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Distributed Signal Processing for Extremely Large-Scale Antenna Array Systems: State-of-the-Art and Future Directions

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IEEE Journal on Selected Topics in Signal Processing 2025年第2期19卷 304-330页

作者： Xu, Yanqing Larsson, Erik G. Jorswieck, Eduard A. Li, Xiao Jin, Shi Chang, Tsung-Hui The Chinese University of HongKong School of Science and Engineering Shenzhen518172 China Linköping University Department of Electrical Engineering Linköping581 83 Sweden Technical University of Braunschweig Institute for Communications Technology Braunschweig38106 Germany Southeast University National Mobile Communication Research Laboratory Nanjing210096 China The Chinese University of HongKong School of Artificial Intelligence Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

Extremely large-scale antenna arrays (ELAA) play a critical role in enabling the functionalities of next generation wireless communication systems. However, as the number of antennas increases, ELAA systems face significant bottlenecks, such as excessive interconnection costs and high computational complexity. Efficient distributed signal processing (SP) algorithms show great promise in overcoming these challenges. In this paper, we provide a comprehensive overview of distributed SP algorithms for ELAA systems, tailored to address these bottlenecks. We start by presenting three representative forms of ELAA systems: single-base station ELAA systems, coordinated distributed antenna systems, and ELAA systems integrated with emerging technologies. For each form, we review the associated distributed SP algorithms in the literature. Additionally, we outline several important future research directions that are essential for improving the performance and practicality of ELAA systems. © 2025 IEEE.

关键词： Antenna arrays

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Low-Complexity Reflecting Elements Selection for RIS-Aided Multiuser MISO Communication

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IEEE Internet of Things Journal 2025年第11期12卷 18419-18422页

作者： Baojuan Liu Songjie Yang Wanting Lyu Chadi Assi Ya Li Zhongpei Zhang School of Computer Science and Engineering Macau University of Science and Technology Macau China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China Concordia Institute for Information Systems Engineering Concordia University Montreal QC Canada Future Research Laboratory China Mobile Research Institute Beijing China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

Effective elements selection (ES) is essential to the deployment of reconfigurable intelligent surface (RIS), which, however, receives little attention. In this letter, we propose two novel ES strategies intended for deployment in RIS-assisted multiuser multiple-input-single-output (MISO) wireless networks. The first scheme is designed to maximize the effective gains of channels, while the second ES scheme presents a linear swapping selection (LSS) method that focuses on optimizing the total achievable rate. Numerical results show that the second scheme using the LSS method performs better than the first one, and is able to achieve a near-optimal performance but with significantly reduced computational complexity compared with the optimal exhaustive search scheme.

关键词： Array signal processing Wireless networks Reconfigurable intelligent surfaces MISO Internet of Things Multiuser channels Computational complexity

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Advancing Underwater Image Enhancement Using Hybrid Deep Learning Models

Advancing Underwater Image Enhancement Using Hybrid Deep Lea...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Jain, Upma Boob, Nandini Shirish Anandhi, R.J. Sehgal, Archana Bhavana, Munugapati Kumar, Yogendra Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India

ISBN: (纸本)9798331512965

When attempting to make underwater images better, there are a lot of obstacles to overcome, including water's inherent color distortion, inadequate visibility, and light absorption and dispersion noise. The difficulties in accurately analyzing images caused by these problems apply to marine research, underwater robots, and environmental monitoring are only a few areas which are impacted. To address the limitations of existing picture enhancement techniques, this paper proposes a new hybrid deep learning CNN + GAN model for enhancing underwater photographs. The hybrid model extracts features with GANs and noise reduces with CNNs to produce better photos with better clarity and color correction. I train and validate the model using an underwater picture dataset and it defeats the state of the art approaches on visibility, color correctness, contrast and contrast. In quantitative and qualitative assessments our hybrid deep learning method yields superior generalization in different undersea environments relative to traditional methods. A suggested approach can potentially improve the underwater photography, marine setting data collecting with more precision, as well as better underwater exploration. This work now provides confidence in research progress into autonomous underwater exploration systems and underwater image processing. © 2025 IEEE.

关键词： Convolutional neural networks

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LLM-KT: Aligning Large Language Models with Knowledge Tracing using a Plug-and-Play Instruction

arXiv

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arXiv 2025年

作者： Wang, Ziwei Zhou, Jie Chen, Qin Zhang, Min Jiang, Bo Zhou, Aimin Bai, Qinchun He, Liang School of Computer Science and Technology East China Normal University China Shanghai Open University China

The knowledge tracing (KT) problem is an extremely important topic in personalized education, which aims to predict whether students can correctly answer the next question based on their past question-answer records. Prior work on this task mainly focused on learning the sequence of behaviors based on the IDs or textual information. However, these studies usually fail to capture students’ sufficient behavioral patterns without reasoning with rich world knowledge about questions. In this paper, we propose a large language models (LLMs)-based framework for KT, named LLM-KT, to integrate the strengths of LLMs and traditional sequence interaction models. For task-level alignment, we design Plug-and-Play instruction to align LLMs with KT, leveraging LLMs’ rich knowledge and powerful reasoning capacity. For modality-level alignment, we design the plug-in context and sequence to integrate multiple modalities learned by traditional methods. To capture the long context of history records, we present a plug-in context to flexibly insert the compressed context embedding into LLMs using question-specific and concept-specific tokens. Furthermore, we introduce a plug-in sequence to enhance LLMs with sequence interaction behavior representation learned by traditional sequence models using a sequence adapter. Extensive experiments show that LLM-KT obtains state-of-the-art performance on four typical datasets by comparing it with approximately 20 strong baselines. © 2025, CC BY-NC-SA.

关键词： Students

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HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation

arXiv

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arXiv 2025年

作者： Wang, Jinze Zhang, Tiehua Zhang, Lu Bai, Yang Li, Xin Jin, Jiong School of Engineering Swinburne University of Technology Australia School of Computer Science and Technology Tongji University China School of Cybersecurity Chengdu University of Information Technology China YSTC Energy Pty Ltd Australia

Next Point-of-Interest (POI) recommendation aims to predict users’ next locations by leveraging historical check-in sequences. Although existing methods have shown promising results, they often struggle to capture complex high-order relationships and effectively adapt to diverse user behaviors, particularly when addressing the cold-start issue. To address these challenges, we propose Hypergraph-enhanced Meta-learning Adaptive Network (HyperMAN), a novel framework that integrates heterogeneous hypergraph modeling with a difficulty-aware meta-learning mechanism for next POI recommendation. Specifically, three types of heterogeneous hyperedges are designed to capture high-order relationships: user visit behaviors at specific times (Temporal behavioral hyperedge), spatial correlations among POIs (spatial functional hyperedge), and user long-term preferences (user preference hyperedge). Furthermore, a diversity-aware meta-learning mechanism is introduced to dynamically adjust learning strategies, considering users behavioral diversity. Extensive experiments on real-world datasets demonstrate that HyperMAN achieves superior performance, effectively addressing cold start challenges and significantly enhancing recommendation accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Deep Learning-Based Object Recognition in AR-VR Environment

Deep Learning-Based Object Recognition in AR-VR Environment

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Sharma, Suchi Diwakar, Manoj Kumar, Priyansh Joshi, Aditya Singh, Prabhishek Mehra, Gunjan Graphic Era Deemed to be University Department of Cse Dehradun India Graphic Era Deemed to be University Department of Computer Applications Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798331527495

Artificial intelligence (AI), AR-VR (Augmented Reality and Virtual Reality) have recently made significant strides, paving the way for innovation and change in a wide range of fields and industries. The quick advancements in AR-VR and computer vision (CV) made it easier to comprehend and analyze the surroundings. In this study, works that combined augmented, virtual reality (AR-VR) and deep learning for object recognition throughout the recent years are thoroughly reviewed and presented. The three primary focuses of this paper are: 1) List out the object identification techniques used in AR-VR in detail, 2) Describe the techniques, tools, and frameworks that are being utilized to create various types of AR- VR object detection, 3) Discussing object detection in AR-VR in detail and 4) Concluding the problems and identifying possible study fields in the AR-VR environment. © 2025 IEEE.

关键词： Deep learning

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Multi-Scale Cascaded with Cross-Attention Network-Based Deformation Vector Field Estimation for Motion-Compensated 4D-CBCT Reconstruction

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 717-731页

作者： Yuan, Peng Lyu, Fei Gao, Zhiqiang Yang, Chunfeng Hu, Dianlin Zhu, Jian Wu, Zhan Lyu, Tianling Zhao, Wei Dong, Jianmin Chen, Yang Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Ministry of Education Southeast University Nanjing210096 China Laboratory of Image Science and Technology Southeast University Nanjing210096 China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China School of Physics Beihang University Beijing China Beihang Hangzhou Innovation Institute Zhejiang Hangzhou China School of Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan Shandong China School of Information Engineering Xizang Minzu University Xianyang712082 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology The School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China

Four-Dimensional Cone Beam Computed Tomography (4D-CBCT) imaging technology offers enhanced image quality and spatial resolution for intraoperative guidance, facilitating real-time tracking of tumor position changes during radiotherapy. However, this is still a task of great challenges due to insufficient projections at each respiratory phase after phase-sorting, and the image phases reconstructed directly from phase-sorted data are discrete and discontinuous. To generate high-quality 4D-CBCT deformation vector fields (DVFs), this paper leverages the preoperative static prior image to guide intraoperative dynamic sparse-view reconstruction images for reducing anatomical structure differences, ultimately achieving continuous and dynamic 4D-CBCT imaging. In this paper, we propose a Multi-scale Cascaded residual deformable vector field estimation framework based on Cross-attention in Motioncompensated 4D-CBCT reconstruction (MCCM), which combines Multi-Scale Cascaded residual registration network (MSCNet), Cross-Attention Enhanced feature Fusion (CAEF) module and Structure-Enhanced Motion-Compensated (SEMC) module: 1) the MCCM employs a multi-scale cascaded residual network strategy, merging multi-receptive fields and multi-resolution feature maps for large-scale internal changes. 2) the CAEF is embedded into MSC-Net to facilitate effective communication and learning between features and promote the flow of information. 3) the SEMC is developed to reduce artifacts after intraoperative CBCT sparse-view reconstruction, restore global lung motion changes and local details, and enhance structural information through image fusion. The proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Our approach exhibits significant improvements across various evaluation metrics. © 2024 IEEE.

关键词： Image fusion

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Enhanced Physical Layer Security for Full-Duplex Facultative Symbiotic Radio: A Pattern Switching and Multi-Device Scheduling Strategy

Enhanced Physical Layer Security for Full-Duplex Facultative...

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IEEE Conference on Wireless Communications and Networking

作者： Chi Jin Zheng Chang Fengye Hu Mingan Luan Timo Hämäläinen Faculty of Information Technology University of Jyväskylä Finland School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Communication Engineering Jilin University China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Physical layer security (PLS) in symbiotic radio (SR) systems is primarily considered for passive eavesdropping scenarios. However, overlooking the impact of proactive eavesdroppers poses significant risks. In this paper, we focus on secure transmission in SR systems under proactive eavesdropping conditions. A PLS strategy is investigated for a full-duplex facultative symbiotic radio (FD-FSR) system. First, we introduce an innovative FSR protocol. It allows backscatter devices (BDs) to dynamically switch between cognitive and symbiotic patterns. Next, we develop a multi-device scheduling method. It adaptively assigns BDs as transmitters, cooperators, or jammers to enhance the secrecy rate. We formulate the pattern switching and BD scheduling as a mixed integer programming problem (MIP). To solve this, we first decompose it into binary decision-making and multi-variable optimization sub-problems. Then, a low-complexity two-stage optimization strategy is employed. Numerical results demonstrate that our proposed strategy significantly outperforms existing schemes.

关键词： Symbiosis Decision making Switches Full-duplex system Physical layer security Security Jamming Optimization Eavesdropping Backscatter

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RIS-Assisted Joint Waveform Design for PAPR-Aware UAV-Enabled Integrated Sensing and Communication

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IEEE Transactions on Vehicular technology 2025年

作者： Gu, Meng Liu, Yaxi He, Boxin Huangfu, Wei Long, Keping University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services School of Computer and Communication Engineering Beijing100083 China University of Science and Technology Beijing Shunde Innovation School China

Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV)-enabled Integrated Sensing and Communication (ISAC) systems have received wide attention due to flexible mobility of UAVs and auxiliary links provided by RIS. However, the existing works usually neglect the peak-to-average power ratio (PAPR), a key communication performance metric that reflects amplifier efficiency, interference levels, and equipment costs. Motivated by this, we consider PAPR constraints in RIS-assisted UAV-enabled ISAC system. An optimization for joint beamforming and RIS phase-shift matrices design is established, with the goal of minimizing the weighted sum of the multiuser interference (MUI) for communication and sensing beam pattern mismatch (BPM), subject to PAPR constraints, power constraints, ideal beam direction approximation constraints, and constant mode constraints on the phase-shift matrices. To tackle this non-convex problem, a multivariate cyclic iterative alternating-direction multiplier method (MCI-ADMM) is adopted to decouple the original problem into three sub-problems regarding three variables. Some mathematical techniques are adopted to solve these sub-problems, including augmented lagrangian, constraint reconstruction method based on orthogonal Procrustes problem, manifold optimization, quadratic representation, and variable substitution. The experimental results show inherent trade-offs between dual functionalities under PAPR constraints. In addition, the consideration of PAPR performs better system performance compared with baselines. Also, the stability and reliability under different parameters settings are verified. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Soot emission reduction via magnetic field coupling with carbon dioxide in diffusion flames

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Energy 2025年 328卷

作者： Yang, Kaixuan Ying, Yaoyao Qi, Dandan Yu, Runtian Chen, Chen Chen, Mingxiao Yan, Weijie Yan, Jianhua Liu, Dong MIIT Key Laboratory of Thermal Control of Electronic Equipment School of Energy and Power Engineering Nanjing University of Science and Technology Nanjing210094 China Advanced Combustion Laboratory School of Energy and Power Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Clean Energy Utilization College of Energy Engineering Zhejiang University Hangzhou310027 China

Considering that soot particles emitted from industrial combustion equipment were substantial contributors to global warming and human health issue, the reduction of soot emission via magnetic field coupled with carbon dioxide (CO2) was explored in this study. As high as an 85 % reduction in soot mass emissions from exhaust gas of inverse diffusion flames could be achieved by introducing a 2T magnetic field and substituting nitrogen (N2) with CO2 as the dilution gas, indicating that magnetic fields could effectively synergize with CO2 to reduce soot emission. Furthermore, to gain insight into the principles of soot reduction, the soot nanostructure and graphitization degree was investigated using the high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy. It was discovered that the nanostructure of soot particles became more disordered and exhibited a lower graphitic degree in the presence of magnetic field and CO2, leading to higher oxidation activity. This finding suggested that magnetic fields and CO2 addition inhibited soot formation primarily through suppressing the polycyclic aromatic hydrocarbon (PAHs) nucleation and soot surface growth. Given its excellent performance in reducing soot emissions, magnetic field synergistic CO2 was a promising strategy for developing cleaner combustion devices. © 2025 Elsevier Ltd

关键词： Magnetic levitation

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