IEEE Transactions on Cognitive Communications and Networking最新文献_第9页

Toward AI-Native Ultra-Dense Networks: Cell Association via Multi-View Sensing 面向人工智能原生超密集网络：基于多视图感知的细胞关联

IF 7 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-04-20 DOI: 10.1109/TCCN.2026.3685403

Khoa Anh Ngo;Seungnyun Kim;Jihoon Moon;Byungju Lee;Jiao Wu;Byonghyo Shim

{"title":"Toward AI-Native Ultra-Dense Networks: Cell Association via Multi-View Sensing","authors":"Khoa Anh Ngo;Seungnyun Kim;Jihoon Moon;Byungju Lee;Jiao Wu;Byonghyo Shim","doi":"10.1109/TCCN.2026.3685403","DOIUrl":"10.1109/TCCN.2026.3685403","url":null,"abstract":"Millimeter wave (mmWave)/terahertz (THz) ultra-dense networks (UDNs) are anticipated to meet the technical performance requirements (TPRs) of immersive 6G applications. By deploying a large number of small base stations (SBSs), UDN can mitigate the severe attenuation in mmWave and THz frequency bands. To fully enjoy the benefits of mmWave/THz UDNs, ensuring line-of-sight (LoS) communication between SBSs and user equipments (UEs) is essential. However, identifying LoS conditions and determining proper cell associations require a contextual understanding of the surrounding environments, which conventional wireless measurement-based approaches may not readily provide. In this paper, we propose a novel cell association scheme for 6G mmWave/THz UDN based on vision sensing and computer vision (CV) technologies. The key idea of the proposed technique is to directly extract the positions and LoS conditions of UEs using multi-view RGB images. As the proposed technique operates without relying on wireless measurements, it eliminates the need for pilot transmissions and channel feedback, thereby reducing signaling overhead and latency significantly. Furthermore, by capturing UEs from multiple viewpoints, we can effectively detect them even in the presence of occlusions. From extensive numerical evaluations on realistic UDN scenarios, we show that the proposed scheme achieves <inline-formula> <tex-math>$30{,}%$ </tex-math></inline-formula> throughput gain over conventional cell association methods.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"7845-7861"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147725739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative AI-Empowered Signal Processing for Collaborative Embodied Agents: A Survey on Agentic Security 基于生成人工智能的协同具身代理信号处理：代理安全研究综述

IF 7 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-03-20 DOI: 10.1109/TCCN.2026.3676045

Gaofeng Pan;Yi Tao;Ziyi Yang;Junnan Li;Yuanyuan Peng;Xuemin Bao;Yiyang Zhang;Zizheng Hua;Shuai Wang;Rui Zhang;Changhao Du

{"title":"Generative AI-Empowered Signal Processing for Collaborative Embodied Agents: A Survey on Agentic Security","authors":"Gaofeng Pan;Yi Tao;Ziyi Yang;Junnan Li;Yuanyuan Peng;Xuemin Bao;Yiyang Zhang;Zizheng Hua;Shuai Wang;Rui Zhang;Changhao Du","doi":"10.1109/TCCN.2026.3676045","DOIUrl":"10.1109/TCCN.2026.3676045","url":null,"abstract":"The transition from the Internet of Things (IoT) to the Internet of Agents (IoA) necessitates a fundamental paradigm shift in physical layer security, moving from static, rule-based defenses toward autonomous, agentic resilience. However, current embodied nodes often lack the cognitive capability required to handle adaptive, intelligent threats. We propose employing Generative AI (GAI) as the cognitive engine—rather than merely a signal processing tool—to empower agents to construct an internal world model of the physical environment. In this work, we present a holistic agentic security framework that instills autonomy across two key dimensions. First, Endogenous Self-Evolution allows agents to utilize GAI for predictive environmental modeling and autonomous resource decision-making, thereby self-calibrating against hardware impairments. Second, Exogenous Active Defense implements a generative observe-orient-decide-act (OODA) loop. This loop enables agents to autonomously execute intelligent threat perception, confrontation planning, and a strategic duality of behavioral concealment and identity camouflage. Finally, we discuss the path toward general-purpose security agents via neuro-symbolic architectures. This survey bridges the gap between signal generation and agentic reasoning, defining a new era where security becomes an innate, evolving property of collaborative embodied intelligence.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6741-6759"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Spatio-Temporal Federated Learning for Cellular Traffic Prediction 蜂窝交通预测的联合时空联邦学习

IF 7 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2026-01-01 Epub Date: 2026-03-19 DOI: 10.1109/TCCN.2026.3694873

Chenhan Zhai;Hui Liu;Ertong Shang;Lizhe Zhang;Zeyu Qiu;Junzhao Du

{"title":"Joint Spatio-Temporal Federated Learning for Cellular Traffic Prediction","authors":"Chenhan Zhai;Hui Liu;Ertong Shang;Lizhe Zhang;Zeyu Qiu;Junzhao Du","doi":"10.1109/TCCN.2026.3694873","DOIUrl":"https://doi.org/10.1109/TCCN.2026.3694873","url":null,"abstract":"Cellular traffic prediction is essential for the energy-efficient and intelligent management of base stations (BSs). In this context, Federated Learning allows BSs to collaboratively train a shared model without transmitting raw data, thus significantly reducing communication overhead and enhancing privacy protection. Despite these advantages, most existing methods struggle to alleviate the coupled spatio-temporal heterogeneity of cellular traffic data across BSs, resulting in suboptimal global prediction performance. The main reason is that they decouple spatio-temporal relationships by modeling temporal and spatial properties independently, with limited consideration of their dependencies in cellular traffic. To overcome these limitations, we propose FedJST, a joint spatio-temporal federated cellular traffic prediction method that effectively integrates the temporal and spatial properties of cellular traffic from different BSs in two key aspects. Firstly, to capture spatio-temporal dependencies, we propose a multi-scale feature guidance strategy. This strategy leverages global multi-scale feature to incorporate shared spatio-temporal information, encouraging local models to capture complex spatio-temporal interactions. Secondly, we propose a spatio-temporal property-aware BS selection strategy that leverages a lightweight augmented dataset to prioritize representative BSs for training, enabling effective learning of diverse spatio-temporal patterns. We conduct extensive experiments across six cases on two real-world datasets, demonstrating that FedJST achieves average improvements of 5.95%, 4.43%, and 2.99% in RMSE, MAE, and <inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula> metrics, respectively.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"8609-8624"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Full-Duplex ISAC-Enabled D2D Underlaid Cellular Networks: Joint Transceiver Beamforming and Power Allocation 全双工isac支持的D2D底层蜂窝网络：联合收发器波束形成和功率分配

IF 8 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-03-18 DOI: 10.1109/TCCN.2025.3552627

Tao Jiang;Ming Jin;Qinghua Guo;Yinhong Liu;Yaming Li;Junteng Yao

引用次数: 0

Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information xml - mimo近场信道估计：边信息引导下的深度生成模型

IF 8 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-03-01 DOI: 10.1109/TCCN.2025.3566047

Zhenzhou Jin;Li You;Derrick Wing Kwan Ng;Xiang-Gen Xia;Xiqi Gao

{"title":"Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information","authors":"Zhenzhou Jin;Li You;Derrick Wing Kwan Ng;Xiang-Gen Xia;Xiqi Gao","doi":"10.1109/TCCN.2025.3566047","DOIUrl":"10.1109/TCCN.2025.3566047","url":null,"abstract":"This paper investigates the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first establish a joint angle-distance (AD) domain-based spherical-wavefront physical channel model that captures the inherent sparsity of XL-MIMO channels. Leveraging the channel’s sparsity in the joint AD domain, the CE is approached as a task of reconstructing sparse signals. Anchored in this framework, we first propose a compressed sensing algorithm to acquire a preliminary channel estimate. Harnessing the powerful implicit prior learning capability of generative artificial intelligence (GenAI), we further propose a GenAI-based approach to refine the estimated channel. Specifically, we introduce the preliminary estimated channel as side information, and derive the evidence lower bound (ELBO) of the log-marginal distribution of the target NF channel conditioned on the preliminary estimated channel, which serves as the optimization objective for the proposed generative diffusion model (GDM). Additionally, we introduce a more generalized version of the GDM, the non-Markovian GDM (NM-GDM), to accelerate the sampling process, achieving an approximately tenfold enhancement in sampling efficiency. Experimental results indicate that the proposed approach is capable of offering substantial performance gain in CE compared to existing benchmark schemes within NF XL-MIMO systems. Furthermore, our approach exhibits enhanced generalization capabilities in both the NF or far-field (FF) regions.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"628-643"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks 数字双边缘网络中的节能联邦学习与迁移

IF 8 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-03-06 DOI: 10.1109/TCCN.2025.3567096

Yuzhi Zhou;Yaru Fu;Zheng Shi;Howard H. Yang;Kevin Hung;Yan Zhang

{"title":"Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks","authors":"Yuzhi Zhou;Yaru Fu;Zheng Shi;Howard H. Yang;Kevin Hung;Yan Zhang","doi":"10.1109/TCCN.2025.3567096","DOIUrl":"10.1109/TCCN.2025.3567096","url":null,"abstract":"The digital twin edge network (DITEN) is a significant paradigm in the sixth-generation wireless system (6G) that aims to organize well-developed infrastructures to meet the requirements of evolving application scenarios. However, the impact of the interaction between the long-term DITEN maintenance and detailed digital twin tasks, which often entail privacy considerations, is commonly overlooked in current research. This paper addresses this issue by introducing a problem of digital twin association and historical data allocation for a federated learning (FL) task within DITEN. To achieve this goal, we start by introducing a closed-form function to predict the training accuracy of the FL task, referring to it as the data utility. Subsequently, we carry out comprehensive convergence analyses on the proposed FL methodology. Our objective is to jointly optimize the data utility of the digital twin-empowered FL task and the energy costs incurred by the long-term DITEN maintenance, encompassing FL model training, data synchronization, and twin migration. To tackle the aforementioned challenge, we present an optimization-driven learning algorithm that effectively identifies optimized solutions for the formulated problem. Numerical results demonstrate that our proposed algorithm outperforms various baseline approaches.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"743-758"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zero-Shot Binarization Lightweight Model for a Data-Free Edge Network 无数据边缘网络的零枪二值化轻量级模型

IF 8 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-03-13 DOI: 10.1109/TCCN.2025.3569598

Kai Zeng;Hongwei Gu;Yuxiu Duan;Tao Shen;Ruidong Li

{"title":"Zero-Shot Binarization Lightweight Model for a Data-Free Edge Network","authors":"Kai Zeng;Hongwei Gu;Yuxiu Duan;Tao Shen;Ruidong Li","doi":"10.1109/TCCN.2025.3569598","DOIUrl":"10.1109/TCCN.2025.3569598","url":null,"abstract":"Recently, edge networks are developed to be integrated with computation and storage in a harmonious architecture to perform intelligence tasks efficiently. However, there are two major challenges in implementing smart computation in edge networks. (1) An intelligent model should be extremely lightweight, because of limited computing power at edge. (2) Data-driven model training requires the protection of security and privacy at edge. To march towards meeting those two challenges, we propose a zero-shot binary neural network (ZS-BNN), which is an ultimate compact model without data-driven training in this paper. With ZS-BNN, a slim BNN client performs data-free adversarial distillation (DFAD) training by introducing a generator and a full-precision teacher to servers. Moreover, considering the distribution deviation, two mechanisms are proposed for further optimization, where the BN constraint term is investigated for distribution rectification in generators, and a distribution alignment for student BNNs is elaborated. Based on those designs, ZS-BNNs have normal data distributions via improvement operations. As a result, binary convolution model is implemented unprecedentedly in a data-free environment and can be considered the most available solution for edge network intelligence. The experimental results show that ZS-BNN is superior to state-of-the-art binarization methods in zero-shot scenarios. The resource codes are validated at <uri>https://github.com/sjmp525/IA/tree/ZS-BNN</uri>.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"881-895"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-Shot Automatic Modulation Classification via Semi-Supervised Metric Learning and Lightweight Conv-Transformer Model 基于半监督度量学习和轻量级逆变器模型的少射自动调制分类

IF 8 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-03-28 DOI: 10.1109/TCCN.2025.3574312

Jitong Ma;Mutian Hu;Xiao Chen;Liangtian Wan;Jie Wang

{"title":"Few-Shot Automatic Modulation Classification via Semi-Supervised Metric Learning and Lightweight Conv-Transformer Model","authors":"Jitong Ma;Mutian Hu;Xiao Chen;Liangtian Wan;Jie Wang","doi":"10.1109/TCCN.2025.3574312","DOIUrl":"10.1109/TCCN.2025.3574312","url":null,"abstract":"Deep learning (DL) techniques have exhibited considerable advantages in automatic modulation classification (AMC) tasks, achieving high classification accuracy. However, most DL-based AMC methods require numerous labeled samples as training dataset, which is usually hard to obtain and maybe impossible in practical non-cooperative communications. To address this issue, a novel semi-supervised metric learning and lightweight convolutional transformer (conv-transformer) network (SMCTNet) is proposed in this paper. The proposed SMCTNet firstly adopts sample enhancement techniques to improve the diversity and number of training samples by randomly selecting a large number of subtasks to support model optimization with a small number of samples. Secondly, SMCTNet reduces the difficulty of model optimization by utilizing a parameter-free metric that directly calculates the similarity between output characteristics for classification. Further, a semi-supervised training method is proposed for SMCTNet, which generates high-confidence pseudo-labels to guide the model optimization through unlabeled data to improve the generalization and recognition accuracy. In addition, SMCTNet utilizes a lightweight conv-transformer network to extract local and global information of augmented samples, and its lightweight design effectively prevents the overfitting phenomenon when there are insufficient samples. Experimental results on an open-source dataset demonstrate that SMCTNet outperforms contrastive methods by 4.45%–37.14% in recognition accuracy when only 10 labeled samples are available.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"1012-1024"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144164934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual Intelligence: Leveraging DRL With Smart Satellites to Counter Intelligent Jamming in Satellite Networks 双重智能：利用DRL与智能卫星对抗卫星网络中的智能干扰

IF 8 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-06-05 DOI: 10.1109/TCCN.2025.3576819

Hongyuan Wang;Qiaolin Ouyang;Wang Xi;Yiyue Xiang;Neng Ye

{"title":"Dual Intelligence: Leveraging DRL With Smart Satellites to Counter Intelligent Jamming in Satellite Networks","authors":"Hongyuan Wang;Qiaolin Ouyang;Wang Xi;Yiyue Xiang;Neng Ye","doi":"10.1109/TCCN.2025.3576819","DOIUrl":"10.1109/TCCN.2025.3576819","url":null,"abstract":"Satellite networks provide a powerful solution for reliable wide-area coverage and robust communication in diverse environments. However, open ground-to-satellite links are highly vulnerable to environmental dynamics and interference, especially the prevalent intelligent jamming attack. To address these challenges, we develop a dual intelligence framework in satellite networks that enhances the random access (RA) efficiency by incorporating intelligence at both the user and satellite sides. On the user terminal (UT) side, we develop an error-attention deep deterministic policy gradient (EADDPG) algorithm that prioritizes samples with larger time-difference (TD) errors, to enhance the training efficiency and accelerate convergence in dynamic environments. On the satellite side, as environmental instability increases, the jamming effectiveness of the intelligent jammer intensifies. Therefore, we introduce the intelligent satellite model to countermeasure the effectiveness increase of jammers in dynamic environments by sending false negative acknowledgment (NACK) messages to disturb the learning process. Simulation results show that the proposed method can effectively mitigate the performance degradation caused by dynamic environments and effectively counter intelligent jamming. The EADDPG outperforms other vanilla deep reinforcement learning (DRL) algorithms in anti-jamming capability, with interference occurrences reduced by nearly 50% across various environments compared to vanilla deep reinforcement learning algorithms.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"1054-1067"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CRB-Rate Tradeoff in RSMA-Enabled Near-Field Integrated Multi-Target Sensing and Multi-User Communications 基于rsma的近场集成多目标传感和多用户通信中的crb -速率权衡

IF 8 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2026-01-01 Epub Date: 2025-07-10 DOI: 10.1109/TCCN.2025.3587818

Jiasi Zhou;Cong Zhou;Yanjing Sun;Chintha Tellambura

{"title":"CRB-Rate Tradeoff in RSMA-Enabled Near-Field Integrated Multi-Target Sensing and Multi-User Communications","authors":"Jiasi Zhou;Cong Zhou;Yanjing Sun;Chintha Tellambura","doi":"10.1109/TCCN.2025.3587818","DOIUrl":"10.1109/TCCN.2025.3587818","url":null,"abstract":"Near-field integrated sensing and communication (NF-ISAC) combines wireless communication with simultaneous angle and distance sensing. However, this dual functionality creates a complex interference environment. Existing NF-ISAC designs offer limited flexibility in managing such interference, highlighting the need for more advanced strategies. To address this, we propose a rate-splitting multiple access (RSMA) scheme for NF-ISAC, leveraging both fully and partially connected hybrid analog-digital (HAD) beamforming architectures. We derive the Cramér-Rao bound (CRB) for joint distance and angle sensing and characterize the tradeoff between the max-min communication rate and multi-target CRB. To explore the CRB-rate Pareto boundary, we formulate a sensing-centric optimization problem under communication rate constraints. For the fully connected HAD architecture, a penalty dual decomposition (PDD)-based double-loop algorithm is developed, while a two-stage approach is used to reduce complexity. This framework is also extended to the partially connected case. Simulations show that the proposed schemes achieve performance comparable to a fully digital beamformer with fewer RF chains, effectively balancing hardware efficiency and system performance. Furthermore, the schemes significantly outperform space division multiple access and far-field ISAC, delivering lower sensing error and an expanded CRB-rate region.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"3687-3700"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0