IEEE Journal of Selected Topics in Signal Processing最新文献_第2页

IEEE Signal Processing Society Publication Information IEEE信号处理学会出版物信息

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-04-01 Epub Date: 2026-06-01 DOI: 10.1109/JSTSP.2026.3681339

引用次数: 0

Intelligent Cooperative Covert ISAC via Fluid Antennas: A GAN-Optimized Framework 基于流体天线的智能协同隐蔽ISAC: gan优化框架

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1109/JSTSP.2026.3658184

Chao Wang;Zan Li;Xiangnan Zhou;Liang Jin

{"title":"Intelligent Cooperative Covert ISAC via Fluid Antennas: A GAN-Optimized Framework","authors":"Chao Wang;Zan Li;Xiangnan Zhou;Liang Jin","doi":"10.1109/JSTSP.2026.3658184","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3658184","url":null,"abstract":"The dual functionality of Integrated sensing and communications (ISAC) signals introduces significant security challenges, including information leakage to unauthorized users and heightened vulnerability to detection by malicious adversaries. This paper addresses these issues by leveraging the extremely large fluid antenna system (FAS) to enable covert and secure communication while simultaneously sensing multiple radar targets in a cooperative millimeter-wave (mmWave) ISAC system. To ensure security and covertness, we formulate a joint optimization problem for the analog beamforming, digital precoder, FAS port-activation matrix, and radar-sensing signal covariance matrix. Our goal is to maximize the minimum secrecy rate and Willie's detection error probability (DEP), subject to a minimum sensing signal-to-interference-plus-noise ratio (SINR) constraint. This problem is highly challenging due to three main factors: (i) it is a multi-objective optimization problem; (ii) the lack of an analytical expression for the DEP makes a tractable problem formulation difficult; and (iii) the problem is non-convex, stemming from the combinatorial nature of the activation matrix design, coupled variables, and the non-convex objective functions. To overcome these challenges, we propose a novel joint design framework based on a generative adversarial network (GAN). This framework employs a discriminator network to emulate the optimal detector at Willie and a generator network to optimize the system parameters. Adversarial training is then used to optimize the system design against this optimal detector. Numerical simulations demonstrate the potential of FAS in enhancing both communication security and sensing performance, and confirm the superior performance of the proposed GAN-based joint optimization framework.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 3","pages":"262-276"},"PeriodicalIF":13.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148104161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scalable Fluid Antenna Systems: A New Paradigm for Array Signal Processing 可扩展流体天线系统：阵列信号处理的新范例

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-04-01 Epub Date: 2026-03-13 DOI: 10.1109/JSTSP.2026.3673981

Tuo Wu;Ye Tian;Jie Tang;Kangda Zhi;Maged Elkashlan;Kin-Fai Tong;Naofal Al-Dhahir;Chan-Byoung Chae;Matthew C. Valenti;George K. Karagiannidis;Kwai-Man Luk

{"title":"Scalable Fluid Antenna Systems: A New Paradigm for Array Signal Processing","authors":"Tuo Wu;Ye Tian;Jie Tang;Kangda Zhi;Maged Elkashlan;Kin-Fai Tong;Naofal Al-Dhahir;Chan-Byoung Chae;Matthew C. Valenti;George K. Karagiannidis;Kwai-Man Luk","doi":"10.1109/JSTSP.2026.3673981","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3673981","url":null,"abstract":"Most existing antenna array-based source localization methods rely on fixed-position arrays (FPAs) and strict assumptions about source field conditions (near-field or far-field), which limits their effectiveness in complex, dynamic real-world scenarios where high-precision localization is required. In contrast, this paper introduces a novel scalable fluid antenna system (SFAS) that can dynamically adjust its aperture configuration to optimize performance for different localization tasks. Within this framework, we develop a two-stage source localization strategy based on the exact spatial geometry (ESG) model: the first stage uses a compact aperture configuration for initial direction-of-arrival (DOA) estimation, while the second stage employs an expanded aperture for enhanced DOA and range estimation. The proposed approach eliminates the traditional need for signal separation or isolation to classify source types and enables a single SFAS array to achieve high localization accuracy without field-specific assumptions, model simplifications, or approximations, representing a new paradigm in array-based source localization. Extensive simulations demonstrate the superiority of the proposed method in terms of localization accuracy, computational efficiency, and robustness to different source types.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 3","pages":"389-406"},"PeriodicalIF":13.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148104169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Signal Processing Society Information IEEE信号处理学会信息

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-04-01 Epub Date: 2026-06-01 DOI: 10.1109/JSTSP.2026.3691042

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Fluid Antenna-Aided ISAC Systems for Low-Altitude Economy Networks 低空经济网络流体天线辅助ISAC系统

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-04-01 Epub Date: 2026-03-06 DOI: 10.1109/JSTSP.2026.3671173

Yiping Zuo;Yupeng Nie;Weicong Chen;Chen Dai;Weibei Fan;Jun Zhang;Shi Jin

{"title":"Fluid Antenna-Aided ISAC Systems for Low-Altitude Economy Networks","authors":"Yiping Zuo;Yupeng Nie;Weicong Chen;Chen Dai;Weibei Fan;Jun Zhang;Shi Jin","doi":"10.1109/JSTSP.2026.3671173","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3671173","url":null,"abstract":"In low-altitude economy (LAE) networks, integrated sensing and communication (ISAC) exhibits transformative potential yet is constrained by fixed-antenna architectures amid dynamic environments. Fluid antenna (FA) technology has accordingly been proposed as a mitigating solution. Nevertheless, the suboptimal synergy between FA and LAE platforms, alongside self-interference during signal transmission and echo reception, remain critical hurdles for ISAC's practical implementation in LAE contexts. To address these challenges, we propose a novel FA-aided ISAC system for LAE networks. Detailed models for the communication and sensing processes are introduced to support this system. Then, we formulate beamforming and antenna positioning strategies as a non-cooperative game, aiming to maximize the total communication and sensing rate for each ISAC BS. To tackle this complex problem, we decompose the game-theoretic problem into multiple sub-game optimization problems and analyze each separately. A mixed alternating iterative algorithm based on the sine cosine and particle swarm optimization (SCPSO) is developed to find the optimal solutions. Extensive simulations show the rapid convergence of the proposed mixed SCPSO-based algorithm. In addition, the FA-aided ISAC scheme for LAE outperforms conventional antenna positioning schemes. The results under different parameter settings further demonstrate that the FA-aided ISAC system for LAE significantly improves both communication and sensing performance through dynamic antenna reconfiguration.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 3","pages":"294-308"},"PeriodicalIF":13.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148104115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Activity Detection and Channel Estimation for Fluid Antenna System Exploiting Geographical and Angular Information 基于地理和角度信息的流体天线系统联合活动检测与信道估计

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-04-01 Epub Date: 2026-03-18 DOI: 10.1109/JSTSP.2026.3673148

Zhentian Zhang;Jian Dang;David Morales-Jimenez;Hao Jiang;Zaichen Zhang;Christos Masouros;Chan-Byoung Chae

{"title":"Joint Activity Detection and Channel Estimation for Fluid Antenna System Exploiting Geographical and Angular Information","authors":"Zhentian Zhang;Jian Dang;David Morales-Jimenez;Hao Jiang;Zaichen Zhang;Christos Masouros;Chan-Byoung Chae","doi":"10.1109/JSTSP.2026.3673148","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3673148","url":null,"abstract":"The fluid antenna system (FAS) refers to a family of reconfigurable antenna technologies that provide substantial spatial gains within a compact, predefined small space, thereby offering extensive degrees of freedom in the physical layer for future communication networks. The acquisition of channel state information (CSI) is critical, as it determines the placement of ports/antennas, which directly impacts FAS-based optimization. Although various channel estimation methods have been developed, significant flaws persist. For instance, the performance of greedy-based algorithms is heavily influenced by signal assumptions, and current model-free kernel-learning methods are infeasible due to prohibitively high computational complexity issue (cubic order). Consequently, there is a pressing need for a well-balanced solution that exhibits flexibility, feasibility, and low complexity to support massive connectivity in FAS. In this work, we propose methods based on approximate message passing (AMP) integrated with adaptive expectation maximization (EM). The EM-AMP framework uniquely enables efficient large matrix computations with adaptive learning capabilities, independent of prior knowledge of the model or parameters within potential distributions, making it a robust candidate for FAS networks. We introduce two variants of the EM-AMP framework that leverage geographical and angular features in a FAS network. These proposed algorithms demonstrate improved estimation precision, fast convergence, and low computational complexity in large activity regions. Additionally, we analytically elucidate the reasons behind the inherent performance floor of greedy-based methods and highlight the critical role of angular information in algorithm design. Extensive numerical results validate the promising efficacy of the proposed algorithm designs and the derived analytical findings.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 3","pages":"354-370"},"PeriodicalIF":13.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148104172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A CNN-Transformer Hybrid Framework for Mapping Annual Wheat Fractional Cover From 2001-2023 Using MODIS Satellite Data Over Asia 利用MODIS卫星数据绘制2001-2023年亚洲小麦年复盖度的CNN-Transformer混合框架

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1109/JSTSP.2026.3660045

Wenyuan Li;Shunlin Liang;Yongzhe Chen;Han Ma;Jianglei Xu;Yichuan Ma;Zhongxin Chen;Husheng Fang;Fengjiao Zhang

{"title":"A CNN-Transformer Hybrid Framework for Mapping Annual Wheat Fractional Cover From 2001-2023 Using MODIS Satellite Data Over Asia","authors":"Wenyuan Li;Shunlin Liang;Yongzhe Chen;Han Ma;Jianglei Xu;Yichuan Ma;Zhongxin Chen;Husheng Fang;Fengjiao Zhang","doi":"10.1109/JSTSP.2026.3660045","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3660045","url":null,"abstract":"Wheat is a staple crop in over 40 countries, with Asia accounting for more than 40% of global cultivation. Long-term mapping of wheat cover is critical for agricultural management and food security. However, existing wheat mapping products face a key limitation in spatiotemporal coverage: they either offer broad spatial coverage for a single or few years, or provide long time series that are confined to specific regions. To address this gap, we propose DeepMapping, a hybrid deep learning framework designed to generate a consistent annual fractional wheat cover product at 250 m resolution from 2001 to 2023 over Asia. Our framework integrates Convolutional Neural Networks (CNNs) and Transformer models to extract complementary spatial and temporal features. It processes multi-resolution data, including 250 m and 500 m MODIS reflectance, alongside GLASS Leaf Area Index and Fractional Vegetation Cover products. The model is trained with fractions derived from the 10 m resolution WorldCereal 2021 map and refined with ancillary information such as coarse-resolution crop products, land cover data, and agricultural statistics to enhance reliability. By learning the relationship between short-term, high-resolution labels and long-term, coarse-resolution MODIS observations, DeepMapping is efficiently used to generates annual wheat fractional cover product over Asia at 250 m from 2001–2023. Validation with 2,556 samples demonstrates overall accuracy of 84.1%, producer's accuracy of 87.8%, and user's accuracy of 70.07%. Comparison with national statistical data confirms high consistency (<inline-formula><tex-math>$R^{2}$</tex-math></inline-formula>: 0.880–0.943). DeepMapping offers a scalable solution for long-term, reliable agricultural mapping.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"153-167"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Labels Generated by Large Language Models Help Measure People’s Empathy in Vitro 由大型语言模型生成的标签有助于在体外测量人们的同理心

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-03-06 DOI: 10.1109/JSTSP.2026.3671186

Md Rakibul Hasan;Yue Yao;Md Zakir Hossain;Aneesh Krishna;Imre Rudas;Shafin Rahman;Tom Gedeon

{"title":"Labels Generated by Large Language Models Help Measure People’s Empathy in Vitro","authors":"Md Rakibul Hasan;Yue Yao;Md Zakir Hossain;Aneesh Krishna;Imre Rudas;Shafin Rahman;Tom Gedeon","doi":"10.1109/JSTSP.2026.3671186","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3671186","url":null,"abstract":"Large language models (LLMs) have revolutionised many fields, with LLM-as-a-service (LLMSaaS) offering accessible, general-purpose solutions without costly task-specific training. In contrast to the widely studied prompt engineering for directly solving tasks (in vivo), this paper explores LLMs’ potential for in-vitro applications: using LLM-generated labels to improve supervised training of mainstream models. We examine two strategies – (1) noisy label correction and (2) training data augmentation – in empathy computing, an emerging task to predict psychology-based questionnaire outcomes from inputs like textual narratives. Crowdsourced datasets in this domain often suffer from noisy labels that misrepresent underlying empathy. We show that replacing or supplementing these crowdsourced labels with LLM-generated labels, developed using psychology-based scale-aware prompts, achieves statistically significant accuracy improvements. Notably, the RoBERTa pre-trained language model (PLM) trained with noise-reduced labels yields a state-of-the-art Pearson correlation coefficient of 0.648 on the public NewsEmp benchmarks. This paper further analyses evaluation metric selection and demographic biases to help guide the future development of more equitable empathy computing models.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"213-226"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-Model Adjudication for Bias Mitigation in Large Language Models 大型语言模型中偏见缓解的跨模型裁决

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1109/JSTSP.2026.3662478

Xiangmei Li;Changwei Li;Wenjie Liu;Yan Long;Yifan Huang;Yi Liu

{"title":"Cross-Model Adjudication for Bias Mitigation in Large Language Models","authors":"Xiangmei Li;Changwei Li;Wenjie Liu;Yan Long;Yifan Huang;Yi Liu","doi":"10.1109/JSTSP.2026.3662478","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3662478","url":null,"abstract":"With the increasing adoption and prevalence of large language models (LLMs), concerns regarding their inherent biases have become paramount. Existing approaches often rely on fixed datasets and metrics for bias detection and subsequent fine-tuning for mitigation. However, relying solely on such fixed datasets and metrics, akin to standardized exams, may be insufficient due to their inherent inflexibility (e.g., fixed testing content) and susceptibility to gaming. Furthermore, these benchmarks risk contamination if inadvertently included in training data. Drawing an analogy to human peer review and collaborative learning, this paper introduces a novel Cross-Model Adjudication Framework (CMAF) for detecting and mitigating biases in LLMs. We implement a distributed peer-review mechanism where four state-of-the-art models (Qwen2.5-7B, DeepSeek-7B-chat, Gemma2-9B, LLaMA3.1-8B) critically evaluate each other’s responses to prompts from the HolisticBias dataset. The consensus-derived, low-bias outputs are then utilized for parameter-efficient fine-tuning. Our method achieves a bias reduction of up to 12.3%, measured by the statistical significance of token likelihood differences across demographic groups, and yields another comparable B-score metric against commercial LLMs, while preserving core task performance and maintaining minimal inference latency overhead post-fine-tuning.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"177-191"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11373720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SPGrasp: Spatiotemporal Prompt-Driven Grasp Synthesis in Dynamic Scenes SPGrasp：动态场景中时空驱动的抓取合成

IF 13.7 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2026-03-01 Epub Date: 2026-03-06 DOI: 10.1109/JSTSP.2026.3671182

Yunpeng Mei;Hongjie Cao;Wei Xiao;Yinqiu Xia;Zhaohan Feng;Gang Wang;Jie Chen

{"title":"SPGrasp: Spatiotemporal Prompt-Driven Grasp Synthesis in Dynamic Scenes","authors":"Yunpeng Mei;Hongjie Cao;Wei Xiao;Yinqiu Xia;Zhaohan Feng;Gang Wang;Jie Chen","doi":"10.1109/JSTSP.2026.3671182","DOIUrl":"https://doi.org/10.1109/JSTSP.2026.3671182","url":null,"abstract":"Real-time interactive grasp synthesis for dynamic objects remains challenging, as existing instance-level methods struggle to achieve low-latency inference while maintaining robust temporal consistency. To bridge this gap, we propose SPGrasp (Spatiotemporal Prompt-driven dynamic Grasp synthesis), a novel framework that extends the Segment Anything Model 2 (SAM 2) for video-stream grasp estimation. Our core innovation integrates user prompts with a spatiotemporal context module, enabling real-time interaction with end-to-end latency as low as 59 ms while preserving consistent instance identity and grasp predictions in dynamic, cluttered scenes, including object overlap and occlusion. In benchmark evaluations, SPGrasp achieves instance-level grasp accuracies of 90.6% on OCID and 93.8% on Jacquard. On the GraspNet-1Billion dataset under continuous tracking, SPGrasp reaches 92.0% accuracy with 73.1 ms per-frame latency, corresponding to a 58.5% latency reduction over the prior state-of-the-art promptable method RoG-SAM while maintaining competitive accuracy. Real-world experiments further demonstrate reliable interactive grasping under frequent occlusions, achieving a 94.8% success rate. These results suggest that SPGrasp effectively mitigates the latency–interactivity trade-off in dynamic grasp synthesis.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"20 2","pages":"202-212"},"PeriodicalIF":13.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0