IET Communications最新文献

{"title":"An Improved Flip SD Algorithm for Symmetric Polar Codes","authors":"Yanfeng Li,&nbsp;Xiuyu Yue,&nbsp;Xiangcheng Li,&nbsp;Youming Sun,&nbsp;Haiqiang Chen","doi":"10.1049/cmu2.70144","DOIUrl":"https://doi.org/10.1049/cmu2.70144","url":null,"abstract":"<p>The sphere decoding (SD) algorithm for polar codes can achieve maximum likelihood decoding performance, but suffers from high computational complexity. Based on the polar codes with symmetrical structure, the complexity of the SD algorithm can be reduced at the expense of a slight performance loss. To further improve the performance of the SD algorithm for symmetric polar codes, an improved flip SD algorithm is proposed in this paper. When the estimated codeword fails the cyclic redundancy check (CRC), the proposed algorithm flips an unreliable combined value and re-performs the decoding process. Specifically, using the structure of symmetric polar codes, a flipping set is first constructed to incorporate unreliable combined values. The combined values in the flipping set are sorted according to their reliability. Then, a new flipping operation is developed for the flipped values to further improve performance. Simulation results show that, compared to the symmetric SD algorithms, the proposed algorithm can achieve a performance gain of up to 1.6 dB for the polar code of length 64 with different rates at the frame error rate (FER) of <span></span><math>\u0000 <semantics>\u0000 <msup>\u0000 <mtext>10</mtext>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msup>\u0000 <annotation>$text{10}^{-3}$</annotation>\u0000 </semantics></math>, with a slightly higher computational complexity; when compared to the original SD algorithm, the proposed algorithm can achieve a maximum of 1.58 dB performance gain at FER = <span></span><math>\u0000 <semantics>\u0000 <msup>\u0000 <mtext>10</mtext>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msup>\u0000 <annotation>$text{10}^{-3}$</annotation>\u0000 </semantics></math> but with a lower computational complexity.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"20 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566269","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":"Secure Transmission in ISAC Systems Aided by Active STAR-RIS","authors":"Baofeng Ji,&nbsp;Xinhao Guo,&nbsp;Shahid Mumtaz","doi":"10.1049/cmu2.70145","DOIUrl":"https://doi.org/10.1049/cmu2.70145","url":null,"abstract":"<p>Integrated sensing and communication (ISAC) is a pivotal technology for sixth-generation (6G) networks. Intelligent reflecting surfaces (IRS), particularly the simultaneous transmitting and reflecting IRS (STAR-RIS), enable dynamic channel control to enhance ISAC performance. However, conventional passive STAR-RIS is constrained by limited signal gain. While the emerging active STAR-RIS addresses this limitation via signal amplification, it introduces heightened power consumption and security risks, especially when sensing targets act as potential eavesdroppers. This paper investigates the secure transmission problem in an active STAR-RIS-aided ISAC downlink system. Our objective is to maximize the sum secrecy rate by jointly optimizing the base station beamforming vectors, the sensing signal covariance matrix and the coefficients of the active STAR-RIS for both reflection and transmission, while satisfying practical constraints on power and sensing performance. To solve this non-convex problem, we propose an efficient two-layer alternating optimization algorithm that decomposes it into tractable subproblems. These subproblems are solved using semidefinite relaxation and a novel eigenvalue-penalty-based method. Numerical simulations demonstrate that the proposed active STAR-RIS scheme significantly outperforms baseline architectures (passive STAR-RIS, active RIS and passive RIS) in achieving a superior balance between communication security and sensing capability.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"20 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147618088","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":"Correction to Anti-Jamming Path Planning for UAVs in Urban Environment With Strong Jammers","authors":"","doi":"10.1049/cmu2.70142","DOIUrl":"https://doi.org/10.1049/cmu2.70142","url":null,"abstract":"<p>https://doi.org/10.1049/cmu2.70114</p><p>I have noticed that the funding statement of this article is missing. This work was supported in part by the National Natural Science Foundation of China under Grant No. 62171465, and this funding information is now added in the article.</p><p>We apologize for this error.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"20 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637112","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":"Enhancing Federated Learning in IoT: A Quality-Based Incentive Mechanism With Stackelberg Game Modelling","authors":"Qinchi Li,&nbsp;Haitao Zhao,&nbsp;Qin Wang,&nbsp;Weicong Zhang,&nbsp;Yangzhi Chen,&nbsp;Zhixiang Hu","doi":"10.1049/cmu2.70139","DOIUrl":"https://doi.org/10.1049/cmu2.70139","url":null,"abstract":"<p>In federated learning (FL)-assisted Internet of Things (IoT) systems, FL trains models using datasets on various client devices without sending the datasets to a centralized server. This approach enhances the accuracy and reliability of models while preserving the privacy of client devices. However, FL implementations face challenges, such as single point of server failure and lack of incentives. To address the server failure issue, a backup server can be added. Meanwhile, each FL client has varying data quality and motivations to participate, leading to differences in the quality of local models uploaded to the server. To motivate clients to contribute more, we designed a novel incentive mechanism based on the Stackelberg game. This mechanism allocates rewards based on the quality of the models each client uploads, rather than the amount of data trained. We separately modelled the utilities of the server and the clients, allowing the server to rationally allocate rewards based on each client's contribution to model training. After analysing the utilities, we transform the game into two optimization problems and develop an algorithm whose per-round complexity scales linearly with the number of clients under fixed numerical tolerances. The obtained equilibrium matches exhaustive search within numerical precision while significantly reducing computation.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"20 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146680362","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":"Regularised Hyper Parameter Bi Level Optimisation With Continual Learning Based Deep Neural Network for Beamforming in Ultra-Wide Band System","authors":"Pradeep Kumar Siddanna,&nbsp;Bidare Divakarachari Parameshachari,&nbsp;Dharmanna Shivappa Lamani","doi":"10.1049/cmu2.70137","DOIUrl":"https://doi.org/10.1049/cmu2.70137","url":null,"abstract":"<p>Ultra-Wideband (UWB) is a wireless communication technology that uses Radio Frequency (RF) to transmit and receive signals between devices. Beamforming in UWB is a technique that uses multiple antennas simultaneously to focus on specific directions. In beamforming, Deep Learning (DL) techniques are applied to enhance signal processing and optimise beam pattern generation by utilising neural networks for efficient and accurate spatial filtering. However, existing DL techniques suffer from catastrophic forgetting, in which the testing data forgets previously learnt data due to the lack of knowledge distillation in other layers. Therefore, this research proposes a Regularised Hyperparameter Bilevel Optimisation with Continual Learning-based Deep Neural Network (RHBO-CLDNN) for beamforming in UWB systems. RHBO optimises hyperparameter efficiency at both the upper and lower levels, thereby enabling the DNN to accurately capture UWB channel characteristics, which improves channel estimation and enhances the Signal-to-Noise Ratio (SNR). CL is applied to dynamically adapt to changing environmental conditions without requiring complete retraining, making it suitable for real-time applications. Elastic Weight Consolidation (EWC) regularisation is also applied, which mitigates catastrophic forgetting by preserving weights from learnt tasks and enables the model to adapt to channel conditions without losing previous knowledge. Experiments on the DeepMIMO dataset show that RHBO-CLDNN enhances the sum-rate by up to 18% and achieves an inference time of 0.025 s over Convolutional Neural Network (CNN), thereby demonstrating its suitability for real-time beamforming.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"20 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146148262","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":"Joint Decision-Making for UAV Deployment and Computational Offloading Optimized for Energy Consumption and Latency in Space-Air-Ground Integrated Networks","authors":"Tengda Huang,&nbsp;Tao Hu,&nbsp;Di Wu,&nbsp;Wenzhi Zhao","doi":"10.1049/cmu2.70134","DOIUrl":"https://doi.org/10.1049/cmu2.70134","url":null,"abstract":"<p>With the rapid advancement of communication technologies, space-air-ground integrated networks (SAGIN) have become a pivotal research frontier in current and future communication domains. To tackle critical challenges in SAGIN scenarios, such as excessive task-related energy consumption and insufficient communication-computing resources, this paper proposes a three-tier edge computing architecture integrating satellites, unmanned aerial vehicle (UAV) swarms, and ground systems. Aiming to minimize the system's weighted energy consumption and latency, we investigate the joint optimization of task allocation, user-UAV association, UAV deployment, and resource allocation between UAVs and low-earth orbit (LEO) satellites. Formulated as a non-convex mixed-integer nonlinear combinatorial optimization problem, this work integrates the branch-and-bound method, multi-start global optimization, and gray wolf optimization (GWO) to develop a suboptimal solution based on block coordinate descent (BCD), which decouples the original problem into three subproblems for independent solving and iterative approximation of the optimal solution. Experimental results show that the proposed algorithm reduces the total system cost by 7.81%, 11.99%, and 45.69% compared with baseline algorithms with random user-UAV association, random UAV positioning, and random task assignment, respectively, effectively cutting down overall energy consumption and task latency.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"20 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162764","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":"Hyperparameter-Free Maximum Versoria Criterion Based Channel-State Acquisition for mmWave Hybrid MIMO With Hardware Impairments","authors":"Rangeet Mitra,&nbsp;Sandesh Jain,&nbsp;K. Venkateswaran,&nbsp;Rajat Kumar,&nbsp;Vimal Bhatia,&nbsp;Ondrej Krejcar","doi":"10.1049/cmu2.70135","DOIUrl":"https://doi.org/10.1049/cmu2.70135","url":null,"abstract":"<p>Millimetre wave (mmWave) multiple-input multiple-output (MIMO) has emerged as a promising physical layer solution to address traffic demands in beyond 5G wireless communication systems. However, modern signal processing techniques for mmWave hybrid MIMO systems fall short of addressing the performance degradations due to residual transceiver hardware impairments (HIs). This paper thus considers a mmWave hybrid MIMO system with residual HIs. Using Bussgang decomposition, the residual transceiver HIs are modelled as an additive non-Gaussian noise that severely affects the received pilot and information signals, which makes channel state acquisition challenging. In this context of channel-estimation over non-Gaussian noise due to HIs, this work presents a hyperparameter-free maximum Versoria criterion (MVC)-based channel estimation technique. In details, the MVC-based channel-estimator is rendered hyperparameter-free by proposing a sampling rule for its spread parameter <span></span><math>\u0000 <semantics>\u0000 <mi>τ</mi>\u0000 <annotation>$tau$</annotation>\u0000 </semantics></math> and a gradient descent-based optimisation for its shape parameter <span></span><math>\u0000 <semantics>\u0000 <mi>p</mi>\u0000 <annotation>$p$</annotation>\u0000 </semantics></math>. Finally, simulations are presented to show the generalisation and scenario-invariance of the proposed MVC-based channel-estimator. The analytical expression for steady-state misadjustment is also derived and validated by simulations.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"20 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146155091","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":"Communication-Security Co-Design for Federated Learning in Grant-Free NOMA IoT Networks","authors":"Emmanuel Atebawone,&nbsp;Kwame Opuni-Boachie Obour Agyekum,&nbsp;James Dzisi Gadze,&nbsp;Kwasi Adu-Boahen Opare,&nbsp;Owusu Agyeman Antwi,&nbsp;Robert Akromond","doi":"10.1049/cmu2.70138","DOIUrl":"https://doi.org/10.1049/cmu2.70138","url":null,"abstract":"<p>The rapid growth of 6G Internet of Things (IoT) networks demands scalable and secure learning systems that can support massive device connectivity with minimal coordination overhead. Federated learning (FL) over grant-free non-orthogonal multiple access (GF-NOMA) offers a promising approach by enabling distributed model training with asynchronous uplink access and low signalling cost. However, this setup introduces coupled vulnerabilities: The uncoordinated nature of GF-NOMA leads to random collisions and residual interference, while the decentralised nature of FL exposes the system to poisoning, Sybil and jamming attacks. These cross-layer threats jointly degrade model convergence and communication reliability. To address this, we propose Security-Aware Proximal Policy Optimisation (SA-PPO), a reinforcement learning framework that co-designs communication security for FL over GF-NOMA. SA-PPO jointly embeds physical-layer features (e.g., SINR and interference) and learning-layer signals (e.g., anomaly scores and trust values) into its state, action and reward spaces. This enables the base station to optimise admission control, resource allocation and trust-weighted aggregation in a unified loop. Unlike prior methods that treat communication and security independently, SA-PPO learns coordinated strategies that attenuate adversarial impact while preserving update diversity. Simulation results show that SA-PPO achieves over 90% anomaly detection accuracy, sustains secure participation above 80% and reduces collision-induced decoding errors by 25% under scenarios with up to 40% compromised devices, while incurring only modest increases in energy and latency. These results demonstrate SA-PPO's effectiveness for secure, scalable and resilient edge intelligence in future 6G IoT environments.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"20 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146148174","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":"Construction-Free Polar Coding For Practical Entropy Coding Tasks","authors":"Zichang Ren,&nbsp;Cheng Zhang,&nbsp;Yuping Zhao","doi":"10.1049/cmu2.70130","DOIUrl":"https://doi.org/10.1049/cmu2.70130","url":null,"abstract":"<p>This paper explores the practical application of source polar codes to entropy coding tasks in modern transform coding pipelines. Transform coding remains the predominant and rapidly evolving framework for compressing complex real-world data. Despite the strong theoretical guarantees of polar codes, conventional polarization-based compression techniques follow a “construct-then-use” paradigm, which proves inefficient and inaccurate when applied to transform coding scenarios characterized by highly dynamic entropy models. To overcome this limitation, we propose a construction-free, plug-and-play polar compression scheme. Rather than relying on precomputed polarized entropies, our method selects output symbols based on probability vectors generated by a conditional entropy model. These vectors can be computed with low complexity and exact numerical precision, enabling efficient adaptation across diverse entropy coding tasks. The proposed approach offers greater flexibility than classical methods and achieves superior performance in the finite-length regime.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"20 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057697","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":"A Contrastive GAN-Based Framework for Full-Body Visual Privacy Protection in Open World Scenarios","authors":"Haolong Fu,&nbsp;Xuan He","doi":"10.1049/cmu2.70128","DOIUrl":"https://doi.org/10.1049/cmu2.70128","url":null,"abstract":"<p>Generative adversarial networks (GANs) with their strong generative capabilities have shown significant promise in visual privacy protection. However, when applied to image full-body visual privacy protection in open-world scenarios, where abnormal visual privacy data may exist in the training data, issues such as mode collapse and instability in GANs can be severely exacerbated. This leads to a significant reduction in both image quality and utility preservation. In this paper, we propose an end-to-end, contrastive GANs-based framework, FBPPGAN, for image full-body visual privacy protection, specifically designed to address these challenges. First, we introduce the architecture of FBPPGAN, which is tailored for full-body visual privacy protection. Second, we propose a novel adversarial loss function aimed at mitigating mode collapse and instability, particularly in the presence of abnormal images in open-world environments. We also design a content mapping network and a content loss function based on contrastive learning to address the issue of insufficient color gamut in generated images. Furthermore, a stylized loss function is introduced to more accurately measure the difference between the generated and target domains. Experimental results across four public datasets demonstrate that FBPPGAN effectively overcomes mode collapse and instability, delivering superior image quality and utility preservation. Compared to the existing state-of-the-art methods, FBPPGAN outperforms in terms of convergence, stability, computational complexity, processing speed, and effectiveness. To the best of our knowledge, this is the first GAN-based framework for image full-body visual privacy protection in open-world scenarios.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"20 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057807","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}