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A Universal Low-Dimensional Subspace Structure in Beamforming Design: Theory and Applications

IEEE Transactions on Signal Processing 2025年

作者： Zhao, Xiaotong Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai201804 China Shenzhen Research Institute of Big Data Shenzhen518172 China

Beamforming design plays a crucial role in multi-antenna systems, with numerous methods proposed to optimize key performance metrics such as spectral efficiency and power consumption. However, these methods often face two major challenges: high computational complexity and excessive communication overhead in distributed implementations. This paper addresses these challenges by analyzing a general beamforming optimization framework—referred to as the standard-form beam-forming problem—which encompasses various beamforming design tasks. We prove that any positive stationary point of this problem exhibits a low-dimensional subspace (LDS) structure, enabling the development of low-complexity and communication-efficient beamforming algorithms. As an illustrative example, we leverage the LDS structure to propose a computationally efficient beamforming algorithm for weighted sum rate maximization in coordinated multi-cell systems, with provable convergence to stationary points. Furthermore, we decentralize the algorithm for distributed coordinated beamforming, ensuring low interaction costs independent of the number of base station antennas. Notably, the proposed LDS structure is broadly applicable to a wide range of beamforming problems, including integrated sensing and communication (ISAC), intelligent reflecting surfaces (IRS), and beyond. Extensive numerical simulations validate the effectiveness and versatility of our approach, particularly the general applicability of the LDS structure. © 1991-2012 IEEE.

关键词： Multibeam antennas

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Improving hierarchical graph pooling with information bottleneck

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Journal of King Saud University - computer and Information sciences 2025年第3期37卷 1-15页

作者： Long, Jun Wang, Zidong Chen, Tingxuan Yang, Liu Big Data Institute Central South University Changsha 410083 China School of Computer Science and Engineering Central South University Changsha 410083 China

Hierarchical graph pooling has received significant attention in recent years due to its capability to capture hierarchical structures and deliver superior performance on graph-level tasks. However, most existing methods design pooling layers empirically and lack a unified theoretical framework. Consequently, the coarsened graphs generated by these methods frequently contain redundant information, leading to significant semantic bias. In this paper, we propose the principle of “minimal sufficient coarsened graph” to precisely define the desired properties of coarsened graphs. Guided by this principle, we introduce a novel Graph Pooling Information Bottleneck (GPIB) for hierarchical graph pooling. GPIB aims to achieve the desired attributes of minimality and sufficiency in coarsened graphs. Specifically, we first incorporate node degree properties to enrich node representations and introduce a dynamic generation mechanism. This mechanism regulates the information flow, ensuring substantial auxiliary knowledge for downstream tasks and enhancing information sufficiency. Then, we introduce a parameter-learning noise distribution, forcing the elimination of redundant information and thus, facilitating minimality and robustness in our model. Remarkably, we theoretically demonstrate that imposing constraints only on the coarsened graph produced by the final pooling layer equates to constraining all coarsened graphs. This insight simplifies the constraint procedure while ensuring its effectiveness. Extensive experiments on six real-world datasets validate the effectiveness of our proposed model, which consistently outperforms the baseline methods and produces high-quality coarsened graphs. © The Author(s) 2025.

关键词： Graph classification Graph neural networks Graph pooling Graph representation learning

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FLEKE: Federated Locate-then-Edit Knowledge Editing

arXiv

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arXiv 2025年

作者： Zhao, Zongkai Xu, Guozeng Li, Xiuhua Wei, Kaiwen Zhong, Jiang School of Big Data & Software Engineering Chongqing University China College of Computer Science Chongqing University China

Locate-then-Edit Knowledge Editing (LEKE) is a key technique for updating large language models (LLMs) without full retraining. However, existing methods assume a single-user setting and become inefficient in real-world multi-client scenarios, where decentralized organizations (e.g., hospitals, financial institutions) independently update overlapping knowledge, leading to redundant mediator knowledge vector (MKV) computations and privacy concerns. To address these challenges, we introduce Federated Locate-then-Edit Knowledge Editing (FLEKE), a novel task that enables multiple clients to collaboratively perform LEKE while preserving privacy and reducing computational overhead. To achieve this, we propose FedEdit, a two-stage framework that optimizes MKV selection and reuse. In the first stage, clients locally apply LEKE and upload the computed MKVs. In the second stage, rather than relying solely on server-based MKV sharing, FLEKE allows clients retrieve relevant MKVs based on cosine similarity, enabling knowledge re-edit and minimizing redundant computations. Experimental results on two benchmark datasets demonstrate that FedEdit retains over 96% of the performance of non-federated LEKE while significantly outperforming a FedAvg-based baseline by approximately twofold. Besides, we find that MEMIT performs more consistently than PMET in the FLEKE task with our FedEdit framework. Our code is available at https://***/zongkaiz/FLEKE. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Hierarchical Optimization for Cross-Regional Planning and Scheduling of Hydrogen Energy Storage Systems Considering Uncertainty

Hierarchical Optimization for Cross-Regional Planning and Sc...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Tang, Zihang Wang, Yingxiao Liu, Yuqian Wu, Jianghua School of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 China Department of Mathematics Nanjing University Nanjing210023 China Shenzhen Research Institute of Big Data the Chinese University of Hong Kong-Shenzhen Guangdong Shenzhen518172 China

ISBN: (纸本)9789819620418

Cross-regional Hydrogen Energy Storage System (HESS) effectively addresses the uneven spatial and temporal distribution of renewable energy sources by facilitating energy storage, transfer, and scheduling across regions. However, the intermittency of renewable energy sources, dynamic changes in demand, and the high costs pose complex challenges for the planning and scheduling of cross-regional HESSs. Although hierarchical modeling, which is divided into upper-level planning and lower-level scheduling, provides flexibility and robustness in handling uncertainties, the difficulties of high model complexity and low solution efficiency still exist. To this end, this paper improves hierarchical modeling by introducing the concept of dynamic transmission price into HESS, which quantifies the difficulty of transferring stored energy to neighboring cities. This concept then guides the energy trading decisions and the power distribution strategies among HESS, eliminating the need to consider the discrete variable of cross-regional connections in the lower-level scheduling for the significant simplification of the solution process. In addition, this paper synchronously integrates simulated annealing and linear programming algorithms as an efficient optimization approach for this novel hierarchical modeling. A case study considering 50 cities demonstrates the feasibility of the hierarchical model and the effectiveness of the optimization approach, providing strong support for the large-scale consumption and absorption of renewable energy across regions. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Mixed-integer linear programming

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Image manipulation localization via semantic-guided feature enhancement and deep multi-scale edge supervision

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Neurocomputing 2025年 639卷

作者： Wang, Haichou Cheng, Hang Chen, Yun Xu, Yongliang Wang, Meiqing School of Mathematics and Statistics Fuzhou University Fuzhou350108 China College of Computer Science and Big Data Fuzhou University Fuzhou350108 China

With the widespread application of image editing software, image manipulation localization has become a focal point and promising research. Existing neural networks for image manipulation primarily rely on RGB and noise features to accurately identify tampered areas within images. However, in practical image manipulation localization tasks, noise features extracted from RGB images alone are often insufficient to effectively address tampering issues. Furthermore, existing encoder–decoder models for image manipulated localization often overlook the direct interactions between different layers during the decoding process, which hinders the effective transfer of deep semantic information to shallow features, thereby impacting the ability to accurately identify manipulated areas. To address the challenges previously identified, this paper presents a dynamically adaptive noise extraction module and achieves inter-layer information exchange in the decoder by fusing output features from different layers to extract edge information. We adaptively map RGB images to an appropriate color space using linear transformations and then extract noise features, leveraging the differences in color blocks to effectively uncover features of tampering. In addition, we integrate features across multiple decoder layers, employ deep multi-scale edge supervision to impose constraints, and introduce a dynamic ringed residual module to further enhance feature representation. Extensive experiments demonstrate that our approach achieves competitive results on diverse large-scale image datasets, exhibiting superior precision and robustness compared with most state-of-the-art methods. © 2025

关键词： Linear transformations

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An Adaptive DoH Encrypted Tunnel Detection Method Based on Contrastive Learning

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IEEE Internet of Things Journal 2025年

作者： Tong, Jiacheng Zhao, Yilin Jin, Chongju Chen, Wei Zhang, Yiting Wu, Lifa Nanjing University of Posts and Telecommunications School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210003 China

The percentage of encrypted network traffic has constantly increased as network security has been continuously improved. Attackers can, however, utilize encrypted DNS over HTTPS (DoH) to conceal their malicious traffic, which makes it more difficult to identify malicious tunnels. To address this issue, we first examine the encryption features of DoH tunnel traffic. Due to the incapability of current detection techniques to properly fuse traffic attributes, a fusion learning-based method is proposed to detect DoH encrypted tunnel traffic. At the same time, we discover that the DoH traffic samples may exhibit concept drift. As a result, we present a concept drift detection approach based on a contrastive sparse autoencoder. In addition to the above method, a model retraining strategy is also suggested to improve the model’s capacity to identify new DoH encrypted tunnel traffic while reducing its reliance on expert label data. This strategy involves incrementally training the model using as few samples as possible. Experiments demonstrate that the proposed method can significantly enhance detection performance. When 7% of drift samples are used during incremental training, the detection accuracy of the model recovers from 74.02% to 99.96%. © 2014 IEEE.

关键词： Contrastive Learning

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DMC-Watermark: A backdoor richer watermark for dual identity verification by dynamic mask covering

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Applied Intelligence 2025年第8期55卷 1-15页

作者： Zhu, Yujia Wang, Ruoxi Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Key Laboratory of Advanced Computing Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

With the increasing use of neural networks, the importance of copyright protection for these models has gained significant attention. Backdoor watermarking is one of the key methods for protecting copyright. However, on the one hand, most existing backdoor watermarks are triggered by visual images, making them easily detectable, and therefore vulnerable to various attacks. On the other hand, it is difficult for these methods to carry information related to the creator’s identity which can easily lead to fraudulent claims of ownership. These factors contribute to the vulnerability and limitations of backdoor watermarking. In this paper, we propose DMC-Watermark, a backdoor richer watermarking method that uses dynamic mask-covered image structures as triggers. Leveraging the semantic preservation of image structure in transformation attacks, we select image structure as triggers. Furthermore, we convert the author-related information into an array of color information and apply it as a mask to the extracted image structures, allowing it to serve as a second layer of verification during the validation phase to resist fraudulent claims of ownership. The final trigger pattern, embedded with author-related image structures, is applied to the selected images in the trigger set, generating a final trigger set that is trained together with clean samples to produce a protected model. The experiments show that the proposed DMC-Watermark performs well in terms of fidelity, invisibility, undetectability, functionality, dual verification and robustness on three different datasets and four representative DNNs, and it has wide applicability and excellent results in high-resolution images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image watermarking

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CPAKE: Dynamic Batch Authenticated Key Exchange with Conditional Privacy 24th

CPAKE: Dynamic Batch Authenticated Key Exchange with Condit...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xiang, Axin Tian, Youliang Xiong, Jinbo Ying, Zuobin Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guiyang550025 China Industrial School of Joint Innovation Quanzhou Vocational and Technical University Quanzhou362268 China Faculty of Data Science City University of Macau 999078 China

ISBN: (纸本)9789819615247

Authenticated key exchange (AKE) needs to be designed for realizing point-to-multipoint secure communications in blockchain networks (BNet). However, since BNet is open, untrusted and decentralized, traditional certificateless AKE schemes are difficult to overcome two technical bottlenecks, i.e., private distribution of partial keys and accurate identification of anonymous traitors. This paper proposes a conditionally private and dynamic batch AKE scheme (CPAKE) in BNet. We first develop a privacy-preserving certificateless key generation algorithm to prevent the disclosure of nodes’ privacy during partial key distribution. Then, we design a simple anonymous traitor tracing mechanism against Byzantine adversaries to accurately identify and trace the traitor’s identity. Finally, we build a dynamic batch authentication protocol with decentralized node management to ensure the final validity of honest nodes. Moreover, performance analysis indicates that our scheme reduces the signing and batch-verifying time by approximately 193.10 ms and 10.93 ms respectively over state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Glare-SNet: Unsupervised Glare Suppression Balance Network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Interference-Based Reliability and Capacity Analysis for IEEE 802.11 Broadcast Ad-Hoc Networks on the Highway 11

Interference-Based Reliability and Capacity Analysis for IEE...

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11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Li, Zhijuan Wu, Xintong Li, Xiaokun Ma, Xiaomin Department of Computer and Big Data Heilongjiang University Harbin150080 China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd. Harbin150090 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China College of Science and Engineering Oral Roberts University TulsaOK74171 United States

ISBN: (纸本)9789897587450

Interference is a critical factor that degrades wireless network performance. In IEEE 802.11 wireless broadcast networks, hidden terminals and concurrent transmissions are the primary sources of interference due to the carrier sense multiple access (CSMA) mechanism. Therefore, analyzing the signal-to-interference-plus-noise ratio (SINR) distribution is essential for evaluating network performance, whose derivation relates to the transmission probabilities of hidden terminals and concurrent transmissions. In this study, we utilize the existing semi-markov process (SMP) model to calculate these transmission probabilities. Subsequently, we employ the Laplace transform to analyze the SINR distribution in IEEE 802.11 broadcast ad-hoc networks on highways. Based on the derived SINR distribution, we further evaluate the reliability and capacity. This approach can be readily extended to two-dimensional (2D) or three-dimensional (3D) scenarios by employing d-dimensional (1 ≤ d ≤ 3, dD) point process. Experimental results demonstrate that the proposed model achieves high accuracy under small to medium interference ranges. Additionally, the analysis remains highly accurate for receivers within 70 meters, even in scenarios with large interference ranges. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Markov processes

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