficiency and data efficiency under practical constraints. These gains confirm the model's superior adaptability and high-quality data reconstruction under diverse conditions. The results of this study underscore the significant benefits of attention-based architectures in semantic communication, particularly in environments with unpredictable channel variations, and highlight their potential for reliable deployment in real-world applications.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272177","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}

{"title":"DDRL-AqP MSPP: Double Deep Reinforcement Learning With Aquila Pelican Optimization Based Energy Hole Prediction for Mobile Sink Path Planning","authors":"Sangeetha S.,&nbsp;T. Aruldoss Albert Victoire,&nbsp;C. Kumar,&nbsp;Sourav Barua","doi":"10.1049/cmu2.70093","DOIUrl":"https://doi.org/10.1049/cmu2.70093","url":null,"abstract":"<p>A wireless sensor network is a collection of spatially distributed sensor nodes that wirelessly communicate to collect and transmit data. Static sink-based routing involves sending data from sensor nodes to a fixed base station (sink node). Sensor nodes on the data path can run out of energy quickly, especially those closer to the sink, and uneven energy consumption may lead to the premature failure of nodes. Mobile sink path planning involves moving the sink node to different locations to collect data that balances energy consumption, extending network lifetime, improved scalability, and enhanced fault tolerance. Still, energy-efficient routing is a challenging task. Thus, the mobile sink path planning (MSPP) by predicting the energy hole using the double deep reinforcement learning (DDRL) is introduced in this research, wherein the optimal action selection by considering the remaining energy and distance is employed using the Aquila pelican optimization (AqP) algorithm. The proposed AqP algorithm is designed by integrating the Pelican optimization-based solution updation with the Aquila Optimization algorithm for enhancing the convergence rate. The proposed AqP-DDRL MSPP accomplished delay, network lifetime, packet delivery ratio, residual energy, and throughput with 1.61 ms, 99.98%, 99.30%, 0.99 J, and 255.31 kbps respectively.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223932","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}