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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Binyan Ren, Ao Zhang, Zihao Duan, Moming Liu, Duo Tan, Yujuan Zhong, Kan College of Computer Science Chongqing University Chongqing China School of Big Data Software & Engineering Chongqing University Chongqing China Institute of Data Science National University of Singapore Singapore

ISBN: (纸本)9798350368741

With the rapid development of social media, sentiment analysis from multimodal posts has garnered significant attention in recent years. However, the substantial size of these models impedes their deployment on resource-constrained embedded devices. Although pruning has been extensively studied to reduce the size of unimodal models, specific challenges remain for Multimodal Sentiment Analysis (MSA) models. First, existing techniques prune fixed original models into sparse models, while our findings indicate that different model architectures of identical size yield varying performance outcomes. Second, prior studies fail to explore the unique characteristics of MSA models, resulting in suboptimal pruning performance. To address these challenges, we propose MPNAS, a unified pruning framework via Neural Architecture Search (NAS) for MSA models. Specifically, we formulate pruning as a NAS problem and analyze MSA model characteristics to guide the subnet search. We conduct an initial coarse-grained NAS on the original model, expanding the search space slightly to identify suitable subnets that enhance pruning rates and accuracy. Subsequently, we refine coarse-grained subnets in a fine-grained NAS stage, where MSA model characteristics guide the search process. Extensive experiments on three representative datasets demonstrate the superiority of our approach over existing methods. © 2025 IEEE.

关键词： Multimodal Compression NAS

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GNN-based Structured Bayesian Inference for Multi-grid Localized Statistical Channel Modeling

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IEEE Transactions on Wireless Communications 2025年

作者： Wang, Yiheng Xue, Ye Zhang, Shutao Chang, Tsung-Hui The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Data Science Guangdong Shenzhen518172 China The Chinese University of Hong Kong-Shenzhen Shenzhen Research Institute of Big Data School of Science and Engineering Guangdong518172 China

Localized statistical channel modeling (LSCM) is an efficient channel modeling framework recently proposed for wireless network optimization which learns the angular power spectrum (APS) of the downlink channel from the beam-wise reference signal receiving power (RSRP). However, the conventional LSCM is only based on RSRP measurements from one single geographical grid and ignores the inherent property of spatial consistency over wireless channels, resulting in suboptimal performance. To this end, we consider the LSCM in a manner of multiple geographical grids and further propose a novel graph-based approach for the multi-grid LSCM, called the accelerated Markovian variational Bayesian graph neural network (AMVB-GNN). The AMVB-GNN leverages a heterogeneous Markovian graph representation to capture the structured sparsity in the channel APSs and employs refined variational Bayesian inference (VBI) to learn the APSs of multiple grids. Notably, the design of AMVB-GNN eliminates the exact matrix inversion operations required in conventional VBI, thereby enhancing computational efficiency. Additionally, we demonstrate the partial permutation equivalence of AMVB-GNN, ensuring both interpretability and reliability. To address the issue of the demand for ground-truth APSs labels, we propose an unsupervised training loss function. Extensive simulation experiments validate the effectiveness and efficiency of the proposed AMVB-GNN model. © 2002-2012 IEEE.

关键词： Graph neural networks

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Two efficient beamforming methods for hybrid IRS-aided AF relay wireless networks

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science China(Information sciences) 2025年第4期68卷 363-379页

作者： Xuehui WANG Qingbo LI Wen ZHU Feng SHU Mengxing HUANG Fuhui ZHOU Riqing CHEN Cunhua PAN Yongpeng WU Jiangzhou WANG School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University School of Information and Communication Engineering Hainan University Collaborative Innovation Center of Information Technology Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Digital Fujian Institute of Big Data for Agriculture Fujian Agriculture and Forestry University National Mobile Communications Research Laboratory Southeast University Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University School of Engineering University of Kent

Owing to its ability to mitigate the double-fading effect by amplifying the reflected signal, the active intelligent reflecting surface(IRS) has garnered significant attention. In this paper, an amplify-and-forward(AF) relay network assisted by a hybrid IRS consisting of both passive and active units is developed. A signal-to-noise ratio(SNR) maximization problem is formulated, where the AF relay beamforming matrix and the hybrid IRS reflecting coefficient matrices for two-time slots need to be optimized. To address the SNR maximization problem, this paper proposes both a high-performance(HP) method and a low-complexity(LC) method. The HP method is based on the semidefinite relaxation and fractional programming(SDR-FP)algorithm, with rank-1 solutions obtained through Gaussian randomization. For the LC method, the amplification coefficient of each active IRS element is assumed to be equal. The SNR maximization problem is then addressed using the whitening filter,generalized power iteration, and generalized Rayleigh-Ritz(WF-GPI-GRR) approach. Simulation results show that compared with the benchmarks, such as the passive IRS-aided AF relay network, the proposed HP-SDR-FP and WF-GPI-GRR methods achieve significant rate improvements. In particular, the HP-SDR-FP and WF-GPI-GRR methods yield more than a 135.0%rate gain when the transmit power Ps of the source is 10 dBm. Furthermore, the proposed HP-SDR-FP method outperforms the WF-GPI-GRR method in terms of rate performance.

关键词： double-fading intelligent reflecting surface active elements passive elements hybrid IRS AF relay

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A Novel Community Detection Algorithm Based on Node Propagation and Attraction

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IEEE Access 2025年 13卷 69161-69174页

作者： Fu, Lidong Xie, Xinxing Dou, Zengfa Bai, Yun Zhao, Xi Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710600 China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China Xi'an University School of Information Engineering Xi'an710065 China Xi'an Big Data Service Center Xi'an710007 China

High-quality community detection in complex networks typically relies on the similarity between nodes to select communities. However, when a node exhibits the same similarity with different communities, it becomes ambiguous to determining the optimal community assignment for that node, potentially leading to incorrect predictions and affecting the accuracy of community assignments for other nodes. To address this problem, this paper proposes a novel community detection algorithm based on node propagation and attraction. The algorithm first introduces a node propagation strategy, which identifies high-propagation nodes based on their connectivity and core attributes. These high-propagation nodes form the initial backbone and framework of communities, minimizing the interference of low-propagation nodes on community structure. Subsequently, an attraction definition is introduced, which starts from high-propagation nodes to select partitioning methods based on their relationships with neighboring nodes, using the attraction among these nodes facilitate community organization and propagation, aiming to enhance community quality. Finally, in the community optimization phase, the algorithm effectively improves the accuracy of partitions that result from similar attractions by considering the association degree between nodes and communities. Experimental results demonstrate that, on both synthetic and real networks, the proposed algorithm outperforms various existing community detection algorithms. © 2013 IEEE.

关键词： Optimization algorithms

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Cost-Effective Edge data Caching with Failure Tolerance and Popularity Awareness

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5357-5369页

作者： Luo, Ruikun Zhang, Zujia He, Qiang Xu, Mengxi Chen, Feifei Dai, Xiaohai Wu, Song Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology China Deakin University School of Information Technology GeelongVIC3125 Australia

In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existing edge data caching (EDC) methods prioritize popular data and aim to minimize users' data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enable uncertainty-aware edge data caching (uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Fast Fractional Programming for Multi-Cell Integrated Sensing and Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Chen, Yannan Feng, Yi Li, Xiaoyang Zhao, Licheng Shen, Kaiming School of Science and Engineering Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

This paper concerns the coordinate multi-cell beamforming design for integrated sensing and communications (ISAC). In particular, we assume that each base station (BS) has massive antennas. The optimization objective is to maximize a weighted sum of the data rates (for communications) and the Fisher information (for sensing). We first show that the conventional beamforming method for the multiple-input multiple-output (MIMO) transmission, i.e., the weighted minimum mean square error (WMMSE) algorithm, works for the ISAC problem case from a fractional programming (FP) perspective. However, the WMMSE algorithm frequently requires computing the N×N matrix inverse, where N is the number of transmit or receive antennas, so the algorithm becomes quite costly when antennas are massively deployed. To address this issue, we develop a nonhomogeneous bound and use it in conjunction with the FP technique to solve the ISAC beamforming problem without the need to invert any large matrices. It is further shown that the resulting new FP algorithm has an intimate connection with gradient projection, based on which we can accelerate the convergence via Nesterov’s gradient extrapolation. © 2002-2012 IEEE.

关键词： Mean square error

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SSMamba: Superpixel Segmentation with Mamba

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IEEE Signal Processing Letters 2025年 32卷 1715-1719页

作者： Jia, Xiaohong Li, Yonghui Jiao, Jianjun Zhao, Yao Xia, Zhiwei Lanzhou Jiaotong University School of Electronic and Information Engineering Lanzhou730070 China Key Laboratory of Big Data and Artiffcial Intelligence in Transportation Beijing100044 China Beijing Jiaotong University Institute of Information Science Beijing100044 China

Deep convolutional networks have achieved remarkable success in superpixel segmentation. However, they only focus on local features ignoring global attributes. The visual Mamba demonstrates an exceptional capability to capture long-range dependencies and offers a lower computational cost compared to the Transformer. Building on this inspiration, we propose a novel superpixel segmentation with Mamba, termed SSMamba. In SSMamba, Mamba is integrated into a global-local architecture, enabling efficient interaction between global attributes and local features to produce high-quality superpixels. The designed activation function further enhances the effectiveness of SSMamba. Extensive experiments on four public datasets demonstrate that SSMamba outperforms existing state-of-the-art methods, achieving competitive average values of ASA=0.9541, BR=0.8768, BP=0.2124, UE=0.0910, and CO=0.3698. Source code is available at https://***/jiaxhm/SSMamba. © 1994-2012 IEEE.

关键词： Convolutional neural networks

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Low Profile, High Aperture Efficiency Magneto-Electric Dipole Circularly Polarized Beam Scanning Antenna Array

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IEEE Antennas and Wireless Propagation Letters 2025年第5期24卷 1094-1098页

作者： Wang, Chao Xu, Guanghui Meng, Yanfeng Yan, Jiaxin Xie, Guoda Chen, Yi Wang, Wei Zhao, Luyu Li, Minquan Li, Yingsong Huang, Zhixiang Anhui University The Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits Information Materials and Intelligent Sensing Laboratory of Anhui Province Center for Big Data and Population Health of lHM Hefei230601 China East China Research Institute of Electronic Engineering Hefei230088 China

In this letter, a low-profile, high-aperture-efficiency millimeter-wave magneto-electric dipole (MED) circularly polarized (CP) beam-scanning antenna array is proposed. Initially, a novel MED antenna element is designed by arranging metallized vias around the radiating patches to serve as beam directors. This novel method not only provides broad axial-ratio (AR) beams and wide half-power beamwidths but also maintains a low-profile and compact structural characteristic. By adjusting the positions of the via directors, the radiation beam direction of the antenna element is effectively controlled. Subsequently, 4 × 4 antenna arrays for the xoz and yoz planes are designed and simulated to verify the CP beam scanning performance. The results demonstrate that the maximum CP beam scanning range can reach from -50° to +50°, with the AR maintained below 3 dB throughout the scanning range. At 29 GHz, the peak aperture efficiencies of the antenna arrays reach 73.8%, while maintaining a low profile of just 0.12λ0. Finally, the measurement results confirm the validity of the simulations. © 2002-2011 IEEE.

关键词： Millimeter waves

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Multivariable financial time series forecasting based on phase space reconstruction compensation

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Neural Computing and Applications 2025年第3期37卷 1389-1402页

作者： Li, Jincheng Zhou, Linli Li, Xuefei Wu, Di Xiong, Jianqiao Song, Liangtu School of Big Data and Statistics Anhui University Hefei230039 China Intelligent Agriculture Engineering Laboratory of Anhui Province Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China

Accurate financial time series forecasting is an important challenge in the financial field due to varying levels of interaction among multiple financial time series, complicating the extraction of valid information from these variables. This study introduces an effective and efficient multivariable financial time series forecasting model based on phase space reconstruction compensation: Phase Space Reconstruction Compensation Long Short-Term Memory (PSR-LSTM). The PSR-LSTM model leverages the long short-term memory (LSTM) network to analyze short-term data behaviors, reconstructs multiple long-term variables by phase space reconstruction method, and utilizes multivariable trend attention to capture trend information in correlated variables. This trend information is finally used to correct the LSTM network’s predictive result. Experimental results demonstrate that the PSR-LSTM outperforms existing multivariable forecasting models by effectively mitigating noise interference while achieving optimal forecasting performance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Phase space methods

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Area Coverage Reliability Evaluation for Collaborative Intelligence and Meta-Computing of Decentralized Industrial Internet of Things

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IEEE Internet of Things Journal 2025年第10期12卷 13734-13745页

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Liu, Shenghao Yin, Duan Gao, Hanjun Yang, Laurence T. Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Mobile Communications Group Terminal Co Beijing China CNNC Wuhan Nuclear Power Operation Technology Co Wuhan China

Industrial Internet of Things (IIoT) is evolving towards decentralization and autonomous operation. Nodes in decentralized IIoT collaboratively sense data and communicate to participate in meta-computing and provide diverse intelligent services. Area coverage reliability evaluates the information collaborative intelligence sensing and communication capabilities of nodes in decentralized IIoT for a target area. It serves as a crucial performance evaluation metric for the meta-computing and intelligent services of decentralized IIoT. Existing area coverage reliability evaluation algorithms do not consider the dynamic node states in decentralized IIoT, and overlook the capability of nodes to collaborate in meta-computing. To address these issues, this paper proposes a novel Confident Information Coverage (CIC)-based area coverage reliability metric (CACREL), which comprehensively considers node multistate, node collaboration, network coverage rate, and network connectivity. To effectively evaluate CACREL, a CIC-based coverage reliability evaluation algorithm (CACR) is proposed. Specifically, CACR transforms complex networks into grid networks and utilizes a Coverage Table (CT) to describe different coverage states of each grid, which reduces the complexity of the area coverage reliability evaluation. Additionally, CACR employs a reliable path algorithm to converge the CT of each grid to the sink node based on grid connectivity. Simulation results demonstrate that the proposed CACR can accurately evaluate the area coverage reliability of decentralized IIoT and enhance its service performance. © 2014 IEEE.

关键词： Decentralized finance

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