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Modeling, Estimation, and Applications of Phase Noise in Wireless Communications: A Survey

IEEE Communications Surveys and Tutorials 2025年第2期27卷 912-940页

作者： Chang, Zikai Xu, Yuntao Chen, Junjie Xie, Ning He, Yejun Li, Hongbin Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Phase noise is inevitably present in wireless communications, arising from both hardware impairments and imperfect channel estimation, and has substantial implications on the integrity and efficiency of communication systems. However, phase noise brings more to wireless systems than just negative effects. For instance, phase noise can be used as a physical layer feature to authenticate received signals and as a source for random number generation. There is a surprising lack of a comprehensive overview of the state-of-the-art estimation methods and applications of phase noise in wireless communications. In this paper, we conduct the first comprehensive survey on the modeling, estimation, and applications of phase noise in wireless communications. We review the state-of-the-art phase-noise models, phase-noise estimation, and mitigation techniques via joint transmit-receive design, as well as applications of phase noise in wireless communications, while highlighting contributions and critical issues of each reviewed work. Finally, we provide an expansive set of research directions, accompanying challenges, and potential solutions related to phase noise that could shape the future of phase noise research in wireless communications. © 1998-2012 IEEE.

关键词： Phase noise

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UDI-3DVT: A Novel network for 3-D Reconstruction via Uncertainty-Driven Sampling and Incremental Visual-Tactile Fusion

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IEEE Sensors Journal 2025年第6期25卷 10358-10369页

作者： Zhang, Chenyang Zhang, Shaohua Li, Yao Chen, Wenbai Gao, Guowei Wu, Peiliang Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Qinhuangdao066004 China Beijing Information Science and Technology University School of Automation Beijing100192 China

The fusion of visual and tactile senses allows robots to reconstruct and understand objects, aiding in downstream tasks such as object recognition and object grasping. However, most existing visual-tactile reconstruction methods typically overlook the role of visual point cloud processing in reconstruction, sampling points using generative-based methods rather than uncertainty-driven approaches. In addition, these methods only concatenate visual and tactile features, failing to fully leverage the complementary advantages of these two modalities, resulting in unsatisfactory reconstruction results. To address this issue, leveraging the open-source GelSight tactile sensor, this article proposes a novel method called UDI-3DVT, which reconstructs 3-D shapes by combining visual point clouds and multiple tactile images. Specifically, we explore the uncertainty of point clouds, performing additional sampling on high-uncertainty edge points, and propose an attention-based point cloud sampling method. Then, the framework leverages the efficiency of convolutional neural network (CNN) in image processing and the transformer's superiority in context modeling by using CNN for tactile features and transformer for visual features. A feature fusion module based on 1 × 1 convolution is designed to aggregate features from two branches. By gradually adding tactile features, we achieve incremental visual-tactile integration. Furthermore, since the contact-based perception of tactile sensors may cause slight deformations in objects, UDI-3DVT supports the reconstruction of both rigid and deformable objects. Extensive experiments demonstrate that our proposed framework outperforms current state-of-the-art (SOTA) methods, especially the Chamfer distance (CD) metric increased by 14.8%. © 2001-2012 IEEE.

关键词： Image reconstruction

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16QAM-OFDM VLC System Based on Frequency Domain Precompensation and DNN Post-Equalization

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IEEE Internet of Things Journal 2025年第9期12卷 12278-12286页

作者： Zhang, Hongyang Yang, Aiying Xu, Hang Ji, Shaoxi Feng, Lihui Zhang, Zhenrong Zhang, Minglun Beijing Institute of Technology Key Laboratory of Photonics Information Technology School of Optics and Photonics Beijing100081 China Guangxi University Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Nanning530004 China Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing100876 China

Visible light communication (VLC) technology utilizes LEDs as light sources, offering several benefits, such as the use of license-free spectrum, green, and safe. However, conventional LED devices face challenges due to their restricted bandwidth and nonlinearity under different current levels. These issues lead to signal distortion and intersymbol interference (ISI), which are particularly problematic in high-order modulation schemes. In this work, we propose a low-complexity scheme combining S21 precompensation and shallow hidden layer deep neural networks (DNNs)-based post-equalization in a VLC system. At the transmitter, S21 precompensation is used to suppress the influence of high-frequency power fading. The post-equalization at the receiver side uses the least square (LS)-DNN cascaded equalization algorithm, where LS performs linear equalization and DNN performs nonlinear equalization. Since the S21 precompensation and LS algorithm have completed partial equalization, it can provide a good initial state for DNN training, thus speeding up the convergence of DNN. To better capture the dependency between symbols, we preprocess the dislocation filling before the data is sent to the input layer of DNN. In the experiment, a VLC system with the 3-dB bandwidth of 9 MHz is used. With S21 precompensation, its 3-dB bandwidth is expanded to about 43 MHz. The two hidden layer DNN is used to carry nonlinear equalization. If an 80-Mb/s 16QAM OFDM signal is transmitted in a 3-m VLC system, the scheme proposed can expand the available peak-to-peak voltage Vpp range by 30% compared with precompensation LS algorithm and achieve transmission over 9 m under 7% hard-decision forward error correction (HD-FEC) BER threshold of 3.8e-3. The comparison with LSTM-DNN-based scheme shows that the proposed scheme can improve the performance of VLC system in terms of BER and EVM, while the training complexity is 2-order of magnitude lower. © 2014 IEEE.

关键词： Forward error correction

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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

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Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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A Novel Approach to Cloud Security: Publicly Verifiable Remote Signcryption Framework

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IEEE Internet of Things Journal 2025年第10期12卷 14828-14844页

作者： Ali, Mohammad Liu, Ximeng Amirkabir University of Technology Department of Mathematics and Computer Science Tehran Iran Fuzhou University Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou350108 China

In communication systems, ensuring both data integrity and confidentiality is crucial. Traditional methods, such as encryption and signature schemes, handle these aspects separately, leading to inefficiencies. Signcryption techniques, which address both requirements simultaneously, present a more efficient alternative. Nonetheless, they are unsuitable for cloud storage systems due to their reliance on original files for data auditing. Remote data integrity checking (RDIC) methods resolve this issue but compromise data confidentiality. To bridge the gap between signcryption and RDIC approaches, remote signcryption (RSC) has been proposed. However, existing RSC methods lack support for public auditing, rendering data auditing non-outsourcable. This paper introduces publicly verifiable remote signcryption (PVRSC) to address this shortcoming and presents the first concrete PVRSC scheme with public verifiability. We define the security model for PVRSC and prove its robustness based on the hardness of the Bilinear Diffie-Hellman (BDH) problem. Our implementation on real data shows that PVRSC significantly outperforms existing methods, achieving speeds up to 100 times faster and reducing communication costs by half compared to RDIC methods. For outsourced data up to 5MB, PVRSC maintains a communication cost below 8KB during data recovery, a substantial improvement over classical signcryption approaches, which exceed 5MB. © 2014 IEEE.

关键词： Cloud security

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DMF: A Deep Multimodal Fusion-Based network Traffic Classification Model

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computers, Materials & Continua 2025年第5期83卷 2267-2285页

作者： Xiangbin Wang Qingjun Yuan Weina Niu Qianwei Meng Yongjuan Wang Chunxiang Gu Henan Key Laboratory of Network Cryptography Technology Information Engineering UniversityZhengzhou450001China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.

关键词： Deep fusion intrusion detection multimodal learning network traffic classification

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LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization 31

LASS: A Novel and Economical Data Augmentation Framework Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Yanyue Li, Pengfei Lai, Yilong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761964

As more than 70% of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are hesitant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the reliance on a specific structure is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, large-scale data augmentation based on large language models faces an apparent disadvantage, the expensive costs. Therefore, in this paper, we propose LASS, a novel data augmentation framework based on both LArge and Small language models for debiaSing opinion summarization. Specifically, a small number of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on perplexity degree and sentiment classification. Experiments have proved that LASS can effectively alleviate emotional bias, similar to using only large models, but in a more economical way. © 2025 Association for Computational Linguistics.

关键词： Data assimilation

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Multiple Edge Data Integrity Verification with Multi-Vendors and Multi-Servers in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4668-4683页

作者： Islam, Md Rashedul Xiang, Yong Uddin, Md Palash Zhao, Yao Kua, Jonathan Gao, Longxiang The School of Information Technology Deakin University GeelongVIC3220 Australia The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Ensuring Edge Data Integrity (EDI) is imperative in providing reliable and low-latency services in mobile edge computing. Existing EDI schemes typically address single-vendor (App Vendor, AV) single-server (Edge Server, ES), single-vendor multi-server, and multi-vendor multi-server scenarios, which consider a single data replica cached by an ES from the AVs. However, the most practical scenario of Multi-Vendors and Multi-Servers with Multiple Data (MVMS-MD) cached by an ES from different AVs remains unexplored. Current solutions struggle when applied to this scenario due to increased computation and communication costs in the verification process across all ESs using the classical challenge-response per-data multi-round strategy. To tackle this issue, we propose a Multiple EDI-Verification (MEDI-V) approach in this paper. In particular, our MEDI-V utilizes an adaptive Merkle Hash Tree (ad-MHT) to efficiently generate a tree of multiple data replicas within each AV. Next, the dynamic mechanism computes minimal verification information using ad-MHT to create a challenge for individual ESs to produce EDI proofs. The ES then leverages its ad-MHT and the ES’s proof to send the reconstructed ad-MHT root to the AV for verification. Theoretical insights into MEDI-V’s correctness, efficiency, security, and comprehensive evaluations demonstrate its superiority in addressing MEDI issues in the MVMS-MD scenario. © 2002-2012 IEEE.

关键词： Data integrity

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A Throughput and Priority Optimization Strategy for High Density Healthcare IoT

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Intelligent and Converged networks 2025年第1期6卷 41-52页

作者： Su, Zhenlang Ren, Junyu Huang, Yeheng Liao, Yang Qin, Tuanfa School of Computer Electronic and Information Guangxi University Nanning530004 China School of Physics and Electronic Information Guangxi Minzu University Nanning530006 China School of Computer Electronic and Information Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China

In the field of wireless body area networks (WBANs), for solving the complex interference problem of inter-WBANs, a density-based adaptive optimization strategy (DAOS) is proposed in this paper. Firstly, the complex interference problem among WBANs is converted into a distance-based graph coloring model, then time division multiple access and a two-level split clustering methods are adopted to allocate initial time slots for nodes. Secondly, the particle swarm optimization algorithm is used to optimize the time slot of each node for maximizing the throughput. We simulate the scenario on MATLAB simulator. Experimental results show that compared with the traditional scheme in high-density healthcare Internet of Things (IoT) scenarios, DAOS has obvious advantages compared with three comparison strategies of faster convergence rate of 48.94%, 60.76%, and 96.82%, and higher throughput of 5.60%, 8.08%, and 8.05% in traffic priorities 7 to 4. © 2020 Tsinghua University Press.

关键词： Particle swarm optimization (PSO)

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JDC-GCN: joint diversity and consistency graph convolutional network

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Neural Computing and Applications 2025年 1-17页

作者： Lin, Renjie Yao, Jie Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

In real-world scenarios, multi-view data comprises heterogeneous features, with each feature corresponding to a specific view. The objective of multi-view semi-supervised classification is to enhance classification performance by leveraging the inherent complementary and consistent information present within diverse views. Nevertheless, many existing frameworks primarily focus on assigning suitable weights to different views while neglecting the importance of consistent information. In this paper, a multi-view semi-supervised classification framework called joint diversity and consistency graph convolutional network (JDC-GCN) is proposed. Firstly, the structure of graph convolutional network is introduced to the multi-view semi-supervised classification, capable of propagating the label information over the topological structure of multi-view data. Secondly, the proposed JDC-GCN captures the complementary and consistent information from multiple views through two indispensable sub-modules, Diversity-GCN and Consistency-GCN, respectively. Finally, the attention mechanism is leveraged to dynamically adjust the weights of various views, allowing us to measure the significance of heterogeneous features and the consistent graph without introducing additional parameters. Comprehensive experiments on eight multi-view datasets are conducted to validate the effectiveness of the JDC-GCN algorithm. The results show that the proposed method exhibits superior classification performance compared to other state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self-supervised learning

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